COVID19 Outbreak in Chile - Live!

This approximation is only descriptive. Epidemiological models are only a personal experience, probably full of errors, misspecifications, and missinterpretations, with poor experimental conditions and without controls, more close to models based on mere speculation than serious research. By now, “cases” will refer only to confirmed cases, notwithstanding which that there may be other cases that are in quarantine or have not attended health care centers to be diagnosed. That is why these results could be biased in terms of the sample due to selection bias.

Worldwide Dataset of Number of Cases By Each Country

We downloaded coronavirus available in this link, obtained from the Johns Hopkins University each day at 1AM (GMT-03). The data from Chile is the obtained in 10 junio, 2020 at 1 PM.

First we defined the datasets in order to identify Latin American countries that have been affected by Coronavirus into an objective time progression (determined by dates).

coronavirus_plot_tiempo_obj<- coronavirus %>%
  dplyr::filter(country %in% c('Argentina','Bolivia','Brazil', 'Chile',
                                      'Colombia','Costa Rica','Cuba', 'Dominican Republic',
                                      'Ecuador', 'Honduras','Mexico', 'Panama',
                                      'Paraguay','Peru', 'Uruguay',
                                      'Venezuela')) %>%
  dplyr::filter(type=="confirmed") %>%
  dplyr::arrange(country,date) %>%
  dplyr::group_by(country) %>%
  dplyr::mutate(acum_new = cumsum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(chile=ifelse(country=="Chile",1,0)) %>%
  dplyr::mutate(date_posixct=as.POSIXct(as.character(date))) 


coronavirus_plot_tiempo_por_paises_prim_caso <- coronavirus_plot_tiempo_obj %>%
  dplyr::filter(acum_new>0) %>%
  dplyr::group_by(country) %>% 
  dplyr::mutate(days_passed=row_number()) %>%
  dplyr::ungroup()

library(magrittr)
library(ggplot2)
coronavirus_plot_tiempo_obj_cl<-coronavirus_plot_tiempo_obj %>%
  dplyr::filter(country=="Chile")

col_vector<-c("darksalmon","gold","orange4","mediumorchid2", "darkred","red","pink1","lightcyan2")
#sample(color, 16)
#pie(rep(1,16), col=sample(col_vector, 16))
manualcolors<-c('black','forestgreen', 'red2', 'orange', 'cornflowerblue', 
                'magenta', 'darkolivegreen4',  
                'indianred1', 'tan4', 'darkblue', 
                'mediumorchid1','firebrick4',  'yellowgreen', 'gray20', 'tan3',
                "tan1",'darkgray', 'wheat4', '#DDAD4B', 'chartreuse', 'seagreen1',
                'moccasin', 'mediumvioletred', 'seagreen','cadetblue1',
                "darkolivegreen1" ,"tan2" ,   "tomato3" , "#7CE3D8","gainsboro")
library(ochRe)
library(randomcoloR)
n <- 16
palette <- distinctColorPalette(n)

pal <- colorRampPalette(ochre_palettes[["emu_woman_paired"]])

#pie(rep(1,30), col=sample(manualcolors, 30))

coronavirus_plot_tiempo_obj %>%
  dplyr::filter(country!="Chile") %>%
  ggplot() +#, ) +
  geom_line(aes(x = date_posixct, y = acum_new, color=factor(country)),size=1) +
  scale_x_datetime(breaks=scales::date_breaks("1 week"),labels = scales::date_format("%d/%m"),
                   limits = as.POSIXct(c('2020-02-20 09:00:00',as.character(Sys.time())))) +
  geom_line(aes(x = date_posixct, y = acum_new,color=" Chile"), data=coronavirus_plot_tiempo_obj_cl, size=2.5) +
  ylim(min(coronavirus_plot_tiempo_obj$acum_new, na.rm=TRUE),max(coronavirus_plot_tiempo_obj$acum_new, na.rm=TRUE)) +
  scale_colour_ochre(name= "Países",
                     palette="emu_woman_paired",
                     labels=c('Chile', 'Argentina','Bolivia','Brazil',
                              'Colombia','Costa Rica','Cuba', 'Dom. Republic',
                              'Ecuador', 'Honduras','Mexico', 'Panama',
                              'Paraguay','Peru', 'Uruguay','Venezuela', discrete=FALSE)) +
  guides(color=guide_legend(ncol=6,name = "Countries",size=6))+  labs(color="Countries")+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.POSIXct('2020-03-03 09:00:00'))+ 
  labs(title = paste0("Figure 1a. Linear Trends of Aggregated Data of Chile v/s Other States of \n Confirmed Cases COVID-19 in Latin America"),
       y = "No. of Confirmed Cases", x = "Week/Year",
       caption="Vertical Line: First case in Chile; Dates since 02-20 until last day of retrieval")+
  theme(plot.title = element_text(color="darkblue"))+
  theme(legend.title = element_text(size= 10.5, colour = "black"), legend.position='bottom', 
        legend.text=element_text(size=7))+
  theme(plot.caption = element_text(hjust = 0, face = "italic"))

coronavirus_plot_tiempo_obj$tooltip <- paste0(substr(coronavirus_plot_tiempo_obj$country,1,3),"=", round(coronavirus_plot_tiempo_obj$acum_new,0) )

rank_la_countries<-coronavirus_plot_tiempo_por_paises_prim_caso %>%
  dplyr::group_by(country) %>% 
  dplyr::filter(date==max(date)) %>%
  dplyr::select(country,acum_new) %>% 
  dplyr::arrange(desc(acum_new))%>%
  dplyr::ungroup()%>%
  dplyr::mutate(rn=row_number())
fila_chile_la_rank<-as.numeric(rank_la_countries %>% dplyr::filter(country=="Chile")%>% 
                                 dplyr::select(rn))

rank<-rank_la_countries %>%
  dplyr::slice(1:fila_chile_la_rank-1)


words_la <-data.frame(rank_la_countries[fila_chile_la_rank-1,"country"])
if (fila_chile_la_rank>3) {
  for (i in seq(from=fila_chile_la_rank-2,to=2,by=1)){
    words_la <-paste0(words_la,", ",rank_la_countries[i,"country"])
  }
  words_la <-paste0(words_la," & ",rank_la_countries[1,"country"])
} else {
  words_la <-paste0(rank_la_countries[2,"country"]," & ",rank_la_countries[1,"country"])
}

library(ggiraph)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- coronavirus_plot_tiempo_obj %>%
  dplyr::mutate(country=dplyr::if_else(country=="Dominican Republic","Dom.Rep",country)) %>%
  dplyr::mutate(country=dplyr::if_else(country=="Costa Rica","C.Rica",country)) %>%
  ggplot(aes( hover_css = "fill:none;")) +#, ) +
  scale_x_datetime(breaks=scales::date_breaks("1 week"),labels = scales::date_format("%d/%m"),
                   limits = as.POSIXct(c('2020-02-20 09:00:00',as.character(Sys.time()))))+
  geom_line(aes(x = date_posixct, y = acum_new, color=factor(country)),size=1) +
  ggiraph::geom_point_interactive(aes(x = date_posixct,y = acum_new, color=country, tooltip=tooltip),size = .75) +
  scale_colour_ochre(name= "Países",
                     palette="emu_woman_paired",
                     labels=c('Chile', 'Argentina','Bolivia','Brazil',
                              'Colombia','Costa Rica','Cuba', 'Dom. Republic',
                              'Ecuador', 'Honduras','Mexico', 'Panama',
                              'Paraguay','Peru', 'Uruguay','Venezuela', discrete=FALSE)) +
  guides(color=guide_legend(ncol=6,name = "Countries",size=6))+  labs(color="Countries")+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.POSIXct('2020-03-03 09:00:00'))+ 
  labs(title = "Figure 1b. Linear Trends of Aggregated Data of Chile v/s\n Other States of Confirmed Cases COVID-19 in Latin Am.", subtitle= "Interactive Plot",
       y = "No. of Confirmed Cases", x = "Week/Year",
       caption="Vertical Line: First case in Chile; Dates since 02-20 until last day of retrieval")+
  theme(plot.title = element_text(color="darkblue"))+
  theme(legend.title = element_text(size= 10.5, colour = "black"), legend.position='bottom', 
        legend.text=element_text(size=7))+
  theme(plot.caption = element_text(hjust = -0.5, face = "italic"))
#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

As seen in Figure 1, Chile is in the 3rd place in terms of cases detected. Peru & Brazil have more confirmed cases. This could be explained by population, date of onset of diagnoses, or the quantity of tests applied.

#library(rCharts)          # To make the convertion data-frame / xts format
#coronavirus_plot_tiempo_obj$date <- as.numeric(as.POSIXct(paste0(coronavirus_plot_tiempo_obj$date, "-01-01")))
coronavirus_plot_tiempo_obj_conv <- transform(coronavirus_plot_tiempo_obj, date = as.numeric(coronavirus_plot_tiempo_obj$date_posixct)*1000)

line.rcharts <- rCharts::hPlot(acum_new ~date, group="country", data=coronavirus_plot_tiempo_obj_conv, type="line",
                      title="Figure 1b. Linear Trends of Aggregated Data of Chile v/s Other States of Confirmed Cases COVID-19 in Latin America", subtitle= "Interactive Plot")
#line.rcharts$show('iframesrc', cdn=TRUE)
# line.rcharts$xAxis(
#    tickFormat =   "#!
#     function(d) {return d3.time.format('%Y-%m-%d')(new Date(d*1000*3600*24));}
#   !#",
#    rotateLabels = -90
# )

x1 <- xPlot(acum_new ~ date_posixct, group = "country", data = coronavirus_plot_tiempo_obj_conv, type = "line-dotted")
x1$print("chart4")

x1$xAxis(type = 'datetime', labels = list(
  format = '{value:%Y-%m-%d}'), title = list(text = "Days since the first diagnosed"))
x1$yAxis(title = list(text = "Days of Exposure"))
#line.rcharts$set(width = 1000, height = 600)
#line.rcharts$set(slider = TRUE)
#line.rcharts$show('inline', include_assets = TRUE, cdn = TRUE)
#line.rcharts$show('iframesrc', cdn = TRUE)
#line.rcharts$print("chart4")

Second, we transformed the accumulative cases into a logaritmic scale, comparing Chile versus other latin american countries in objective time periods. As can be seen, Chile seems to have a greater logaritmic progression. Still, objective dates do not permit comparisons between countries, since there is a difference between the date of the first cases in each one. Additionally, the comparison is made on aggregated means, hence, some countries could be over Chile in terms of confirmed cases.

combo_plot<-  ggplot(coronavirus_plot_tiempo_obj, aes(x = date_posixct, y = log(acum_new), group = chile, color=as.factor(chile))) +
  stat_smooth(aes(group = chile),method = "lm", se = T) + 
  stat_summary(geom = "point", fun.y = mean, shape = 15, size = 2.5) + 
  #  stat_quantile(quantiles = c(0.25,  0.75), color= "black", linetype="dashed") +
  scale_x_datetime(breaks=scales::date_breaks("1 week"),labels = scales::date_format("%d/%m"),
                   limits = as.POSIXct(c('2020-02-20 09:00:00',as.character(Sys.time())))) +
  sjPlot::theme_sjplot2() +
  #  theme(legend.position = "none") +
  labs(title = paste0("Figure 2. Linear Trends, objective time of progression in cum. cases"),
       subtitle = "(Linear regression in shaded area. Means in squares)",
       y = "Cumulative Cases (Log. Scale)", x = "Weeks/Year",
       caption="Note. Vertical Line: First case in Chile; Dates since 02-20; Other countries: Argentina,Bolivia,Brazil,Colombia,Costa Rica,Cuba,\nDom. Republic,Ecuador, Honduras,Mexico, Panama, Paraguay,Peru, Uruguay & Venezuela")+ 
  geom_vline(xintercept = as.POSIXct('2020-03-03 09:00:00'))+ 
  scale_colour_discrete(name  ="País",
                        labels=c("Other Countries", "Chile"))  +
  theme(plot.title = element_text(color="darkblue"))+
  theme(legend.text = element_text(size = 8, face = "italic"))+
  theme(legend.title = element_text(size = 8, face = "italic"))+
  theme(plot.caption = element_text(hjust = 0, face= "italic", size=8))
print(combo_plot)

#ggsave(paste0("G:/Mi unidad/covid19/_Primer gráfico simple.png"), combo_plot, dpi = 300)

Third, we obtained the number of cases, but grouping the other countries of latin america, vs. cases in Chile, but also incluiding Interquartile Range, in order to catch the heterogeneity. Also we included a linear regression to show the overall linear trend. As can be seen in Figure 3, when we normalized dates into the difference between the date of the diagnosis of the first case, Chile seems to follow nearly the same trend than the other countries.

segundo_grafico<- 
  ggplot(data = coronavirus_plot_tiempo_por_paises_prim_caso, aes(x = days_passed)) +
  stat_smooth(method = "lm", se = T, aes(y = acum_new, fill = as.factor(chile),color = as.factor(chile)), linetype="dashed") +
  stat_summary(geom = "line", fun.y = median, size = 1, aes(y = acum_new, color=as.factor(chile))) +
  stat_summary(geom="ribbon", aes(y = acum_new,color="green" ), fun.ymin = function(x) quantile(x, 0.25), fun.ymax = function(x) quantile(x, 0.75), alpha=.45)+ 
  #geom_line(aes(y = acum_new), color = "blue") +
  #geom_line(aes(y = B19301_001E), color = "red") +
  labs(title = paste0("Figure 3. Linear Trends of Aggregated Data of Chile v/s\nOther States of Confirmed Cases COVID-19 in Latin America"),
       y = "No. of Confirmed Cases", x = "Days Since the First Diagnosed",
       caption="Note: Linear Regression in Dashed Lines, SE's (Std. Errors) in gray;O Other countries: Argentina,Bolivia,Brazil,\nColombia,Costa Rica,Cuba,Dom. Republic,Ecuador, Honduras,Mexico, Panama,Paraguay,Peru, Uruguay & Venezuela")+ 
  #scale_x_continuous("Quarters & Years",breaks=seq(0,14,7)) +
  theme(axis.text.x = element_text(size=13.5,vjust = 0.5), axis.text.y = element_text(size=14),
        axis.title.y = element_text(size = 14)) +
  theme(legend.text = element_text(size = 12, face = "italic")) +
  scale_colour_discrete(name  ="Legend", 
                        labels=c("Other Countries", "Chile", "25th & 75th percentiles\n  Orher Countries")) +
  #Medians in continuous lines, 25th and 75th \npercentiles in Shaded Areas
  #scale_fill_manual(values=c("red","blue"))+
  #ylim(c(0,100))+
  #  scale_fill_manual(values=c("0","1"),labels=c("Otros Países", "Chile")) +
  sjPlot::theme_sjplot2() +
  theme(plot.title = element_text(color="darkblue"))+
  theme(legend.title = element_text(size= 10.5, colour = "black"), legend.position='bottom')+
  theme(plot.caption = element_text(hjust = 0, face = "italic",size=8.5))+
  guides( fill = F)

print(segundo_grafico)

#ggsave("G:/Mi unidad/covid19/_Segundo gráfico.png", segundo_grafico,dpi = 300)

In Figure 3 we can see that if we calculated the trends by starting from the first day of the first confirmed case, our country starts showed a more pronounced trend compared to the rest of latin american countries starting from the 12th day of the first reported case, but since the 35th day, the no. of confirmed cases in Chile started being not different than the other latinoamerican countries.

We obtained data from the world bank regarding Population(total), Current health expenditure (% of GDP),Hospital beds (per 1,000 people), and GDP (current US$) from the last available year (more info on this link). Additionally, we added the diabetes prevalence,the median age, the percentage of population 65+ years old, and the percentage of male and female smokers. This and other reports are compiled in owid/covid-19-data GitHub repository through a rate per habitants (Total tests for COVID-19 per 1,000 people) (more info, available in this link).

#"https://raw.githubusercontent.com/demm94/Covid19-CL/master/15-03-2020.csv"
##MODO JSON
library(dplyr)
library(readxl)

url <- "http://api.worldbank.org/v2/en/indicator/SP.POP.TOTL?downloadformat=excel"
destfile <- "SP_POP.xls"
curl::curl_download(url, destfile)
SP_POP <- read_excel(destfile, skip=2)

SP_POP2<- reshape2::melt(SP_POP[c(1,5:63)],  na.rm = T,id="Country Name", measure.vars = as.character(1960:2018))%>%
  dplyr::group_by(`Country Name`) %>%
  slice(which.max(variable)) %>%
  dplyr::ungroup()%>%
  dplyr::mutate(`Country Name`=ifelse(`Country Name`=="Venezuela, RB","Venezuela",`Country Name`))

url2 <- "http://api.worldbank.org/v2/en/indicator/SH.XPD.CHEX.GD.ZS?downloadformat=excel"
destfile2 <- "SH_XPD_CHEX_GD.xls"
curl::curl_download(url2, destfile2)
SH_XPD_CHEX_GD <- read_excel(destfile2, skip=2)

SH_XPD_CHEX_GD2<-reshape2::melt(SH_XPD_CHEX_GD[c(1,5:63)],  na.rm = T,id="Country Name", measure.vars = as.character(1960:2018))%>%
  dplyr::group_by(`Country Name`) %>%
  slice(which.max(variable)) %>%
  dplyr::ungroup()%>%
  dplyr::mutate(`Country Name`=ifelse(`Country Name`=="Venezuela, RB","Venezuela",`Country Name`))

url3 <- "http://api.worldbank.org/v2/en/indicator/SH.MED.BEDS.ZS?downloadformat=excel"
destfile3 <- "SH.MED.BEDS.ZS.xls"
curl::curl_download(url3, destfile3)
SH_MED_BEDS_ZS <- read_excel(destfile3, skip=2)
SH_MED_BEDS_ZS<-reshape2::melt(SH_MED_BEDS_ZS[c(1,5:64)],  na.rm = T,id="Country Name", measure.vars = as.character(1960:2019)) %>%
  dplyr::group_by(`Country Name`) %>% 
  dplyr::slice(which.max(variable)) %>%
  dplyr::ungroup()%>%
  dplyr::mutate(`Country Name`=ifelse(`Country Name`=="Venezuela, RB","Venezuela",`Country Name`))

url4 <- "http://api.worldbank.org/v2/en/indicator/NY.GDP.MKTP.CD?downloadformat=excel"
destfile4 <- "NY.GDP.MKTP.CD.xls"
curl::curl_download(url4, destfile4)
NY.GDP.MKTP.CD <- read_excel(destfile4, skip=2)
NY.GDP.MKTP.CD<-reshape2::melt(NY.GDP.MKTP.CD[c(1,5:63)],  na.rm = T,id="Country Name", measure.vars = as.character(1960:2018)) %>%
  dplyr::group_by(`Country Name`) %>% 
  dplyr::slice(which.max(variable)) %>%
  dplyr::ungroup()%>%
  dplyr::mutate(`Country Name`=ifelse(`Country Name`=="Venezuela, RB","Venezuela",`Country Name`))

bd_comp_countries<- SP_POP2 %>%
  dplyr::left_join(SH_XPD_CHEX_GD2, by="Country Name",suffix = c("_pop", "_health_exp")) %>%
  dplyr::left_join(SH_MED_BEDS_ZS, by="Country Name",suffix = c("", "_beds")) %>%
  dplyr::left_join(NY.GDP.MKTP.CD, by="Country Name",suffix = c("", "_gdp")) %>%
  dplyr::mutate(chile=ifelse(`Country Name`=="Chile",1,0)) %>%
  dplyr::mutate(`Country Name`= recode(`Country Name`, "Iran" = "Iran, Islamic Rep.",
                                       "Russia" = "Russian Federation",
                                       "South Korea"="Korea, Rep.",
                                       "Slovakia" = "Slovak Republic"))
  #  dplyr::mutate(value_pop=round(value_pop/100000,0),
  #                value_health_exp=round(value_health_exp,0),
  #                value_beds=round(value,2),
  #                value_gdp=round(as.numeric(value_gdp/100000000)))%>%

#https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/testing/covid-testing-all-observations.csv
covid_testing_all_observations <- readr::read_csv("https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/testing/covid-testing-all-observations.csv", 
                                                  col_types = cols(`Cumulative total` = col_number(), 
                                                                   `Cumulative total per thousand` = col_number(), 
                                                                   `Daily change in cumulative total` = col_number(), 
                                                                   `Daily change in cumulative total per thousand` = col_number(), 
                                                                   Date = col_datetime(format = "%Y-%m-%d")))%>%
  dplyr::mutate(date_date=as.Date(as.character(Date))) %>%
  separate("Entity", c("Country.Name", "measure"), sep= " - ",extra = "merge")


#Para ver chile 
#bd_comp_countries %>% dplyr::filter(`Country Name`=="Chile")

#JOIN DATASET W POP
coronavirus_plot_tiempo_por_paises_prim_caso %>%
  dplyr::left_join(bd_comp_countries,by=c("country"="Country Name")) %>%
  dplyr::left_join(covid_testing_all_observations, by=c("country"="Country.Name", "date"="date_date"))%>%
  dplyr::mutate(acum_new_pop=(acum_new/value_pop)*100000) %>% 
  assign("coronavirus_plot_tiempo_por_paises_prim_caso_pop_c",., envir = .GlobalEnv)

#casos / poblacion *100000 = casos por 100 mil hab
#fit1=lm(acum_new_pop~days_passed+`2016` + (1|as.factor(chile)),data=coronavirus_plot_tiempo_por_paises_prim_caso_pop, na.rm=T)

coronavirus_plot_tiempo_por_paises_prim_caso_pop_c %>%
  dplyr::rename("variable_beds"="variable") %>%
  dplyr::rename("value_beds"="value") %>%
  dplyr::rename("chile"="chile.x") %>%
  dplyr::group_by(country)%>%
  dplyr::mutate(rn=row_number())%>%
  dplyr::filter(rn==max(rn)) %>% 
  dplyr::ungroup()%>%
  dplyr::select(-rn, -chile.y,-type, -`Daily change in cumulative total`,-`Daily change in cumulative total per thousand`,-`Source label`) %>%
  dplyr::rename("year_pop"="variable_pop") %>%
  dplyr::rename("year_health_exp"="variable_health_exp") %>%
  dplyr::rename("year_beds"="variable_beds") %>%
  dplyr::rename("year_gdp"="variable_gdp") %>%
  assign("coronavirus_plot_tiempo_por_paises_prim_caso_pop_1_exp",., envir = .GlobalEnv)

coronavirus_plot_tiempo_por_paises_prim_caso_pop_1_exp%>% 
  knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
               caption = paste0("Table 1. Comparison of Latinoamerican countries"),
               align =rep('c', 101)) %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 7) %>%
  kableExtra::add_footnote( c("Note. acum_new= Cumulative confirmed cases",
                              "cases= Daily confirmed cases",
                              "date_posixct= Date of the last available date of cases confirmed",
                              "days_passed= Days passed since the first confirmed case", 
                              "year_= Year in which an indicator was reported",
                              "value_= Corresponding value for each indicator",
                              "measure= Source of test reported",
                              "Date= date of the last report of tests",
                              "Source URL= webpage of datasets or reports",
                              "Notes= Additional observations to each report",
                              "Cumulative total= Of tests reported",
                              "Cumulative total per thousand" = "Rate of Cum. Tot. per 1,000 people"), 
                            notation = "none") %>%
  kableExtra::scroll_box(width = "100%", height = "375px")

Table 1. Comparison of Latinoamerican countries

date	province	country	lat	long	cases	acum_new	chile	date_posixct	days_passed	year_pop	value_pop	year_health_exp	value_health_exp	year_beds	value_beds	year_gdp	value_gdp	measure	ISO code	Date	Source URL	Notes	Cumulative total	Cumulative total per thousand	7-day smoothed daily change	7-day smoothed daily change per thousand	acum_new_pop
2020-06-10		Argentina	-38.4161	-63.6167	1,226	25,987	0	2020-06-10	100	2018	44,494,502	2017	9.124315	2014	5.0	2018	5.198715e+11	NA	NA	NA	NA	NA	NA	NA	NA	NA	58.404969
2020-06-10		Bolivia	-16.2902	-63.5887	637	15,281	0	2020-06-10	92	2018	11,353,142	2017	6.443161	2014	1.1	2018	4.028765e+10	NA	NA	NA	NA	NA	NA	NA	NA	NA	134.597101
2020-06-10		Brazil	-14.2350	-51.9253	32,913	772,416	0	2020-06-10	106	2018	209,469,333	2017	9.467477	2014	2.2	2018	1.885483e+12	NA	NA	NA	NA	NA	NA	NA	NA	NA	368.748966
2020-06-10		Chile	-35.6751	-71.5430	5,697	148,456	1	2020-06-10	100	2018	18,729,160	2017	8.983510	2013	2.2	2018	2.982311e+11	NA	NA	NA	NA	NA	NA	NA	NA	NA	792.646333
2020-06-10		Colombia	4.5709	-74.2973	1,359	42,206	0	2020-06-10	97	2018	49,648,685	2017	7.226281	2014	1.5	2018	3.310470e+11	NA	NA	NA	NA	NA	NA	NA	NA	NA	85.009301
2020-06-10		Costa Rica	9.7489	-83.7534	86	1,461	0	2020-06-10	97	2018	4,999,441	2017	7.328796	2015	1.2	2018	6.013011e+10	NA	NA	NA	NA	NA	NA	NA	NA	NA	29.223267
2020-06-10		Cuba	22.0000	-80.0000	6	2,211	0	2020-06-10	91	2018	11,338,138	2017	11.711327	2014	5.2	2018	1.000230e+11	NA	NA	NA	NA	NA	NA	NA	NA	NA	19.500556
2020-06-10		Dominican Republic	18.7357	-70.1627	393	20,808	0	2020-06-10	102	2018	10,627,165	2017	6.136395	2014	1.6	2018	8.555539e+10	NA	NA	NA	NA	NA	NA	NA	NA	NA	195.800103
2020-06-10		Ecuador	-1.8312	-78.1834	523	44,440	0	2020-06-10	102	2018	17,084,357	2017	8.257429	2013	1.5	2018	1.083981e+11	NA	NA	NA	NA	NA	NA	NA	NA	NA	260.120998
2020-06-10		Honduras	15.2000	-86.2419	425	7,360	0	2020-06-10	92	2018	9,587,522	2017	7.858425	2014	0.7	2018	2.402419e+10	NA	NA	NA	NA	NA	NA	NA	NA	NA	76.766447
2020-06-10		Mexico	23.6345	-102.5528	4,883	129,184	0	2020-06-10	104	2018	126,190,788	2017	5.516541	2015	1.5	2018	1.220699e+12	NA	NA	NA	NA	NA	NA	NA	NA	NA	102.371973
2020-06-10		Panama	8.5380	-80.7821	656	17,889	0	2020-06-10	93	2018	4,176,873	2017	7.319491	2013	2.3	2018	6.505510e+10	NA	NA	NA	NA	NA	NA	NA	NA	NA	428.286903
2020-06-10		Paraguay	-23.4425	-58.4438	15	1,202	0	2020-06-10	95	2018	6,956,071	2017	6.654569	2011	1.3	2018	4.049695e+10	NA	NA	NA	NA	NA	NA	NA	NA	NA	17.279870
2020-06-10		Peru	-9.1900	-75.0152	5,087	208,823	0	2020-06-10	97	2018	31,989,256	2017	4.995151	2014	1.6	2018	2.220450e+11	NA	NA	NA	NA	NA	NA	NA	NA	NA	652.791050
2020-06-10		Uruguay	-32.5228	-55.7658	1	847	0	2020-06-10	90	2018	3,449,299	2017	9.296212	2014	2.8	2018	5.959689e+10	NA	NA	NA	NA	NA	NA	NA	NA	NA	24.555714
2020-06-10		Venezuela	6.4238	-66.5897	106	2,738	0	2020-06-10	89	2018	28,870,195	2017	1.181210	2014	0.8	2014	4.823593e+11	NA	NA	NA	NA	NA	NA	NA	NA	NA	9.483829
Note. acum_new= Cumulative confirmed cases
cases= Daily confirmed cases
date_posixct= Date of the last available date of cases confirmed
days_passed= Days passed since the first confirmed case
year_= Year in which an indicator was reported
value_= Corresponding value for each indicator
measure= Source of test reported
Date= date of the last report of tests
Source URL= webpage of datasets or reports
Notes= Additional observations to each report
Cumulative total= Of tests reported
Rate of Cum. Tot. per 1,000 people

coronavirus_world<- coronavirus %>%
  dplyr::filter(type=="confirmed") %>%
  dplyr::arrange(country,date) %>%
  dplyr::group_by(country) %>%
  mutate(acum_new = cumsum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(chile=ifelse(country=="Chile",1,0)) %>%
  dplyr::mutate(date_posixct=as.POSIXct(as.character(date)))  %>%
  dplyr::filter(acum_new>0) %>%
  dplyr::group_by(country) %>% 
  dplyr::mutate(days_passed=row_number()) %>%
  dplyr::ungroup()

library(readxl)
library("rjson")
url <- "https://github.com/owid/covid-19-data/blob/master/public/data/owid-covid-data.xlsx?raw=true"
destfile <- "owid_covid_data.xlsx"
curl::curl_download(url, destfile)
owid_covid_data <- read_excel(destfile)
owid_covid_data <-owid_covid_data%>% 
  dplyr::mutate(date=as.Date(date,tryFormats = c("%Y-%m-%d")))

covid_testing_all_observations%>%
  dplyr::left_join(bd_comp_countries,by=c("Country.Name"="Country Name")) %>%
  #dplyr::left_join(covid_testing_all_observations, by=c(="Country.Name"))%>%
  dplyr::rename("country"="Country.Name")%>%
  dplyr::mutate(acum_new_pop=(`Cumulative total`/value_pop)*100000) %>%
  dplyr::left_join(owid_covid_data,by=c("ISO code"="iso_code","date_date"="date")) %>%
  dplyr::group_by(country) %>% 
  dplyr::mutate(days_passed=row_number()) %>%
  dplyr::ungroup()%>%
  assign("coronavirus_plot_tiempo_por_paises_world_ult_caso",., envir = .GlobalEnv)

coronavirus_plot_tiempo_por_paises_world_ult_caso %>%
  dplyr::rename("variable_beds"="variable") %>%
  dplyr::rename("value_beds"="value") %>%
  #dplyr::rename("chile"="chile.x") %>%
  dplyr::group_by(country)%>%
  dplyr::mutate(rn=row_number())%>%
  dplyr::filter(rn==max(rn)) %>% 
  dplyr::ungroup()%>%
  dplyr::select(-rn, -`Daily change in cumulative total`,-`Daily change in cumulative total per thousand`,-`Source label`) %>%
  dplyr::rename("year_pop"="variable_pop") %>%
  dplyr::rename("year_health_exp"="variable_health_exp") %>%
  dplyr::rename("year_beds"="variable_beds") %>%
  dplyr::rename("year_gdp"="variable_gdp") %>%
  assign("coronavirus_plot_tiempo_world_log",., envir = .GlobalEnv)

norm_coronavirus_plot_tiempo_world_log<-rockchalk::gmc(coronavirus_plot_tiempo_world_log, c("value_pop", "value_health_exp", "value_beds", "value_gdp", "acum_new_pop"), "chile", FUN = mean, suffix = c("_mn", "_dev"),fulldataframe = TRUE)

#glm.fit<-glm(chile~ value_pop + value_health_exp + value_beds + value_gdp, data=coronavirus_plot_tiempo_world_log, family = binomial)

#glm.probs <- predict(std.glm.fit,type = "response")
#cbind(na.omit(coronavirus_plot_tiempo_world_log[,c(1,11,13,15,17)]),glm.probs) %>%
#  dplyr::arrange(desc(glm.probs))

#library(caret)

#x <- coronavirus_plot_tiempo_world_log[,c(11,13,15,17)]
#y <- coronavirus_plot_tiempo_world_log[,7]
#scales <- list(x=list(relation="free"), y=list(relation="free"))
#caret::featurePlot(x=x, y=y, plot="density", scales=scales)

Table 1 let us see differences fixed by each country in terms of resources to confront this pandemic.

#POPULATION DATASET
library(readxl)
require(ggiraph)
require(ggiraphExtra)
require(gridExtra)

tt <- ttheme_minimal(colhead=list(fg_params = list(parse=TRUE)),base_size = 5)
tbl <- tableGrob(coronavirus_plot_tiempo_por_paises_prim_caso_pop_1_exp, rows=NULL, theme=tt)

coronavirus_plot_tiempo_por_paises_prim_caso %>%
  dplyr::left_join(SP_POP2, by=c("country"="Country Name"),suffix = c("", "_pop")) %>%
  dplyr::mutate(acum_new_pop=(acum_new/value)*100000) %>% 
  assign("coronavirus_treds_plus_pop",., envir = .GlobalEnv)

my.formula <- acum_new_pop ~ value_pop + value_health_exp + value_beds + value_gdp

tercer_grafico<- 
  ggplot(data = coronavirus_treds_plus_pop, aes(x = days_passed)) +
  stat_smooth(method = "lm", se = T, aes(y = acum_new_pop, fill = as.factor(chile),color = as.factor(chile)), linetype="dashed") +
  # stat_smooth(method = "lm", se = T,aes(y = acum_new_pop, color=as.factor(chile)), formula=my.formula,data=coronavirus_plot_tiempo_por_paises_prim_caso_pop_1_exp) +
  stat_summary(geom = "line", fun.y = median, size = 1, aes(y = acum_new_pop, color=as.factor(chile))) +
  stat_summary(geom="ribbon", aes(y = acum_new_pop,color="green" ), fun.ymin = function(x) quantile(x, 0.25), fun.ymax = function(x) quantile(x, 0.75), alpha=.45)+ 
  #geom_line(aes(y = acum_new), color = "blue") +
  #geom_line(aes(y = B19301_001E), color = "red") +
  labs(title = paste0("Figure 4. Linear Trends of Aggregated Data of Chile v/s Other States of \nConfirmed Cases COVID-19 in Latin America"),
       y = "No. Confirmed Cases per 100,000 hab", x = "Days Since the First Diagnosed",
       caption="Note: Linear Regression in Dashed Lines, SE's (Std. Errors) in gray; Other countries: Argentina,Bolivia,Brazil,Colombia,Costa Rica,Cuba,Dom. Republic,\nEcuador,Honduras,Mexico, Panama,Paraguay,Peru, Uruguay & Venezuela")+ 
  #scale_x_continuous("Quarters & Years",breaks=seq(0,14,7)) +
  theme(axis.text.x = element_text(size=10,vjust = 0.5), 
        axis.title.y = element_text(size = 9.5)) +
  theme(legend.text = element_text(size = 12, face = "italic")) +
  scale_colour_discrete(name  ="Legend", 
                        labels=c("Other Countries", "Chile", "25th & 75th percentiles\n  Orher Countries")) +
  #Medians in continuous lines, 25th and 75th \npercentiles in Shaded Areas
  #scale_fill_manual(values=c("red","blue"))+
  # ylim(c(0,100))+
  #  scale_fill_manual(values=c("0","1"),labels=c("Otros Países", "Chile")) +
  sjPlot::theme_sjplot2() +
  theme(plot.title = element_text(color="darkblue"))+
  theme(legend.title = element_text(size= 10, colour = "black"), legend.position='bottom',
        legend.text=element_text(size=7))+
  theme(plot.caption = element_text(hjust = 0, face = "italic",size = 7))+
  guides( fill = F)
tercer_grafico

However, when we transformed the number of confirmed cases into a logaritmic scale, the difference seems to bend over the other countries more clearly, differing from the 15th day since the first case (See Figure 4 & 5).

#POPULATION DATASET
library(readxl)

coronavirus_treds_plus_pop %>%
  dplyr::mutate(log_acum_new_pop=log(acum_new_pop)) %>%
  assign("coronavirus_treds_plus_pop_log",., envir = .GlobalEnv)

#JOIN DATASET W POP
tercer_grafico<- 
  ggplot(data = coronavirus_treds_plus_pop_log, aes(x = days_passed)) +
  stat_smooth(method = "lm", se = T, aes(y = log_acum_new_pop, fill = as.factor(chile),color = as.factor(chile)), linetype="dashed") +
  stat_summary(geom = "line", fun.y = median, size = 1, aes(y = log_acum_new_pop, color=as.factor(chile))) +
  stat_summary(geom="ribbon", aes(y = log_acum_new_pop,color="green" ), fun.ymin = function(x) quantile(x, 0.25), fun.ymax = function(x) quantile(x, 0.75), alpha=.45)+ 
  #geom_line(aes(y = acum_new), color = "blue") +
  #geom_line(aes(y = B19301_001E), color = "red") +
  labs(title = paste0("Figure 5. Linear Trends of Aggregated Data of Chile v/s Other States of \nConfirmed Cases COVID-19 in Latin America"),
       y = "No of Confirmed Cases per 100,000 hab (Log)", x = "Days Since the First Diagnosed",
       caption="Note: Linear Regression in Dashed Lines, SE's (Std. Errors) in gray;Other countries: Argentina,Bolivia,Brazil,Colombia,Costa Rica,\nCuba,Dom. Republic,Ecuador,Honduras,Mexico, Panama,Paraguay,Peru, Uruguay & Venezuela")+ 
  #scale_x_continuous("Quarters & Years",breaks=seq(0,14,7)) +
  theme(axis.text.x = element_text(size=13.5,vjust = 0.5), axis.text.y = element_text(size=14),
        axis.title.y = element_text(size = 13)) +
  theme(legend.text = element_text(size = 12, face = "italic")) +
  scale_colour_discrete(name  ="Legend", 
                        labels=c("Other Countries", "Chile", "25th & 75th percentiles\n  Orher Countries")) +
  #Medians in continuous lines, 25th and 75th \npercentiles in Shaded Areas
  #scale_fill_manual(values=c("red","blue"))+
  # ylim(c(0,100))+
  #  scale_fill_manual(values=c("0","1"),labels=c("Otros Países", "Chile")) +
  sjPlot::theme_sjplot2() +
  theme(plot.title = element_text(color="darkblue"))+
  theme(legend.title = element_text(size= 10.5, colour = "black"), legend.position='bottom')+
  theme(plot.caption = element_text(hjust = 0, face = "italic", size=8.5))+
  guides( fill = F)

print(tercer_grafico)

We also analysed deaths in Latinoamerican countries in the reported deaths through a logaritmic scale. The dots represents the lockdowns policies in the GOVERNMENT MEASURES DATASET available in this link.

covid19_dta <- download_merged_data(silent = TRUE, cached = TRUE)

covid19_dta_la<-covid19_dta %>% 
  dplyr::filter(country %in%
                  c('Chile', 'Argentina','Bolivia','Brazil',
                    'Colombia','Costa Rica','Cuba', 'Dom. Republic',
                    'Ecuador', 'Honduras','Mexico', 'Panama',
                    'Paraguay','Peru', 'Uruguay','Venezuela'))
p1<-plot_covid19_spread(
  covid19_dta_la, type = "deaths", min_cases = 0.1, min_by_ctry_obs = 1,
  edate_cutoff = 50, per_capita = T, log_scale = T,
  cumulative = TRUE, change_ave = 7,
  # highlight = c("CHL"),
  intervention = 'lockdown') +
  labs(title="Figure 6a. Linear Trends of Reported Deaths w/ COVID-19 in Latin America")+
  theme(plot.title = element_text(color="darkblue"))+
  theme(plot.subtitle = element_text(size = 9, face = "italic"))

p2<-plot_covid19_spread(
  covid19_dta_la, type = "deaths", min_cases = 0.1, min_by_ctry_obs = 1,
  edate_cutoff = 50, per_capita = T, log_scale = T,
  cumulative = TRUE, change_ave = 7,
  highlight = c("CHL"),
  intervention = 'lockdown') +
  labs(title="Figure 6b. Linear Trends of Reported Deaths in Chile w/ COVID-19 vs. Latin America", 
       subtitle= "Other countries: Argentina,Bolivia,Brazil,Colombia,Costa Rica,Cuba,Dom.Republic,Ecuador,Honduras,Mexico,Panama,\nParaguay,Peru,Uruguay & Venezuela")+
  theme(plot.title = element_text(color="darkblue"))+
  theme(plot.subtitle = element_text(size = 9, face = "italic"))

grid.arrange(p1, p2, nrow=2)

As seen in Figures 6a and 6b, Chile does not show the highest death rates. However, must note that deaths could be underreported.

Datasets of Hospitals and Individual Cases By Geographical Location

We obtained confirmed cases from COVID 19 from this link, to map cases by region. Cases are shown in Table 2.

#"https://raw.githubusercontent.com/demm94/Covid19-CL/master/15-03-2020.csv"
##MODO JSON
library(dplyr)

minsal_casos_sin_hosp = rjson::fromJSON(file="https://atlas.jifo.co/api/connectors/53c116ec-41cb-4560-ae21-45b3263fa73c")

minsal_casos_sin_hosp<- data.table::data.table(t(as.data.frame(minsal_casos_sin_hosp))) %>%
  dplyr::mutate(V1=ifelse(V1=="Totales", "Región", V1)) %>%
  janitor::row_to_names(row_number = 1) %>%
  dplyr::slice(-c(n(),n()-1,n()-2,n()-3))
minsal_casos_sin_hosp %>% 
  knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
               caption = paste0("Table 2. Confirmed cases at ",format(Sys.time(), '%d-%m-%Y')),
               align =rep('c', 101)) %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 7) %>%
  kableExtra::scroll_box(width = "100%", height = "375px")

Table 2. Confirmed cases at 11-06-2020

Región	Totales	Casos nuevos totales	Nuevos con síntomas	Nuevos sin síntomas*	Fallecidos
Arica y Parinacota	930	16	16	0	10
Tarapacá	3,591	100	79	21	46
Antofagasta	3,559	157	143	14	56
Atacama	365	17	14	3	0
Coquimbo	1,256	81	78	3	6
Valparaíso	5,614	223	198	25	99
RM	119,746	4,620	4,235	385	2,115
O’Higgins	1,442	125	117	8	26
Maule	2,440	151	123	28	18
Ñuble	1,616	32	24	8	20
Biobío	2,846	152	138	14	11
Araucanía	2,488	43	42	1	33
Los Ríos	452	6	6	0	8
Los Lagos	1,041	13	12	1	12
Aysén	21	0	0	0	0
Magallanes	1,089	1	1	0	15

These cases are being updated by ivanMSC /COVID19_Chile, most of them being infered due to the secrecy of MINSAL in publishing information In Figure 6, we can see that most of the cases are private centers and from the metropolitan region.

casos <- readr::read_csv("https://raw.githubusercontent.com/ivanMSC/COVID19_Chile/master/covid19_chile.csv")

casos %>% 
  knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
               caption = paste0("Table 2. Confirmed cases at ",format(Sys.time(), '%d-%m-%Y')," w/ completed info."),
               align =rep('c', 101)) %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 7) %>%
  kableExtra::scroll_box(width = "100%", height = "375px")

Table 2. Confirmed cases at 11-06-2020 w/ completed info.

Fecha	Region	Nuevo Confirmado	Nuevo Muerte	Nuevo Recuperado	Acum Confirmado	Acum Muerte	Acum Recuperado	Casos UCI
03-03-2020	Arica y Parinacota	0	0	0	0	0	0	NA
03-03-2020	Tarapacá	0	0	0	0	0	0	NA
03-03-2020	Antofagasta	0	0	0	0	0	0	NA
03-03-2020	Atacama	0	0	0	0	0	0	NA
03-03-2020	Coquimbo	0	0	0	0	0	0	NA
03-03-2020	Valparaíso	0	0	0	0	0	0	NA
03-03-2020	Metropolitana	0	0	0	0	0	0	NA
03-03-2020	O’Higgins	0	0	0	0	0	0	NA
03-03-2020	Maule	2	0	0	2	0	0	NA
03-03-2020	Ñuble	0	0	0	0	0	0	NA
03-03-2020	Biobío	0	0	0	0	0	0	NA
03-03-2020	Araucanía	0	0	0	0	0	0	NA
03-03-2020	Los Ríos	0	0	0	0	0	0	NA
03-03-2020	Los Lagos	0	0	0	0	0	0	NA
03-03-2020	Aysén	0	0	0	0	0	0	NA
03-03-2020	Magallanes	0	0	0	0	0	0	NA
03-03-2020	No Informado	0	0	0	0	0	0	NA
04-03-2020	Arica y Parinacota	0	0	0	0	0	0	NA
04-03-2020	Tarapacá	0	0	0	0	0	0	NA
04-03-2020	Antofagasta	0	0	0	0	0	0	NA
04-03-2020	Atacama	0	0	0	0	0	0	NA
04-03-2020	Coquimbo	0	0	0	0	0	0	NA
04-03-2020	Valparaíso	0	0	0	0	0	0	NA
04-03-2020	Metropolitana	1	0	0	1	0	0	NA
04-03-2020	O’Higgins	0	0	0	0	0	0	NA
04-03-2020	Maule	0	0	0	2	0	0	NA
04-03-2020	Ñuble	0	0	0	0	0	0	NA
04-03-2020	Biobío	0	0	0	0	0	0	NA
04-03-2020	Araucanía	0	0	0	0	0	0	NA
04-03-2020	Los Ríos	0	0	0	0	0	0	NA
04-03-2020	Los Lagos	0	0	0	0	0	0	NA
04-03-2020	Aysén	0	0	0	0	0	0	NA
04-03-2020	Magallanes	0	0	0	0	0	0	NA
04-03-2020	No Informado	0	0	0	0	0	0	NA
05-03-2020	Arica y Parinacota	0	0	0	0	0	0	NA
05-03-2020	Tarapacá	0	0	0	0	0	0	NA
05-03-2020	Antofagasta	0	0	0	0	0	0	NA
05-03-2020	Atacama	0	0	0	0	0	0	NA
05-03-2020	Coquimbo	0	0	0	0	0	0	NA
05-03-2020	Valparaíso	0	0	0	0	0	0	NA
05-03-2020	Metropolitana	1	0	0	2	0	0	NA
05-03-2020	O’Higgins	0	0	0	0	0	0	NA
05-03-2020	Maule	0	0	0	2	0	0	NA
05-03-2020	Ñuble	0	0	0	0	0	0	NA
05-03-2020	Biobío	0	0	0	0	0	0	NA
05-03-2020	Araucanía	0	0	0	0	0	0	NA
05-03-2020	Los Ríos	0	0	0	0	0	0	NA
05-03-2020	Los Lagos	0	0	0	0	0	0	NA
05-03-2020	Aysén	0	0	0	0	0	0	NA
05-03-2020	Magallanes	0	0	0	0	0	0	NA
05-03-2020	No Informado	0	0	0	0	0	0	NA
06-03-2020	Arica y Parinacota	0	0	0	0	0	0	NA
06-03-2020	Tarapacá	0	0	0	0	0	0	NA
06-03-2020	Antofagasta	0	0	0	0	0	0	NA
06-03-2020	Atacama	0	0	0	0	0	0	NA
06-03-2020	Coquimbo	0	0	0	0	0	0	NA
06-03-2020	Valparaíso	0	0	0	0	0	0	NA
06-03-2020	Metropolitana	1	0	0	3	0	0	NA
06-03-2020	O’Higgins	0	0	0	0	0	0	NA
06-03-2020	Maule	0	0	0	2	0	0	NA
06-03-2020	Ñuble	0	0	0	0	0	0	NA
06-03-2020	Biobío	0	0	0	0	0	0	NA
06-03-2020	Araucanía	0	0	0	0	0	0	NA
06-03-2020	Los Ríos	0	0	0	0	0	0	NA
06-03-2020	Los Lagos	0	0	0	0	0	0	NA
06-03-2020	Aysén	0	0	0	0	0	0	NA
06-03-2020	Magallanes	0	0	0	0	0	0	NA
06-03-2020	No Informado	0	0	0	0	0	0	NA
07-03-2020	Arica y Parinacota	0	0	0	0	0	0	NA
07-03-2020	Tarapacá	0	0	0	0	0	0	NA
07-03-2020	Antofagasta	0	0	0	0	0	0	NA
07-03-2020	Atacama	0	0	0	0	0	0	NA
07-03-2020	Coquimbo	0	0	0	0	0	0	NA
07-03-2020	Valparaíso	0	0	0	0	0	0	NA
07-03-2020	Metropolitana	1	0	0	4	0	0	NA
07-03-2020	O’Higgins	0	0	0	0	0	0	NA
07-03-2020	Maule	0	0	0	2	0	0	NA
07-03-2020	Ñuble	0	0	0	0	0	0	NA
07-03-2020	Biobío	0	0	0	0	0	0	NA
07-03-2020	Araucanía	0	0	0	0	0	0	NA
07-03-2020	Los Ríos	0	0	0	0	0	0	NA
07-03-2020	Los Lagos	1	0	0	1	0	0	NA
07-03-2020	Aysén	0	0	0	0	0	0	NA
07-03-2020	Magallanes	0	0	0	0	0	0	NA
07-03-2020	No Informado	0	0	0	0	0	0	NA
08-03-2020	Arica y Parinacota	0	0	0	0	0	0	NA
08-03-2020	Tarapacá	0	0	0	0	0	0	NA
08-03-2020	Antofagasta	0	0	0	0	0	0	NA
08-03-2020	Atacama	0	0	0	0	0	0	NA
08-03-2020	Coquimbo	0	0	0	0	0	0	NA
08-03-2020	Valparaíso	0	0	0	0	0	0	NA
08-03-2020	Metropolitana	2	0	0	6	0	0	NA
08-03-2020	O’Higgins	0	0	0	0	0	0	NA
08-03-2020	Maule	1	0	0	3	0	0	NA
08-03-2020	Ñuble	0	0	0	0	0	0	NA
08-03-2020	Biobío	0	0	0	0	0	0	NA
08-03-2020	Araucanía	0	0	0	0	0	0	NA
08-03-2020	Los Ríos	0	0	0	0	0	0	NA
08-03-2020	Los Lagos	0	0	0	1	0	0	NA
08-03-2020	Aysén	0	0	0	0	0	0	NA
08-03-2020	Magallanes	0	0	0	0	0	0	NA
08-03-2020	No Informado	0	0	0	0	0	0	NA
09-03-2020	Arica y Parinacota	0	0	0	0	0	0	NA
09-03-2020	Tarapacá	0	0	0	0	0	0	NA
09-03-2020	Antofagasta	0	0	0	0	0	0	NA
09-03-2020	Atacama	0	0	0	0	0	0	NA
09-03-2020	Coquimbo	0	0	0	0	0	0	NA
09-03-2020	Valparaíso	0	0	0	0	0	0	NA
09-03-2020	Metropolitana	1	0	0	7	0	0	NA
09-03-2020	O’Higgins	0	0	0	0	0	0	NA
09-03-2020	Maule	1	0	0	4	0	0	NA
09-03-2020	Ñuble	0	0	0	0	0	0	NA
09-03-2020	Biobío	1	0	0	1	0	0	NA
09-03-2020	Araucanía	0	0	0	0	0	0	NA
09-03-2020	Los Ríos	0	0	0	0	0	0	NA
09-03-2020	Los Lagos	0	0	0	1	0	0	NA
09-03-2020	Aysén	0	0	0	0	0	0	NA
09-03-2020	Magallanes	0	0	0	0	0	0	NA
09-03-2020	No Informado	0	0	0	0	0	0	NA
10-03-2020	Arica y Parinacota	0	0	0	0	0	0	NA
10-03-2020	Tarapacá	0	0	0	0	0	0	NA
10-03-2020	Antofagasta	0	0	0	0	0	0	NA
10-03-2020	Atacama	0	0	0	0	0	0	NA
10-03-2020	Coquimbo	0	0	0	0	0	0	NA
10-03-2020	Valparaíso	0	0	0	0	0	0	NA
10-03-2020	Metropolitana	3	0	0	10	0	0	NA
10-03-2020	O’Higgins	0	0	0	0	0	0	NA
10-03-2020	Maule	1	0	0	5	0	0	NA
10-03-2020	Ñuble	0	0	0	0	0	0	NA
10-03-2020	Biobío	0	0	0	1	0	0	NA
10-03-2020	Araucanía	0	0	0	0	0	0	NA
10-03-2020	Los Ríos	0	0	0	0	0	0	NA
10-03-2020	Los Lagos	0	0	0	1	0	0	NA
10-03-2020	Aysén	0	0	0	0	0	0	NA
10-03-2020	Magallanes	0	0	0	0	0	0	NA
10-03-2020	No Informado	0	0	0	0	0	0	NA
11-03-2020	Arica y Parinacota	0	0	0	0	0	0	NA
11-03-2020	Tarapacá	0	0	0	0	0	0	NA
11-03-2020	Antofagasta	0	0	0	0	0	0	NA
11-03-2020	Atacama	0	0	0	0	0	0	NA
11-03-2020	Coquimbo	0	0	0	0	0	0	NA
11-03-2020	Valparaíso	0	0	0	0	0	0	NA
11-03-2020	Metropolitana	4	0	0	14	0	0	NA
11-03-2020	O’Higgins	0	0	0	0	0	0	NA
11-03-2020	Maule	2	0	0	7	0	0	NA
11-03-2020	Ñuble	0	0	0	0	0	0	NA
11-03-2020	Biobío	0	0	0	1	0	0	NA
11-03-2020	Araucanía	0	0	0	0	0	0	NA
11-03-2020	Los Ríos	0	0	0	0	0	0	NA
11-03-2020	Los Lagos	0	0	0	1	0	0	NA
11-03-2020	Aysén	0	0	0	0	0	0	NA
11-03-2020	Magallanes	0	0	0	0	0	0	NA
11-03-2020	No Informado	0	0	0	0	0	0	NA
12-03-2020	Arica y Parinacota	0	0	0	0	0	0	NA
12-03-2020	Tarapacá	0	0	0	0	0	0	NA
12-03-2020	Antofagasta	0	0	0	0	0	0	NA
12-03-2020	Atacama	0	0	0	0	0	0	NA
12-03-2020	Coquimbo	0	0	0	0	0	0	NA
12-03-2020	Valparaíso	0	0	0	0	0	0	NA
12-03-2020	Metropolitana	9	0	0	23	0	0	NA
12-03-2020	O’Higgins	0	0	0	0	0	0	NA
12-03-2020	Maule	0	0	0	7	0	0	NA
12-03-2020	Ñuble	1	0	0	1	0	0	NA
12-03-2020	Biobío	0	0	0	1	0	0	NA
12-03-2020	Araucanía	0	0	0	0	0	0	NA
12-03-2020	Los Ríos	0	0	0	0	0	0	NA
12-03-2020	Los Lagos	0	0	0	1	0	0	NA
12-03-2020	Aysén	0	0	0	0	0	0	NA
12-03-2020	Magallanes	0	0	0	0	0	0	NA
12-03-2020	No Informado	0	0	0	0	0	0	NA
13-03-2020	Arica y Parinacota	0	0	0	0	0	0	NA
13-03-2020	Tarapacá	0	0	0	0	0	0	NA
13-03-2020	Antofagasta	0	0	0	0	0	0	NA
13-03-2020	Atacama	0	0	0	0	0	0	NA
13-03-2020	Coquimbo	0	0	0	0	0	0	NA
13-03-2020	Valparaíso	0	0	0	0	0	0	NA
13-03-2020	Metropolitana	6	0	0	29	0	0	NA
13-03-2020	O’Higgins	0	0	0	0	0	0	NA
13-03-2020	Maule	1	0	0	8	0	0	NA
13-03-2020	Ñuble	1	0	0	2	0	0	NA
13-03-2020	Biobío	2	0	0	3	0	0	NA
13-03-2020	Araucanía	0	0	0	0	0	0	NA
13-03-2020	Los Ríos	0	0	0	0	0	0	NA
13-03-2020	Los Lagos	0	0	0	1	0	0	NA
13-03-2020	Aysén	0	0	0	0	0	0	NA
13-03-2020	Magallanes	0	0	0	0	0	0	NA
13-03-2020	No Informado	0	0	0	0	0	0	NA
14-03-2020	Arica y Parinacota	0	0	0	0	0	0	NA
14-03-2020	Tarapacá	0	0	0	0	0	0	NA
14-03-2020	Antofagasta	2	0	0	2	0	0	NA
14-03-2020	Atacama	1	0	0	1	0	0	NA
14-03-2020	Coquimbo	0	0	0	0	0	0	NA
14-03-2020	Valparaíso	0	0	0	0	0	0	NA
14-03-2020	Metropolitana	11	0	0	40	0	0	NA
14-03-2020	O’Higgins	0	0	0	0	0	0	NA
14-03-2020	Maule	1	0	0	9	0	0	NA
14-03-2020	Ñuble	2	0	0	4	0	0	NA
14-03-2020	Biobío	0	0	0	3	0	0	NA
14-03-2020	Araucanía	0	0	0	0	0	0	NA
14-03-2020	Los Ríos	0	0	0	0	0	0	NA
14-03-2020	Los Lagos	0	0	0	1	0	0	NA
14-03-2020	Aysén	1	0	0	1	0	0	NA
14-03-2020	Magallanes	0	0	0	0	0	0	NA
14-03-2020	No Informado	0	0	0	0	0	0	NA
15-03-2020	Arica y Parinacota	0	0	0	0	0	0	NA
15-03-2020	Tarapacá	0	0	0	0	0	0	NA
15-03-2020	Antofagasta	0	0	0	2	0	0	NA
15-03-2020	Atacama	0	0	0	1	0	0	NA
15-03-2020	Coquimbo	0	0	0	0	0	0	NA
15-03-2020	Valparaíso	0	0	0	0	0	0	NA
15-03-2020	Metropolitana	14	0	0	54	0	0	NA
15-03-2020	O’Higgins	0	0	0	0	0	0	NA
15-03-2020	Maule	0	0	0	9	0	0	NA
15-03-2020	Ñuble	0	0	0	4	0	0	NA
15-03-2020	Biobío	0	0	0	3	0	0	NA
15-03-2020	Araucanía	0	0	0	0	0	0	NA
15-03-2020	Los Ríos	0	0	0	0	0	0	NA
15-03-2020	Los Lagos	0	0	0	1	0	0	NA
15-03-2020	Aysén	0	0	0	1	0	0	NA
15-03-2020	Magallanes	0	0	0	0	0	0	NA
15-03-2020	No Informado	0	0	0	0	0	0	NA
16-03-2020	Arica y Parinacota	0	0	0	0	0	0	NA
16-03-2020	Tarapacá	0	0	0	0	0	0	NA
16-03-2020	Antofagasta	0	0	0	2	0	0	NA
16-03-2020	Atacama	0	0	0	1	0	0	NA
16-03-2020	Coquimbo	0	0	0	0	0	0	NA
16-03-2020	Valparaíso	1	0	0	1	0	0	NA
16-03-2020	Metropolitana	69	0	0	123	0	0	NA
16-03-2020	O’Higgins	0	0	0	0	0	0	NA
16-03-2020	Maule	0	0	0	9	0	0	NA
16-03-2020	Ñuble	8	0	0	12	0	0	NA
16-03-2020	Biobío	1	0	0	4	0	0	NA
16-03-2020	Araucanía	1	0	0	1	0	0	NA
16-03-2020	Los Ríos	1	0	0	1	0	0	NA
16-03-2020	Los Lagos	0	0	0	1	0	0	NA
16-03-2020	Aysén	0	0	0	1	0	0	NA
16-03-2020	Magallanes	0	0	0	0	0	0	NA
16-03-2020	No Informado	0	0	0	0	0	0	NA
17-03-2020	Arica y Parinacota	0	0	0	0	0	0	NA
17-03-2020	Tarapacá	0	0	0	0	0	0	NA
17-03-2020	Antofagasta	0	0	0	2	0	0	NA
17-03-2020	Atacama	0	0	0	1	0	0	NA
17-03-2020	Coquimbo	0	0	0	0	0	0	NA
17-03-2020	Valparaíso	0	0	0	1	0	0	NA
17-03-2020	Metropolitana	29	0	0	152	0	0	NA
17-03-2020	O’Higgins	0	0	0	0	0	0	NA
17-03-2020	Maule	0	0	0	9	0	0	NA
17-03-2020	Ñuble	14	0	0	26	0	0	NA
17-03-2020	Biobío	0	0	0	4	0	0	NA
17-03-2020	Araucanía	0	0	0	1	0	0	NA
17-03-2020	Los Ríos	0	0	0	1	0	0	NA
17-03-2020	Los Lagos	0	0	0	1	0	0	NA
17-03-2020	Aysén	0	0	0	1	0	0	NA
17-03-2020	Magallanes	2	0	0	2	0	0	NA
17-03-2020	No Informado	0	0	0	0	0	0	8
18-03-2020	Arica y Parinacota	0	0	0	0	0	0	NA
18-03-2020	Tarapacá	0	0	0	0	0	0	NA
18-03-2020	Antofagasta	0	0	0	2	0	0	NA
18-03-2020	Atacama	0	0	0	1	0	0	NA
18-03-2020	Coquimbo	0	0	0	0	0	0	NA
18-03-2020	Valparaíso	0	0	0	1	0	0	NA
18-03-2020	Metropolitana	22	0	0	174	0	0	NA
18-03-2020	O’Higgins	0	0	0	0	0	0	NA
18-03-2020	Maule	2	0	0	11	0	0	NA
18-03-2020	Ñuble	0	0	0	26	0	0	NA
18-03-2020	Biobío	3	0	0	7	0	0	NA
18-03-2020	Araucanía	3	0	0	4	0	0	NA
18-03-2020	Los Ríos	0	0	0	1	0	0	NA
18-03-2020	Los Lagos	7	0	0	8	0	0	NA
18-03-2020	Aysén	0	0	0	1	0	0	NA
18-03-2020	Magallanes	0	0	0	2	0	0	NA
18-03-2020	No Informado	0	0	0	0	0	0	19
19-03-2020	Arica y Parinacota	1	0	0	1	0	0	NA
19-03-2020	Tarapacá	0	0	0	0	0	0	NA
19-03-2020	Antofagasta	0	0	0	2	0	0	NA
19-03-2020	Atacama	0	0	0	1	0	0	NA
19-03-2020	Coquimbo	2	0	0	2	0	0	NA
19-03-2020	Valparaíso	3	0	0	4	0	0	NA
19-03-2020	Metropolitana	73	0	0	247	0	0	NA
19-03-2020	O’Higgins	2	0	0	2	0	0	NA
19-03-2020	Maule	3	0	0	14	0	0	NA
19-03-2020	Ñuble	2	0	0	28	0	0	NA
19-03-2020	Biobío	7	0	0	14	0	0	NA
19-03-2020	Araucanía	3	0	0	7	0	0	NA
19-03-2020	Los Ríos	0	0	0	1	0	0	NA
19-03-2020	Los Lagos	8	0	0	16	0	0	NA
19-03-2020	Aysén	0	0	0	1	0	0	NA
19-03-2020	Magallanes	0	0	0	2	0	0	NA
19-03-2020	No Informado	0	0	5	0	0	5	32
20-03-2020	Arica y Parinacota	0	0	0	1	0	0	NA
20-03-2020	Tarapacá	0	0	0	0	0	0	NA
20-03-2020	Antofagasta	4	0	0	6	0	0	NA
20-03-2020	Atacama	0	0	0	1	0	0	NA
20-03-2020	Coquimbo	1	0	0	3	0	0	NA
20-03-2020	Valparaíso	1	0	0	5	0	0	NA
20-03-2020	Metropolitana	57	0	0	304	0	0	NA
20-03-2020	O’Higgins	4	0	0	6	0	0	NA
20-03-2020	Maule	0	0	0	14	0	0	NA
20-03-2020	Ñuble	16	0	0	44	0	0	NA
20-03-2020	Biobío	5	0	0	19	0	0	NA
20-03-2020	Araucanía	2	0	0	9	0	0	NA
20-03-2020	Los Ríos	0	0	0	1	0	0	NA
20-03-2020	Los Lagos	2	0	0	18	0	0	NA
20-03-2020	Aysén	0	0	0	1	0	0	NA
20-03-2020	Magallanes	0	0	0	2	0	0	NA
20-03-2020	No Informado	0	0	1	0	0	6	36
21-03-2020	Arica y Parinacota	0	0	0	1	0	0	NA
21-03-2020	Tarapacá	0	0	0	0	0	0	NA
21-03-2020	Antofagasta	4	0	0	10	0	0	NA
21-03-2020	Atacama	0	0	0	1	0	0	NA
21-03-2020	Coquimbo	0	0	0	3	0	0	NA
21-03-2020	Valparaíso	8	0	0	13	0	0	NA
21-03-2020	Metropolitana	55	1	0	359	1	0	NA
21-03-2020	O’Higgins	1	0	0	7	0	0	NA
21-03-2020	Maule	1	0	0	15	0	0	NA
21-03-2020	Ñuble	14	0	0	58	0	0	NA
21-03-2020	Biobío	11	0	0	30	0	0	NA
21-03-2020	Araucanía	6	0	0	15	0	0	NA
21-03-2020	Los Ríos	0	0	0	1	0	0	NA
21-03-2020	Los Lagos	2	0	0	20	0	0	NA
21-03-2020	Aysén	0	0	0	1	0	0	NA
21-03-2020	Magallanes	1	0	0	3	0	0	NA
21-03-2020	No Informado	0	0	2	0	0	8	29
22-03-2020	Arica y Parinacota	1	0	0	2	0	0	NA
22-03-2020	Tarapacá	0	0	0	0	0	0	NA
22-03-2020	Antofagasta	1	0	0	11	0	0	NA
22-03-2020	Atacama	0	0	0	1	0	0	NA
22-03-2020	Coquimbo	2	0	0	5	0	0	NA
22-03-2020	Valparaíso	3	0	0	16	0	0	NA
22-03-2020	Metropolitana	50	0	0	409	1	0	NA
22-03-2020	O’Higgins	1	0	0	8	0	0	NA
22-03-2020	Maule	10	0	0	25	0	0	NA
22-03-2020	Ñuble	6	0	0	64	0	0	NA
22-03-2020	Biobío	5	0	0	35	0	0	NA
22-03-2020	Araucanía	12	0	0	27	0	0	NA
22-03-2020	Los Ríos	0	0	0	1	0	0	NA
22-03-2020	Los Lagos	3	0	0	23	0	0	NA
22-03-2020	Aysén	0	0	0	1	0	0	NA
22-03-2020	Magallanes	1	0	0	4	0	0	NA
22-03-2020	No Informado	0	0	0	0	0	8	34
23-03-2020	Arica y Parinacota	0	0	0	2	0	0	NA
23-03-2020	Tarapacá	1	0	0	1	0	0	NA
23-03-2020	Antofagasta	0	0	0	11	0	0	NA
23-03-2020	Atacama	0	0	0	1	0	0	NA
23-03-2020	Coquimbo	1	0	0	6	0	0	NA
23-03-2020	Valparaíso	3	0	0	19	0	0	NA
23-03-2020	Metropolitana	50	0	0	459	1	0	NA
23-03-2020	O’Higgins	0	0	0	8	0	0	NA
23-03-2020	Maule	3	0	0	28	0	0	NA
23-03-2020	Ñuble	17	0	0	81	0	0	NA
23-03-2020	Biobío	11	0	0	46	0	0	NA
23-03-2020	Araucanía	13	0	0	40	0	0	NA
23-03-2020	Los Ríos	3	0	0	4	0	0	NA
23-03-2020	Los Lagos	10	0	0	33	0	0	NA
23-03-2020	Aysén	0	0	0	1	0	0	NA
23-03-2020	Magallanes	2	0	0	6	0	0	NA
23-03-2020	No Informado	0	0	3	0	0	11	40
24-03-2020	Arica y Parinacota	0	0	0	2	0	0	NA
24-03-2020	Tarapacá	3	0	0	4	0	0	NA
24-03-2020	Antofagasta	2	0	0	13	0	0	NA
24-03-2020	Atacama	0	0	0	1	0	0	NA
24-03-2020	Coquimbo	5	0	0	11	0	0	NA
24-03-2020	Valparaíso	6	0	0	25	0	0	NA
24-03-2020	Metropolitana	81	1	0	540	2	0	NA
24-03-2020	O’Higgins	1	0	0	9	0	0	NA
24-03-2020	Maule	1	0	0	29	0	0	NA
24-03-2020	Ñuble	24	0	0	105	0	0	NA
24-03-2020	Biobío	27	0	0	73	0	0	NA
24-03-2020	Araucanía	19	0	0	59	0	0	NA
24-03-2020	Los Ríos	2	0	0	6	0	0	NA
24-03-2020	Los Lagos	3	0	0	36	0	0	NA
24-03-2020	Aysén	0	0	0	1	0	0	NA
24-03-2020	Magallanes	2	0	0	8	0	0	NA
24-03-2020	No Informado	0	0	6	0	0	17	43
25-03-2020	Arica y Parinacota	0	0	0	2	0	0	NA
25-03-2020	Tarapacá	0	0	0	4	0	0	NA
25-03-2020	Antofagasta	6	0	0	19	0	0	NA
25-03-2020	Atacama	0	0	0	1	0	0	NA
25-03-2020	Coquimbo	1	0	0	12	0	0	NA
25-03-2020	Valparaíso	7	0	0	32	0	0	NA
25-03-2020	Metropolitana	142	0	0	682	2	0	NA
25-03-2020	O’Higgins	2	0	0	11	0	0	NA
25-03-2020	Maule	1	0	0	30	0	0	NA
25-03-2020	Ñuble	6	0	0	111	0	0	NA
25-03-2020	Biobío	22	1	0	95	1	0	NA
25-03-2020	Araucanía	15	0	0	74	0	0	NA
25-03-2020	Los Ríos	5	0	0	11	0	0	NA
25-03-2020	Los Lagos	8	0	0	44	0	0	NA
25-03-2020	Aysén	0	0	0	1	0	0	NA
25-03-2020	Magallanes	5	0	0	13	0	0	NA
25-03-2020	No Informado	0	0	5	0	0	22	44
26-03-2020	Arica y Parinacota	1	0	0	3	0	0	NA
26-03-2020	Tarapacá	1	0	0	5	0	0	NA
26-03-2020	Antofagasta	1	0	0	20	0	0	NA
26-03-2020	Atacama	0	0	0	1	0	0	NA
26-03-2020	Coquimbo	1	0	0	13	0	0	NA
26-03-2020	Valparaíso	12	0	0	44	0	0	NA
26-03-2020	Metropolitana	64	0	0	746	2	0	NA
26-03-2020	O’Higgins	3	0	0	14	0	0	NA
26-03-2020	Maule	1	0	0	31	0	0	NA
26-03-2020	Ñuble	3	0	0	114	0	0	NA
26-03-2020	Biobío	14	1	0	109	2	0	NA
26-03-2020	Araucanía	37	0	0	111	0	0	NA
26-03-2020	Los Ríos	3	0	0	14	0	0	NA
26-03-2020	Los Lagos	16	0	0	60	0	0	NA
26-03-2020	Aysén	1	0	0	2	0	0	NA
26-03-2020	Magallanes	6	0	0	19	0	0	NA
26-03-2020	No Informado	0	0	11	0	0	33	52
27-03-2020	Arica y Parinacota	0	0	0	3	0	0	NA
27-03-2020	Tarapacá	0	0	0	5	0	0	NA
27-03-2020	Antofagasta	1	0	0	21	0	0	NA
27-03-2020	Atacama	0	0	0	1	0	0	NA
27-03-2020	Coquimbo	1	0	0	14	0	0	NA
27-03-2020	Valparaíso	5	0	0	49	0	0	NA
27-03-2020	Metropolitana	192	1	0	938	3	0	NA
27-03-2020	O’Higgins	2	0	0	16	0	0	NA
27-03-2020	Maule	1	0	0	32	0	0	NA
27-03-2020	Ñuble	30	0	0	144	0	0	NA
27-03-2020	Biobío	26	0	0	135	2	0	NA
27-03-2020	Araucanía	32	0	0	143	0	0	NA
27-03-2020	Los Ríos	8	0	0	22	0	0	NA
27-03-2020	Los Lagos	3	0	0	63	0	0	NA
27-03-2020	Aysén	0	0	0	2	0	0	NA
27-03-2020	Magallanes	3	0	0	22	0	0	NA
27-03-2020	No Informado	0	0	10	0	0	43	81
28-03-2020	Arica y Parinacota	0	0	0	3	0	0	NA
28-03-2020	Tarapacá	0	0	0	5	0	0	NA
28-03-2020	Antofagasta	4	0	0	25	0	0	NA
28-03-2020	Atacama	0	0	0	1	0	0	NA
28-03-2020	Coquimbo	1	0	0	15	0	0	NA
28-03-2020	Valparaíso	22	0	0	71	0	0	NA
28-03-2020	Metropolitana	146	0	0	1,084	3	0	NA
28-03-2020	O’Higgins	4	0	0	20	0	0	NA
28-03-2020	Maule	5	0	0	37	0	0	NA
28-03-2020	Ñuble	28	0	0	172	0	0	NA
28-03-2020	Biobío	16	0	0	151	2	0	NA
28-03-2020	Araucanía	34	1	0	177	1	0	NA
28-03-2020	Los Ríos	5	0	0	27	0	0	NA
28-03-2020	Los Lagos	30	0	0	93	0	0	NA
28-03-2020	Aysén	0	0	0	2	0	0	NA
28-03-2020	Magallanes	4	0	0	26	0	0	NA
28-03-2020	No Informado	0	0	18	0	0	61	105
29-03-2020	Arica y Parinacota	1	0	0	4	0	0	NA
29-03-2020	Tarapacá	1	0	0	6	0	0	NA
29-03-2020	Antofagasta	2	0	0	27	0	0	NA
29-03-2020	Atacama	1	0	0	2	0	0	NA
29-03-2020	Coquimbo	3	0	0	18	0	0	NA
29-03-2020	Valparaíso	9	0	0	80	0	0	NA
29-03-2020	Metropolitana	83	0	0	1,167	3	0	NA
29-03-2020	O’Higgins	1	0	0	21	0	0	NA
29-03-2020	Maule	5	0	0	42	0	0	NA
29-03-2020	Ñuble	25	0	0	197	0	0	NA
29-03-2020	Biobío	34	0	0	185	2	0	NA
29-03-2020	Araucanía	28	1	0	205	2	0	NA
29-03-2020	Los Ríos	13	0	0	40	0	0	NA
29-03-2020	Los Lagos	11	0	0	104	0	0	NA
29-03-2020	Aysén	0	0	0	2	0	0	NA
29-03-2020	Magallanes	13	0	0	39	0	0	NA
29-03-2020	No Informado	0	0	14	0	0	75	122
30-03-2020	Arica y Parinacota	2	0	0	6	0	0	NA
30-03-2020	Tarapacá	2	0	0	8	0	0	NA
30-03-2020	Antofagasta	8	0	0	35	0	0	NA
30-03-2020	Atacama	0	0	0	2	0	0	NA
30-03-2020	Coquimbo	9	0	0	27	0	0	NA
30-03-2020	Valparaíso	28	0	0	108	0	0	NA
30-03-2020	Metropolitana	128	0	0	1,295	3	0	NA
30-03-2020	O’Higgins	0	0	0	21	0	0	NA
30-03-2020	Maule	12	1	0	54	1	0	NA
30-03-2020	Ñuble	32	0	0	229	0	0	NA
30-03-2020	Biobío	16	0	0	201	2	0	NA
30-03-2020	Araucanía	42	0	0	247	2	0	NA
30-03-2020	Los Ríos	7	0	0	47	0	0	NA
30-03-2020	Los Lagos	24	0	0	128	0	0	NA
30-03-2020	Aysén	0	0	0	2	0	0	NA
30-03-2020	Magallanes	0	0	0	39	0	0	NA
30-03-2020	No Informado	0	0	81	0	0	156	138
31-03-2020	Arica y Parinacota	0	0	0	6	0	0	NA
31-03-2020	Tarapacá	2	0	0	10	0	0	NA
31-03-2020	Antofagasta	0	0	0	35	0	0	NA
31-03-2020	Atacama	0	0	0	2	0	0	NA
31-03-2020	Coquimbo	1	0	0	28	0	0	NA
31-03-2020	Valparaíso	7	1	0	115	1	0	NA
31-03-2020	Metropolitana	125	1	0	1,420	4	0	NA
31-03-2020	O’Higgins	2	0	0	23	0	0	NA
31-03-2020	Maule	8	0	0	62	1	0	NA
31-03-2020	Ñuble	16	0	0	245	0	0	NA
31-03-2020	Biobío	15	0	0	216	2	0	NA
31-03-2020	Araucanía	55	2	0	302	4	0	NA
31-03-2020	Los Ríos	11	0	0	58	0	0	NA
31-03-2020	Los Lagos	23	0	0	151	0	0	NA
31-03-2020	Aysén	0	0	0	2	0	0	NA
31-03-2020	Magallanes	24	0	0	63	0	0	NA
31-03-2020	No Informado	0	0	0	0	0	156	NA
01-04-2020	Arica y Parinacota	0	0	0	6	0	0	0
01-04-2020	Tarapacá	0	0	0	10	0	0	1
01-04-2020	Antofagasta	4	0	0	39	0	0	3
01-04-2020	Atacama	1	0	0	3	0	0	0
01-04-2020	Coquimbo	2	0	0	30	0	0	1
01-04-2020	Valparaíso	21	0	0	136	1	0	14
01-04-2020	Metropolitana	101	1	0	1,521	5	0	83
01-04-2020	O’Higgins	3	0	0	26	0	0	5
01-04-2020	Maule	9	0	0	71	1	0	3
01-04-2020	Ñuble	14	0	0	259	0	0	7
01-04-2020	Biobío	24	0	0	240	2	0	14
01-04-2020	Araucanía	41	2	0	343	6	0	26
01-04-2020	Los Ríos	6	1	0	64	1	0	1
01-04-2020	Los Lagos	30	0	0	181	0	0	10
01-04-2020	Aysén	1	0	0	3	0	0	0
01-04-2020	Magallanes	36	0	0	99	0	0	5
01-04-2020	No Informado	0	0	78	0	0	234	NA
02-04-2020	Arica y Parinacota	1	0	0	7	0	0	0
02-04-2020	Tarapacá	2	0	0	12	0	0	1
02-04-2020	Antofagasta	8	0	0	47	0	0	4
02-04-2020	Atacama	0	0	0	3	0	0	0
02-04-2020	Coquimbo	4	0	0	34	0	0	1
02-04-2020	Valparaíso	20	0	0	156	1	0	17
02-04-2020	Metropolitana	115	1	0	1,636	6	0	90
02-04-2020	O’Higgins	6	0	0	32	0	0	5
02-04-2020	Maule	11	0	0	82	1	0	3
02-04-2020	Ñuble	82	0	0	341	0	0	8
02-04-2020	Biobío	35	0	0	275	2	0	18
02-04-2020	Araucanía	46	1	0	389	7	0	29
02-04-2020	Los Ríos	4	0	0	68	1	0	2
02-04-2020	Los Lagos	22	0	0	203	0	0	12
02-04-2020	Aysén	2	0	0	5	0	0	0
02-04-2020	Magallanes	15	0	0	114	0	0	10
02-04-2020	No Informado	0	0	101	0	0	335	NA
03-04-2020	Arica y Parinacota	5	0	0	12	0	0	0
03-04-2020	Tarapacá	1	0	0	13	0	0	1
03-04-2020	Antofagasta	7	0	0	54	0	0	3
03-04-2020	Atacama	2	0	0	5	0	0	0
03-04-2020	Coquimbo	1	0	0	35	0	0	1
03-04-2020	Valparaíso	20	0	0	176	1	0	18
03-04-2020	Metropolitana	106	2	0	1,742	8	0	107
03-04-2020	O’Higgins	1	0	0	33	0	0	5
03-04-2020	Maule	7	0	0	89	1	0	3
03-04-2020	Ñuble	29	1	0	370	1	0	10
03-04-2020	Biobío	27	0	0	302	2	0	21
03-04-2020	Araucanía	43	0	0	432	7	0	37
03-04-2020	Los Ríos	10	0	0	78	1	0	2
03-04-2020	Los Lagos	31	0	0	234	0	0	18
03-04-2020	Aysén	1	0	0	6	0	0	0
03-04-2020	Magallanes	42	1	0	156	1	0	11
03-04-2020	No Informado	0	0	92	0	0	427	NA
04-04-2020	Arica y Parinacota	0	0	0	12	0	0	0
04-04-2020	Tarapacá	4	0	0	17	0	0	1
04-04-2020	Antofagasta	1	0	0	55	0	0	3
04-04-2020	Atacama	0	0	0	5	0	0	0
04-04-2020	Coquimbo	9	0	0	44	0	0	2
04-04-2020	Valparaíso	9	0	0	185	1	0	22
04-04-2020	Metropolitana	215	1	0	1,957	9	0	129
04-04-2020	O’Higgins	5	0	0	38	0	0	5
04-04-2020	Maule	8	0	0	97	1	0	3
04-04-2020	Ñuble	31	1	0	401	2	0	11
04-04-2020	Biobío	45	0	0	347	2	0	24
04-04-2020	Araucanía	45	2	0	477	9	0	44
04-04-2020	Los Ríos	11	0	0	89	1	0	2
04-04-2020	Los Lagos	25	0	0	259	0	0	22
04-04-2020	Aysén	0	0	0	6	0	0	0
04-04-2020	Magallanes	16	1	0	172	2	0	12
04-04-2020	No Informado	0	0	101	0	0	528	NA
05-04-2020	Arica y Parinacota	16	0	0	28	0	0	2
05-04-2020	Tarapacá	3	0	0	20	0	0	1
05-04-2020	Antofagasta	5	0	0	60	0	0	3
05-04-2020	Atacama	0	0	0	5	0	0	0
05-04-2020	Coquimbo	6	0	0	50	0	0	1
05-04-2020	Valparaíso	6	0	0	191	1	0	27
05-04-2020	Metropolitana	145	1	0	2,102	10	0	146
05-04-2020	O’Higgins	3	0	0	41	0	0	5
05-04-2020	Maule	6	0	0	103	1	0	4
05-04-2020	Ñuble	24	1	0	425	3	0	10
05-04-2020	Biobío	18	0	0	365	2	0	26
05-04-2020	Araucanía	32	4	0	509	13	0	44
05-04-2020	Los Ríos	10	0	0	99	1	0	3
05-04-2020	Los Lagos	17	1	0	276	1	0	22
05-04-2020	Aysén	1	0	0	7	0	0	0
05-04-2020	Magallanes	18	0	0	190	2	0	13
05-04-2020	No Informado	0	0	70	0	0	598	NA
06-04-2020	Arica y Parinacota	13	0	0	41	0	0	4
06-04-2020	Tarapacá	1	0	0	21	0	0	2
06-04-2020	Antofagasta	8	0	0	68	0	0	3
06-04-2020	Atacama	0	0	0	5	0	0	0
06-04-2020	Coquimbo	1	0	0	51	0	0	1
06-04-2020	Valparaíso	7	0	0	198	1	0	22
06-04-2020	Metropolitana	142	0	0	2,244	10	0	161
06-04-2020	O’Higgins	1	0	0	42	0	0	6
06-04-2020	Maule	5	0	0	108	1	0	5
06-04-2020	Ñuble	49	1	0	474	4	0	11
06-04-2020	Biobío	18	0	0	383	2	0	27
06-04-2020	Araucanía	53	1	0	562	14	0	46
06-04-2020	Los Ríos	5	0	0	104	1	0	4
06-04-2020	Los Lagos	10	1	0	286	2	0	23
06-04-2020	Aysén	0	0	0	7	0	0	0
06-04-2020	Magallanes	31	0	0	221	2	0	12
06-04-2020	No Informado	0	0	130	0	0	728	NA
07-04-2020	Arica y Parinacota	12	0	0	53	0	0	4
07-04-2020	Tarapacá	2	0	0	23	0	0	2
07-04-2020	Antofagasta	1	1	0	69	1	0	2
07-04-2020	Atacama	1	0	0	6	0	0	0
07-04-2020	Coquimbo	1	0	0	52	0	0	2
07-04-2020	Valparaíso	16	0	0	214	1	0	21
07-04-2020	Metropolitana	106	2	0	2,350	12	0	160
07-04-2020	O’Higgins	0	0	0	42	0	0	6
07-04-2020	Maule	0	1	0	108	2	0	7
07-04-2020	Ñuble	48	1	0	522	5	0	13
07-04-2020	Biobío	27	0	0	410	2	0	32
07-04-2020	Araucanía	50	0	0	612	14	0	46
07-04-2020	Los Ríos	5	1	0	109	2	0	6
07-04-2020	Los Lagos	13	0	0	299	2	0	24
07-04-2020	Aysén	0	0	0	7	0	0	0
07-04-2020	Magallanes	19	0	0	240	2	0	12
07-04-2020	No Informado	0	0	170	0	0	898	NA
08-04-2020	Arica y Parinacota	10	0	0	63	0	0	5
08-04-2020	Tarapacá	3	0	0	26	0	0	2
08-04-2020	Antofagasta	8	0	0	77	1	0	2
08-04-2020	Atacama	2	0	0	8	0	0	1
08-04-2020	Coquimbo	4	0	0	56	0	0	1
08-04-2020	Valparaíso	11	1	0	225	2	0	20
08-04-2020	Metropolitana	198	2	0	2,548	14	0	174
08-04-2020	O’Higgins	1	0	0	43	0	0	6
08-04-2020	Maule	11	0	0	119	2	0	8
08-04-2020	Ñuble	39	0	0	561	5	0	13
08-04-2020	Biobío	29	0	0	439	2	0	31
08-04-2020	Araucanía	57	1	0	669	15	0	54
08-04-2020	Los Ríos	5	0	0	114	2	0	6
08-04-2020	Los Lagos	26	0	0	325	2	0	25
08-04-2020	Aysén	0	0	0	7	0	0	0
08-04-2020	Magallanes	26	1	0	266	3	0	14
08-04-2020	No Informado	0	0	217	0	0	1,115	NA
09-04-2020	Arica y Parinacota	12	0	0	75	0	0	5
09-04-2020	Tarapacá	3	0	0	29	0	0	2
09-04-2020	Antofagasta	15	0	0	92	1	0	2
09-04-2020	Atacama	2	0	0	10	0	0	1
09-04-2020	Coquimbo	5	0	0	61	0	0	1
09-04-2020	Valparaíso	5	0	0	230	2	0	22
09-04-2020	Metropolitana	284	7	0	2,832	21	0	164
09-04-2020	O’Higgins	1	0	0	44	0	0	6
09-04-2020	Maule	9	0	0	128	2	0	10
09-04-2020	Ñuble	10	1	0	571	6	0	13
09-04-2020	Biobío	21	0	0	460	2	0	33
09-04-2020	Araucanía	20	1	0	689	16	0	56
09-04-2020	Los Ríos	4	0	0	118	2	0	7
09-04-2020	Los Lagos	15	0	0	340	2	0	24
09-04-2020	Aysén	0	0	0	7	0	0	0
09-04-2020	Magallanes	20	0	0	286	3	0	14
09-04-2020	No Informado	0	0	159	0	0	1,274	NA
10-04-2020	Arica y Parinacota	12	0	0	87	0	0	5
10-04-2020	Tarapacá	6	0	0	35	0	0	3
10-04-2020	Antofagasta	18	0	0	110	1	0	3
10-04-2020	Atacama	3	0	0	13	0	0	1
10-04-2020	Coquimbo	0	0	0	61	0	0	2
10-04-2020	Valparaíso	11	0	0	241	2	0	21
10-04-2020	Metropolitana	361	8	0	3,193	29	0	181
10-04-2020	O’Higgins	0	0	0	44	0	0	7
10-04-2020	Maule	3	0	0	131	2	0	11
10-04-2020	Ñuble	18	0	0	589	6	0	13
10-04-2020	Biobío	14	0	0	474	2	0	32
10-04-2020	Araucanía	23	0	0	712	16	0	57
10-04-2020	Los Ríos	6	0	0	124	2	0	7
10-04-2020	Los Lagos	9	0	0	349	2	0	25
10-04-2020	Aysén	0	0	0	7	0	0	0
10-04-2020	Magallanes	45	0	0	331	3	0	15
10-04-2020	No Informado	0	0	297	0	0	1,571	NA
11-04-2020	Arica y Parinacota	20	0	0	107	0	0	5
11-04-2020	Tarapacá	3	0	0	38	0	0	3
11-04-2020	Antofagasta	7	0	0	117	1	0	2
11-04-2020	Atacama	0	0	0	13	0	0	1
11-04-2020	Coquimbo	3	0	0	64	0	0	2
11-04-2020	Valparaíso	7	0	0	248	2	0	22
11-04-2020	Metropolitana	255	3	0	3,448	32	0	178
11-04-2020	O’Higgins	1	0	0	45	0	0	7
11-04-2020	Maule	3	1	0	134	3	0	13
11-04-2020	Ñuble	17	0	0	606	6	0	13
11-04-2020	Biobío	16	0	0	490	2	0	33
11-04-2020	Araucanía	27	1	0	739	17	0	53
11-04-2020	Los Ríos	6	0	0	130	2	0	7
11-04-2020	Los Lagos	15	3	0	364	5	0	26
11-04-2020	Aysén	0	0	0	7	0	0	0
11-04-2020	Magallanes	46	0	0	377	3	0	18
11-04-2020	No Informado	0	0	293	0	0	1,864	NA
12-04-2020	Arica y Parinacota	8	1	0	115	1	0	5
12-04-2020	Tarapacá	8	0	0	46	0	0	2
12-04-2020	Antofagasta	21	0	0	138	1	0	3
12-04-2020	Atacama	0	0	0	13	0	0	1
12-04-2020	Coquimbo	2	0	0	66	0	0	1
12-04-2020	Valparaíso	6	0	0	254	2	0	18
12-04-2020	Metropolitana	151	3	0	3,599	35	0	185
12-04-2020	O’Higgins	1	0	0	46	0	0	7
12-04-2020	Maule	4	0	0	138	3	0	15
12-04-2020	Ñuble	7	1	0	613	7	0	12
12-04-2020	Biobío	10	0	0	500	2	0	31
12-04-2020	Araucanía	36	0	0	775	17	0	56
12-04-2020	Los Ríos	5	1	0	135	3	0	7
12-04-2020	Los Lagos	8	0	0	372	5	0	26
12-04-2020	Aysén	0	0	0	7	0	0	0
12-04-2020	Magallanes	19	1	0	396	4	0	18
12-04-2020	No Informado	0	0	195	0	0	2,059	NA
13-04-2020	Arica y Parinacota	0	0	0	115	1	0	5
13-04-2020	Tarapacá	6	0	0	52	0	0	2
13-04-2020	Antofagasta	11	0	0	149	1	0	4
13-04-2020	Atacama	0	0	0	13	0	0	1
13-04-2020	Coquimbo	0	0	0	66	0	0	0
13-04-2020	Valparaíso	19	0	0	273	2	0	16
13-04-2020	Metropolitana	204	1	0	3,803	36	0	189
13-04-2020	O’Higgins	2	0	0	48	0	0	7
13-04-2020	Maule	3	0	0	141	3	0	16
13-04-2020	Ñuble	5	0	0	618	7	0	13
13-04-2020	Biobío	12	0	0	512	2	0	35
13-04-2020	Araucanía	20	0	0	795	17	0	52
13-04-2020	Los Ríos	3	0	0	138	3	0	7
13-04-2020	Los Lagos	8	0	0	380	5	0	25
13-04-2020	Aysén	0	0	0	7	0	0	0
13-04-2020	Magallanes	19	1	0	415	5	0	15
13-04-2020	No Informado	0	0	308	0	0	2,367	NA
14-04-2020	Arica y Parinacota	5	0	0	120	1	0	5
14-04-2020	Tarapacá	10	0	0	62	0	0	1
14-04-2020	Antofagasta	6	0	0	155	1	0	4
14-04-2020	Atacama	0	0	0	13	0	0	1
14-04-2020	Coquimbo	0	0	0	66	0	0	1
14-04-2020	Valparaíso	12	0	0	285	2	0	16
14-04-2020	Metropolitana	283	4	0	4,086	40	0	190
14-04-2020	O’Higgins	5	0	0	53	0	0	5
14-04-2020	Maule	1	1	0	142	4	0	15
14-04-2020	Ñuble	4	1	0	622	8	0	12
14-04-2020	Biobío	16	0	0	528	2	0	35
14-04-2020	Araucanía	21	3	0	816	20	0	46
14-04-2020	Los Ríos	10	0	0	148	3	0	7
14-04-2020	Los Lagos	5	0	0	385	5	0	26
14-04-2020	Aysén	0	0	0	7	0	0	0
14-04-2020	Magallanes	14	1	0	429	6	0	15
14-04-2020	No Informado	0	0	279	0	0	2,646	NA
15-04-2020	Arica y Parinacota	4	0	0	124	1	0	5
15-04-2020	Tarapacá	0	0	0	62	0	0	0
15-04-2020	Antofagasta	21	0	0	176	1	0	5
15-04-2020	Atacama	0	0	0	13	0	0	1
15-04-2020	Coquimbo	0	0	0	66	0	0	1
15-04-2020	Valparaíso	14	0	0	299	2	0	16
15-04-2020	Metropolitana	248	1	0	4,334	41	0	198
15-04-2020	O’Higgins	1	0	0	54	0	0	6
15-04-2020	Maule	10	0	0	152	4	0	15
15-04-2020	Ñuble	12	0	0	634	8	0	13
15-04-2020	Biobío	14	0	0	542	2	0	35
15-04-2020	Araucanía	10	1	0	826	21	0	46
15-04-2020	Los Ríos	2	0	0	150	3	0	6
15-04-2020	Los Lagos	5	0	0	390	5	0	26
15-04-2020	Aysén	0	0	0	7	0	0	0
15-04-2020	Magallanes	15	0	0	444	6	0	16
15-04-2020	No Informado	0	0	291	0	0	2,937	NA
16-04-2020	Arica y Parinacota	5	0	0	129	1	0	6
16-04-2020	Tarapacá	4	0	0	66	0	0	0
16-04-2020	Antofagasta	16	0	0	192	1	0	5
16-04-2020	Atacama	0	0	0	13	0	0	1
16-04-2020	Coquimbo	2	0	0	68	0	0	0
16-04-2020	Valparaíso	31	2	0	330	4	0	18
16-04-2020	Metropolitana	348	7	0	4,682	48	0	196
16-04-2020	O’Higgins	0	0	0	54	0	0	4
16-04-2020	Maule	15	0	0	167	4	0	13
16-04-2020	Ñuble	5	2	0	639	10	0	12
16-04-2020	Biobío	17	0	0	559	2	0	30
16-04-2020	Araucanía	56	0	0	882	21	0	47
16-04-2020	Los Ríos	3	0	0	153	3	0	7
16-04-2020	Los Lagos	9	0	0	399	5	0	27
16-04-2020	Aysén	0	0	0	7	0	0	0
16-04-2020	Magallanes	23	0	0	467	6	0	18
16-04-2020	No Informado	0	0	362	0	0	3,299	NA
17-04-2020	Arica y Parinacota	5	1	0	134	2	0	6
17-04-2020	Tarapacá	7	0	0	73	0	0	0
17-04-2020	Antofagasta	19	0	0	211	1	0	6
17-04-2020	Atacama	0	0	0	13	0	0	1
17-04-2020	Coquimbo	0	0	0	68	0	0	0
17-04-2020	Valparaíso	15	0	0	345	4	0	18
17-04-2020	Metropolitana	233	3	0	4,915	51	0	202
17-04-2020	O’Higgins	1	0	0	55	0	0	4
17-04-2020	Maule	56	3	0	223	7	0	12
17-04-2020	Ñuble	17	1	0	656	11	0	12
17-04-2020	Biobío	19	1	0	578	3	0	27
17-04-2020	Araucanía	25	1	0	907	22	0	47
17-04-2020	Los Ríos	1	0	0	154	3	0	6
17-04-2020	Los Lagos	13	1	0	412	6	0	26
17-04-2020	Aysén	0	0	0	7	0	0	0
17-04-2020	Magallanes	34	0	0	501	6	0	18
17-04-2020	No Informado	0	0	322	0	0	3,621	NA
18-04-2020	Arica y Parinacota	8	0	0	142	2	0	5
18-04-2020	Tarapacá	8	0	0	81	0	0	0
18-04-2020	Antofagasta	15	0	0	226	1	0	5
18-04-2020	Atacama	0	0	0	13	0	0	1
18-04-2020	Coquimbo	0	0	0	68	0	0	0
18-04-2020	Valparaíso	14	0	0	359	4	0	19
18-04-2020	Metropolitana	277	7	0	5,192	58	0	181
18-04-2020	O’Higgins	0	0	0	55	0	0	4
18-04-2020	Maule	53	1	0	276	8	0	12
18-04-2020	Ñuble	11	1	0	667	12	0	13
18-04-2020	Biobío	28	0	0	606	3	0	25
18-04-2020	Araucanía	37	1	0	944	23	0	46
18-04-2020	Los Ríos	2	0	0	156	3	0	6
18-04-2020	Los Lagos	4	0	0	416	6	0	26
18-04-2020	Aysén	0	0	0	7	0	0	0
18-04-2020	Magallanes	21	0	0	522	6	0	17
18-04-2020	No Informado	0	0	414	0	0	4,035	NA
19-04-2020	Arica y Parinacota	3	0	0	145	2	0	5
19-04-2020	Tarapacá	9	0	0	90	0	0	1
19-04-2020	Antofagasta	23	0	0	249	1	0	5
19-04-2020	Atacama	0	0	0	13	0	0	1
19-04-2020	Coquimbo	0	0	0	68	0	0	0
19-04-2020	Valparaíso	22	1	0	381	5	0	22
19-04-2020	Metropolitana	189	5	0	5,381	63	0	186
19-04-2020	O’Higgins	0	0	0	55	0	0	4
19-04-2020	Maule	26	0	0	302	8	0	15
19-04-2020	Ñuble	11	1	0	678	13	0	14
19-04-2020	Biobío	10	0	0	616	3	0	26
19-04-2020	Araucanía	28	0	0	972	23	0	44
19-04-2020	Los Ríos	1	0	0	157	3	0	7
19-04-2020	Los Lagos	5	0	0	421	6	0	25
19-04-2020	Aysén	0	0	0	7	0	0	0
19-04-2020	Magallanes	31	0	0	553	6	0	18
19-04-2020	No Informado	0	0	303	0	0	4,338	NA
20-04-2020	Arica y Parinacota	16	0	0	161	2	0	5
20-04-2020	Tarapacá	3	0	0	93	0	0	1
20-04-2020	Antofagasta	14	0	0	263	1	0	5
20-04-2020	Atacama	0	0	0	13	0	0	1
20-04-2020	Coquimbo	1	0	0	69	0	0	0
20-04-2020	Valparaíso	7	0	0	388	5	0	22
20-04-2020	Metropolitana	262	4	0	5,643	67	0	194
20-04-2020	O’Higgins	1	0	0	56	0	0	4
20-04-2020	Maule	2	2	0	304	10	0	9
20-04-2020	Ñuble	9	0	0	687	13	0	15
20-04-2020	Biobío	10	0	0	626	3	0	26
20-04-2020	Araucanía	73	0	0	1,045	23	0	45
20-04-2020	Los Ríos	5	0	0	162	3	0	5
20-04-2020	Los Lagos	3	0	0	424	6	0	24
20-04-2020	Aysén	0	0	0	7	0	0	0
20-04-2020	Magallanes	13	0	0	566	6	0	21
20-04-2020	No Informado	0	0	338	0	0	4,676	NA
21-04-2020	Arica y Parinacota	9	0	0	170	2	0	5
21-04-2020	Tarapacá	11	0	0	104	0	0	1
21-04-2020	Antofagasta	28	0	0	291	1	0	7
21-04-2020	Atacama	0	0	0	13	0	0	1
21-04-2020	Coquimbo	0	0	0	69	0	0	0
21-04-2020	Valparaíso	15	1	0	403	6	0	22
21-04-2020	Metropolitana	145	2	0	5,788	69	0	204
21-04-2020	O’Higgins	1	1	0	57	1	0	3
21-04-2020	Maule	12	0	0	316	10	0	14
21-04-2020	Ñuble	7	0	0	694	13	0	15
21-04-2020	Biobío	10	1	0	636	4	0	25
21-04-2020	Araucanía	47	2	0	1,092	25	0	45
21-04-2020	Los Ríos	5	0	0	167	3	0	6
21-04-2020	Los Lagos	8	0	0	432	6	0	25
21-04-2020	Aysén	0	0	0	7	0	0	0
21-04-2020	Magallanes	27	1	0	593	7	0	19
21-04-2020	No Informado	0	0	293	0	0	4,969	NA
22-04-2020	Arica y Parinacota	10	0	0	180	2	0	4
22-04-2020	Tarapacá	10	0	0	114	0	0	2
22-04-2020	Antofagasta	35	1	0	326	2	0	10
22-04-2020	Atacama	0	0	0	13	0	0	1
22-04-2020	Coquimbo	1	0	0	70	0	0	0
22-04-2020	Valparaíso	18	1	0	421	7	0	24
22-04-2020	Metropolitana	295	9	0	6,083	78	0	213
22-04-2020	O’Higgins	2	0	0	59	1	0	4
22-04-2020	Maule	12	0	0	328	10	0	10
22-04-2020	Ñuble	9	0	0	703	13	0	15
22-04-2020	Biobío	22	1	0	658	5	0	25
22-04-2020	Araucanía	21	1	0	1,113	26	0	43
22-04-2020	Los Ríos	4	0	0	171	3	0	7
22-04-2020	Los Lagos	13	0	0	445	6	0	23
22-04-2020	Aysén	0	0	0	7	0	0	0
22-04-2020	Magallanes	12	0	0	605	7	0	18
22-04-2020	No Informado	0	0	417	0	0	5,386	NA
23-04-2020	Arica y Parinacota	30	0	0	210	2	0	4
23-04-2020	Tarapacá	14	0	0	128	0	0	2
23-04-2020	Antofagasta	14	0	0	340	2	0	14
23-04-2020	Atacama	5	0	0	18	0	0	1
23-04-2020	Coquimbo	0	0	0	70	0	0	0
23-04-2020	Valparaíso	8	0	0	429	7	0	20
23-04-2020	Metropolitana	351	7	0	6,434	85	0	234
23-04-2020	O’Higgins	17	0	0	76	1	0	4
23-04-2020	Maule	5	0	0	333	10	0	9
23-04-2020	Ñuble	5	0	0	708	13	0	15
23-04-2020	Biobío	17	0	0	675	5	0	25
23-04-2020	Araucanía	29	0	0	1,142	26	0	37
23-04-2020	Los Ríos	2	0	0	173	3	0	7
23-04-2020	Los Lagos	9	1	0	454	7	0	22
23-04-2020	Aysén	0	0	0	7	0	0	0
23-04-2020	Magallanes	10	0	0	615	7	0	17
23-04-2020	No Informado	0	0	418	0	0	5,804	NA
24-04-2020	Arica y Parinacota	29	1	0	239	3	0	7
24-04-2020	Tarapacá	6	0	0	134	0	0	2
24-04-2020	Antofagasta	26	0	0	366	2	0	16
24-04-2020	Atacama	3	0	0	21	0	0	1
24-04-2020	Coquimbo	2	0	0	72	0	0	0
24-04-2020	Valparaíso	7	1	0	436	8	0	17
24-04-2020	Metropolitana	327	1	0	6,761	86	0	242
24-04-2020	O’Higgins	5	0	0	81	1	0	4
24-04-2020	Maule	16	0	0	349	10	0	9
24-04-2020	Ñuble	7	1	0	715	14	0	13
24-04-2020	Biobío	8	0	0	683	5	0	22
24-04-2020	Araucanía	42	0	0	1,184	26	0	38
24-04-2020	Los Ríos	1	0	0	174	3	0	7
24-04-2020	Los Lagos	6	1	0	460	8	0	21
24-04-2020	Aysén	0	0	0	7	0	0	0
24-04-2020	Magallanes	9	1	0	624	8	0	16
24-04-2020	No Informado	0	0	523	0	0	6,327	NA
25-04-2020	Arica y Parinacota	6	0	0	245	3	0	8
25-04-2020	Tarapacá	9	1	0	143	1	0	3
25-04-2020	Antofagasta	25	1	0	391	3	0	16
25-04-2020	Atacama	3	0	0	24	0	0	1
25-04-2020	Coquimbo	1	0	0	73	0	0	0
25-04-2020	Valparaíso	18	1	0	454	9	0	16
25-04-2020	Metropolitana	404	3	0	7,165	89	0	246
25-04-2020	O’Higgins	4	0	0	85	1	0	4
25-04-2020	Maule	1	0	0	350	10	0	8
25-04-2020	Ñuble	10	0	0	725	14	0	10
25-04-2020	Biobío	13	0	0	696	5	0	22
25-04-2020	Araucanía	19	1	0	1,203	27	0	39
25-04-2020	Los Ríos	3	0	0	177	3	0	7
25-04-2020	Los Lagos	5	0	0	465	8	0	21
25-04-2020	Aysén	0	0	0	7	0	0	0
25-04-2020	Magallanes	31	0	0	655	8	0	17
25-04-2020	No Informado	0	0	419	0	0	6,746	NA
26-04-2020	Arica y Parinacota	7	0	0	252	3	0	9
26-04-2020	Tarapacá	13	0	0	156	1	0	2
26-04-2020	Antofagasta	52	1	0	443	4	0	15
26-04-2020	Atacama	5	0	0	29	0	0	1
26-04-2020	Coquimbo	0	0	0	73	0	0	0
26-04-2020	Valparaíso	6	0	0	460	9	0	14
26-04-2020	Metropolitana	331	2	0	7,496	91	0	248
26-04-2020	O’Higgins	7	0	0	92	1	0	4
26-04-2020	Maule	4	1	0	354	11	0	10
26-04-2020	Ñuble	6	0	0	731	14	0	10
26-04-2020	Biobío	7	1	0	703	6	0	23
26-04-2020	Araucanía	13	2	0	1,216	29	0	38
26-04-2020	Los Ríos	1	0	0	178	3	0	7
26-04-2020	Los Lagos	8	0	0	473	8	0	17
26-04-2020	Aysén	0	0	0	7	0	0	0
26-04-2020	Magallanes	13	1	0	668	9	0	17
26-04-2020	No Informado	0	0	278	0	0	7,024	NA
27-04-2020	Arica y Parinacota	13	0	0	265	3	0	10
27-04-2020	Tarapacá	8	0	0	164	1	0	2
27-04-2020	Antofagasta	14	0	0	457	4	0	17
27-04-2020	Atacama	6	0	0	35	0	0	1
27-04-2020	Coquimbo	1	0	0	74	0	0	0
27-04-2020	Valparaíso	25	0	0	485	9	0	15
27-04-2020	Metropolitana	362	4	0	7,858	95	0	252
27-04-2020	O’Higgins	2	0	0	94	1	0	4
27-04-2020	Maule	9	1	0	363	12	0	10
27-04-2020	Ñuble	10	0	0	741	14	0	10
27-04-2020	Biobío	3	0	0	706	6	0	24
27-04-2020	Araucanía	20	3	0	1,236	32	0	37
27-04-2020	Los Ríos	2	0	0	180	3	0	6
27-04-2020	Los Lagos	4	0	0	477	8	0	17
27-04-2020	Aysén	0	0	0	7	0	0	0
27-04-2020	Magallanes	3	1	0	671	10	0	21
27-04-2020	No Informado	0	0	303	0	0	7,327	NA
28-04-2020	Arica y Parinacota	3	0	0	268	3	0	10
28-04-2020	Tarapacá	5	0	0	169	1	0	2
28-04-2020	Antofagasta	24	0	0	481	4	0	19
28-04-2020	Atacama	0	0	0	35	0	0	1
28-04-2020	Coquimbo	0	0	0	74	0	0	0
28-04-2020	Valparaíso	8	1	0	493	10	0	15
28-04-2020	Metropolitana	442	5	0	8,300	100	0	258
28-04-2020	O’Higgins	3	0	0	97	1	0	5
28-04-2020	Maule	21	0	0	384	12	0	8
28-04-2020	Ñuble	6	0	0	747	14	0	9
28-04-2020	Biobío	3	1	0	709	7	0	21
28-04-2020	Araucanía	15	0	0	1,251	32	0	35
28-04-2020	Los Ríos	0	1	0	180	4	0	5
28-04-2020	Los Lagos	0	1	0	477	9	0	18
28-04-2020	Aysén	0	0	0	7	0	0	0
28-04-2020	Magallanes	22	0	0	693	10	0	22
28-04-2020	No Informado	0	0	383	0	0	7,710	NA
29-04-2020	Arica y Parinacota	4	1	0	272	4	0	9
29-04-2020	Tarapacá	14	0	0	183	1	0	3
29-04-2020	Antofagasta	33	0	0	514	4	0	20
29-04-2020	Atacama	7	0	0	42	0	0	1
29-04-2020	Coquimbo	2	0	0	76	0	0	0
29-04-2020	Valparaíso	33	2	0	526	12	0	14
29-04-2020	Metropolitana	589	4	0	8,889	104	0	256
29-04-2020	O’Higgins	7	0	0	104	1	0	4
29-04-2020	Maule	8	1	0	392	13	0	7
29-04-2020	Ñuble	14	0	0	761	14	0	7
29-04-2020	Biobío	10	0	0	719	7	0	22
29-04-2020	Araucanía	22	0	0	1,273	32	0	35
29-04-2020	Los Ríos	2	0	0	182	4	0	5
29-04-2020	Los Lagos	4	0	0	481	9	0	18
29-04-2020	Aysén	0	0	0	7	0	0	0
29-04-2020	Magallanes	21	1	0	714	11	0	17
29-04-2020	No Informado	0	0	347	0	0	8,057	NA
30-04-2020	Arica y Parinacota	7	0	0	279	4	0	8
30-04-2020	Tarapacá	15	0	0	198	1	0	2
30-04-2020	Antofagasta	32	1	0	546	5	0	21
30-04-2020	Atacama	1	0	0	43	0	0	1
30-04-2020	Coquimbo	0	0	0	76	0	0	0
30-04-2020	Valparaíso	33	0	0	559	12	0	18
30-04-2020	Metropolitana	736	6	0	9,625	110	0	257
30-04-2020	O’Higgins	4	0	0	108	1	0	5
30-04-2020	Maule	1	0	0	393	13	0	6
30-04-2020	Ñuble	7	0	0	768	14	0	7
30-04-2020	Biobío	8	0	0	727	7	0	22
30-04-2020	Araucanía	20	3	0	1,293	35	0	30
30-04-2020	Los Ríos	7	0	0	189	4	0	5
30-04-2020	Los Lagos	6	1	0	487	10	0	19
30-04-2020	Aysén	0	0	0	7	0	0	0
30-04-2020	Magallanes	11	0	0	725	11	0	18
30-04-2020	No Informado	0	0	523	0	0	8,580	NA
01-05-2020	Arica y Parinacota	18	0	0	297	4	0	9
01-05-2020	Tarapacá	2	0	0	200	1	0	2
01-05-2020	Antofagasta	43	2	0	589	7	0	23
01-05-2020	Atacama	0	0	0	43	0	0	1
01-05-2020	Coquimbo	1	0	0	77	0	0	0
01-05-2020	Valparaíso	9	0	0	568	12	0	19
01-05-2020	Metropolitana	891	4	0	10,516	114	0	262
01-05-2020	O’Higgins	1	0	0	109	1	0	6
01-05-2020	Maule	1	0	0	394	13	0	5
01-05-2020	Ñuble	0	0	0	768	14	0	8
01-05-2020	Biobío	2	0	0	729	7	0	23
01-05-2020	Araucanía	2	1	0	1,295	36	0	29
01-05-2020	Los Ríos	0	0	0	189	4	0	5
01-05-2020	Los Lagos	14	0	0	501	10	0	18
01-05-2020	Aysén	0	0	0	7	0	0	0
01-05-2020	Magallanes	1	0	0	726	11	0	18
01-05-2020	No Informado	0	0	438	0	0	9,018	NA
02-05-2020	Arica y Parinacota	10	0	0	307	4	0	7
02-05-2020	Tarapacá	12	0	0	212	1	0	2
02-05-2020	Antofagasta	90	0	0	679	7	0	22
02-05-2020	Atacama	16	0	0	59	0	0	1
02-05-2020	Coquimbo	1	0	0	78	0	0	0
02-05-2020	Valparaíso	46	1	0	614	13	0	18
02-05-2020	Metropolitana	1,145	8	0	11,661	122	0	269
02-05-2020	O’Higgins	11	2	0	120	3	0	5
02-05-2020	Maule	2	0	0	396	13	0	5
02-05-2020	Ñuble	8	2	0	776	16	0	8
02-05-2020	Biobío	22	0	0	751	7	0	21
02-05-2020	Araucanía	19	0	0	1,314	36	0	28
02-05-2020	Los Ríos	7	0	0	196	4	0	4
02-05-2020	Los Lagos	12	0	0	513	10	0	17
02-05-2020	Aysén	0	0	0	7	0	0	0
02-05-2020	Magallanes	26	0	0	752	11	0	18
02-05-2020	No Informado	0	0	554	0	0	9,572	NA
03-05-2020	Arica y Parinacota	1	0	0	308	4	0	7
03-05-2020	Tarapacá	60	0	0	272	1	0	4
03-05-2020	Antofagasta	61	0	0	740	7	0	24
03-05-2020	Atacama	8	0	0	67	0	0	1
03-05-2020	Coquimbo	10	0	0	88	0	0	0
03-05-2020	Valparaíso	24	0	0	638	13	0	17
03-05-2020	Metropolitana	995	13	0	12,656	135	0	289
03-05-2020	O’Higgins	5	0	0	125	3	0	5
03-05-2020	Maule	1	0	0	397	13	0	8
03-05-2020	Ñuble	10	0	0	786	16	0	9
03-05-2020	Biobío	8	0	0	759	7	0	20
03-05-2020	Araucanía	14	0	0	1,328	36	0	27
03-05-2020	Los Ríos	2	0	0	198	4	0	3
03-05-2020	Los Lagos	4	0	0	517	10	0	17
03-05-2020	Aysén	0	0	0	7	0	0	0
03-05-2020	Magallanes	25	0	0	777	11	0	18
03-05-2020	No Informado	0	0	469	0	0	10,041	NA
04-05-2020	Arica y Parinacota	0	0	0	308	4	0	7
04-05-2020	Tarapacá	23	0	0	295	1	0	4
04-05-2020	Antofagasta	20	0	0	760	7	0	25
04-05-2020	Atacama	3	0	0	70	0	0	1
04-05-2020	Coquimbo	3	0	0	91	0	0	0
04-05-2020	Valparaíso	18	0	0	656	13	0	18
04-05-2020	Metropolitana	872	8	0	13,528	143	0	303
04-05-2020	O’Higgins	6	0	0	131	3	0	5
04-05-2020	Maule	2	0	0	399	13	0	8
04-05-2020	Ñuble	1	1	0	787	17	0	9
04-05-2020	Biobío	3	1	0	762	8	0	18
04-05-2020	Araucanía	3	0	0	1,331	36	0	26
04-05-2020	Los Ríos	1	0	0	199	4	0	3
04-05-2020	Los Lagos	7	0	0	524	10	0	18
04-05-2020	Aysén	0	0	0	7	0	0	0
04-05-2020	Magallanes	18	0	0	795	11	0	19
04-05-2020	No Informado	0	0	374	0	0	10,415	NA
05-05-2020	Arica y Parinacota	7	0	0	315	4	0	6
05-05-2020	Tarapacá	27	0	0	322	1	0	5
05-05-2020	Antofagasta	51	0	0	811	7	0	24
05-05-2020	Atacama	8	0	0	78	0	0	1
05-05-2020	Coquimbo	6	0	0	97	0	0	0
05-05-2020	Valparaíso	31	2	0	687	15	0	17
05-05-2020	Metropolitana	1,179	3	0	14,707	146	0	314
05-05-2020	O’Higgins	5	0	0	136	3	0	7
05-05-2020	Maule	9	0	0	408	13	0	7
05-05-2020	Ñuble	2	0	0	789	17	0	7
05-05-2020	Biobío	12	0	0	774	8	0	18
05-05-2020	Araucanía	21	0	0	1,352	36	0	25
05-05-2020	Los Ríos	0	0	0	199	4	0	4
05-05-2020	Los Lagos	1	0	0	525	10	0	16
05-05-2020	Aysén	0	0	0	7	0	0	0
05-05-2020	Magallanes	14	0	0	809	11	0	19
05-05-2020	No Informado	0	0	295	0	0	10,710	NA
06-05-2020	Arica y Parinacota	3	1	0	318	5	0	7
06-05-2020	Tarapacá	17	0	0	339	1	0	5
06-05-2020	Antofagasta	44	0	0	855	7	0	28
06-05-2020	Atacama	2	0	0	80	0	0	1
06-05-2020	Coquimbo	0	0	0	97	0	0	0
06-05-2020	Valparaíso	29	1	0	716	16	0	16
06-05-2020	Metropolitana	875	4	0	15,582	150	0	340
06-05-2020	O’Higgins	9	0	0	145	3	0	5
06-05-2020	Maule	0	0	0	408	13	0	4
06-05-2020	Ñuble	2	0	0	791	17	0	7
06-05-2020	Biobío	15	0	0	789	8	0	15
06-05-2020	Araucanía	10	0	0	1,362	36	0	24
06-05-2020	Los Ríos	1	0	0	200	4	0	4
06-05-2020	Los Lagos	16	0	0	541	10	0	11
06-05-2020	Aysén	0	0	0	7	0	0	0
06-05-2020	Magallanes	9	0	0	818	11	0	19
06-05-2020	No Informado	0	0	479	0	0	11,189	NA
07-05-2020	Arica y Parinacota	7	1	0	325	6	0	8
07-05-2020	Tarapacá	78	0	0	417	1	0	5
07-05-2020	Antofagasta	40	0	0	895	7	0	28
07-05-2020	Atacama	7	0	0	87	0	0	1
07-05-2020	Coquimbo	3	0	0	100	0	0	0
07-05-2020	Valparaíso	65	1	0	781	17	0	15
07-05-2020	Metropolitana	1,246	2	0	16,828	152	0	348
07-05-2020	O’Higgins	6	0	0	151	3	0	6
07-05-2020	Maule	6	0	0	414	13	0	4
07-05-2020	Ñuble	2	0	0	793	17	0	7
07-05-2020	Biobío	25	0	0	814	8	0	14
07-05-2020	Araucanía	23	0	0	1,385	36	0	24
07-05-2020	Los Ríos	2	0	0	202	4	0	4
07-05-2020	Los Lagos	4	0	0	545	10	0	10
07-05-2020	Aysén	1	0	0	8	0	0	0
07-05-2020	Magallanes	18	0	0	836	11	0	19
07-05-2020	No Informado	0	0	475	0	0	11,664	NA
08-05-2020	Arica y Parinacota	2	0	0	327	6	0	7
08-05-2020	Tarapacá	42	0	0	459	1	0	4
08-05-2020	Antofagasta	28	2	0	923	9	0	29
08-05-2020	Atacama	1	0	0	88	0	0	1
08-05-2020	Coquimbo	11	0	0	111	0	0	0
08-05-2020	Valparaíso	47	1	0	828	18	0	18
08-05-2020	Metropolitana	1,151	5	0	17,979	157	0	366
08-05-2020	O’Higgins	16	0	0	167	3	0	6
08-05-2020	Maule	11	0	0	425	13	0	4
08-05-2020	Ñuble	1	1	0	794	18	0	7
08-05-2020	Biobío	28	0	0	842	8	0	16
08-05-2020	Araucanía	18	0	0	1,403	36	0	21
08-05-2020	Los Ríos	1	0	0	203	4	0	4
08-05-2020	Los Lagos	3	0	0	548	10	0	10
08-05-2020	Aysén	0	0	0	8	0	0	0
08-05-2020	Magallanes	31	0	0	867	11	0	15
08-05-2020	No Informado	0	0	496	0	0	12,160	NA
09-05-2020	Arica y Parinacota	10	0	0	337	6	0	7
09-05-2020	Tarapacá	26	0	0	485	1	0	6
09-05-2020	Antofagasta	24	1	0	947	10	0	30
09-05-2020	Atacama	29	0	0	117	0	0	2
09-05-2020	Coquimbo	18	0	0	129	0	0	0
09-05-2020	Valparaíso	50	1	0	878	19	0	19
09-05-2020	Metropolitana	978	6	0	18,957	163	0	394
09-05-2020	O’Higgins	15	0	0	182	3	0	5
09-05-2020	Maule	7	1	0	432	14	0	4
09-05-2020	Ñuble	7	0	0	801	18	0	8
09-05-2020	Biobío	40	0	0	882	8	0	18
09-05-2020	Araucanía	15	0	0	1,418	36	0	22
09-05-2020	Los Ríos	3	1	0	206	5	0	3
09-05-2020	Los Lagos	11	0	0	559	10	0	11
09-05-2020	Aysén	0	0	0	8	0	0	0
09-05-2020	Magallanes	14	0	0	881	11	0	15
09-05-2020	No Informado	0	0	507	0	0	12,667	NA
10-05-2020	Arica y Parinacota	3	0	0	340	6	0	7
10-05-2020	Tarapacá	35	0	0	520	1	0	6
10-05-2020	Antofagasta	31	0	0	978	10	0	31
10-05-2020	Atacama	7	0	0	124	0	0	2
10-05-2020	Coquimbo	6	1	0	135	1	0	0
10-05-2020	Valparaíso	55	1	0	933	20	0	18
10-05-2020	Metropolitana	1,396	3	0	20,353	166	0	415
10-05-2020	O’Higgins	13	0	0	195	3	0	5
10-05-2020	Maule	9	0	0	441	14	0	4
10-05-2020	Ñuble	11	0	0	812	18	0	8
10-05-2020	Biobío	17	0	0	899	8	0	19
10-05-2020	Araucanía	36	1	0	1,454	37	0	23
10-05-2020	Los Ríos	0	0	0	206	5	0	2
10-05-2020	Los Lagos	16	0	0	575	10	0	10
10-05-2020	Aysén	0	0	0	8	0	0	0
10-05-2020	Magallanes	12	2	0	893	13	0	15
10-05-2020	No Informado	0	0	445	0	0	13,112	NA
11-05-2020	Arica y Parinacota	11	1	0	351	7	0	7
11-05-2020	Tarapacá	27	0	0	547	1	0	7
11-05-2020	Antofagasta	36	0	0	1,014	10	0	32
11-05-2020	Atacama	9	0	0	133	0	0	2
11-05-2020	Coquimbo	5	0	0	140	1	0	0
11-05-2020	Valparaíso	37	1	0	970	21	0	19
11-05-2020	Metropolitana	964	6	0	21,317	172	0	421
11-05-2020	O’Higgins	21	1	0	216	4	0	5
11-05-2020	Maule	13	0	0	454	14	0	5
11-05-2020	Ñuble	2	0	0	814	18	0	8
11-05-2020	Biobío	33	0	0	932	8	0	18
11-05-2020	Araucanía	12	1	0	1,466	38	0	22
11-05-2020	Los Ríos	2	0	0	208	5	0	2
11-05-2020	Los Lagos	17	0	0	592	10	0	10
11-05-2020	Aysén	0	0	0	8	0	0	0
11-05-2020	Magallanes	8	1	0	901	14	0	16
11-05-2020	No Informado	0	0	493	0	0	13,605	NA
12-05-2020	Arica y Parinacota	0	0	0	351	7	0	7
12-05-2020	Tarapacá	31	0	0	578	1	0	9
12-05-2020	Antofagasta	95	0	0	1,109	10	0	32
12-05-2020	Atacama	1	0	0	134	0	0	2
12-05-2020	Coquimbo	8	0	0	148	1	0	0
12-05-2020	Valparaíso	59	2	0	1,029	23	0	21
12-05-2020	Metropolitana	1,392	6	0	22,709	178	0	450
12-05-2020	O’Higgins	8	0	0	224	4	0	6
12-05-2020	Maule	12	0	0	466	14	0	6
12-05-2020	Ñuble	12	1	0	826	19	0	7
12-05-2020	Biobío	10	1	0	942	9	0	17
12-05-2020	Araucanía	16	1	0	1,482	39	0	20
12-05-2020	Los Ríos	3	0	0	211	5	0	1
12-05-2020	Los Lagos	2	1	0	594	11	0	10
12-05-2020	Aysén	0	0	0	8	0	0	0
12-05-2020	Magallanes	9	0	0	910	14	0	16
12-05-2020	No Informado	0	0	520	0	0	14,125	NA
13-05-2020	Arica y Parinacota	8	0	0	359	7	0	7
13-05-2020	Tarapacá	37	0	0	615	1	0	8
13-05-2020	Antofagasta	107	1	0	1,216	11	0	38
13-05-2020	Atacama	5	0	0	139	0	0	2
13-05-2020	Coquimbo	5	0	0	153	1	0	0
13-05-2020	Valparaíso	94	1	0	1,123	24	0	24
13-05-2020	Metropolitana	2,256	6	0	24,965	184	0	490
13-05-2020	O’Higgins	24	0	0	248	4	0	7
13-05-2020	Maule	19	0	0	485	14	0	6
13-05-2020	Ñuble	13	0	0	839	19	0	5
13-05-2020	Biobío	33	0	0	975	9	0	14
13-05-2020	Araucanía	13	0	0	1,495	39	0	18
13-05-2020	Los Ríos	1	1	0	212	6	0	0
13-05-2020	Los Lagos	34	0	0	628	11	0	11
13-05-2020	Aysén	0	0	0	8	0	0	0
13-05-2020	Magallanes	11	2	0	921	16	0	12
13-05-2020	No Informado	0	0	740	0	0	14,865	NA
14-05-2020	Arica y Parinacota	4	0	0	363	7	0	7
14-05-2020	Tarapacá	67	0	0	682	1	0	9
14-05-2020	Antofagasta	61	0	0	1,277	11	0	39
14-05-2020	Atacama	1	0	0	140	0	0	2
14-05-2020	Coquimbo	12	0	0	165	1	0	0
14-05-2020	Valparaíso	90	1	0	1,213	25	0	25
14-05-2020	Metropolitana	2,251	18	0	27,216	202	0	509
14-05-2020	O’Higgins	47	0	0	295	4	0	8
14-05-2020	Maule	34	0	0	519	14	0	6
14-05-2020	Ñuble	10	1	0	849	20	0	5
14-05-2020	Biobío	20	0	0	995	9	0	11
14-05-2020	Araucanía	41	2	0	1,536	41	0	17
14-05-2020	Los Ríos	2	0	0	214	6	0	0
14-05-2020	Los Lagos	5	0	0	633	11	0	13
14-05-2020	Aysén	0	0	0	8	0	0	0
14-05-2020	Magallanes	14	0	0	935	16	0	12
14-05-2020	No Informado	0	0	790	0	0	15,655	NA
15-05-2020	Arica y Parinacota	5	0	0	368	7	0	7
15-05-2020	Tarapacá	97	1	0	779	2	0	13
15-05-2020	Antofagasta	54	1	0	1,331	12	0	41
15-05-2020	Atacama	3	0	0	143	0	0	3
15-05-2020	Coquimbo	15	0	0	180	1	0	2
15-05-2020	Valparaíso	99	2	0	1,312	27	0	25
15-05-2020	Metropolitana	2,060	19	0	29,276	221	0	551
15-05-2020	O’Higgins	28	1	0	323	5	0	7
15-05-2020	Maule	21	0	0	540	14	0	6
15-05-2020	Ñuble	36	0	0	885	20	0	5
15-05-2020	Biobío	41	0	0	1,036	9	0	12
15-05-2020	Araucanía	28	2	0	1,564	43	0	16
15-05-2020	Los Ríos	5	0	0	219	6	0	0
15-05-2020	Los Lagos	8	0	0	641	11	0	11
15-05-2020	Aysén	0	0	0	8	0	0	0
15-05-2020	Magallanes	2	0	0	937	16	0	12
15-05-2020	No Informado	0	0	959	0	0	16,614	NA
16-05-2020	Arica y Parinacota	9	0	0	377	7	0	8
16-05-2020	Tarapacá	66	0	0	845	2	0	13
16-05-2020	Antofagasta	98	0	0	1,429	12	0	45
16-05-2020	Atacama	3	0	0	146	0	0	3
16-05-2020	Coquimbo	7	1	0	187	2	0	1
16-05-2020	Valparaíso	53	1	0	1,365	28	0	26
16-05-2020	Metropolitana	1,518	25	0	30,794	246	0	581
16-05-2020	O’Higgins	6	0	0	329	5	0	10
16-05-2020	Maule	11	0	0	551	14	0	8
16-05-2020	Ñuble	36	0	0	921	20	0	5
16-05-2020	Biobío	38	0	0	1,074	9	0	14
16-05-2020	Araucanía	23	0	0	1,587	43	0	16
16-05-2020	Los Ríos	0	0	0	219	6	0	0
16-05-2020	Los Lagos	3	0	0	644	11	0	10
16-05-2020	Aysén	0	0	0	8	0	0	0
16-05-2020	Magallanes	15	0	0	952	16	0	11
16-05-2020	No Informado	0	0	1,400	0	0	18,014	NA
17-05-2020	Arica y Parinacota	1	0	0	378	7	0	8
17-05-2020	Tarapacá	130	2	0	975	4	0	12
17-05-2020	Antofagasta	76	1	0	1,505	13	0	49
17-05-2020	Atacama	3	0	0	149	0	0	3
17-05-2020	Coquimbo	7	0	0	194	2	0	1
17-05-2020	Valparaíso	109	1	0	1,474	29	0	27
17-05-2020	Metropolitana	1,890	24	0	32,684	270	0	595
17-05-2020	O’Higgins	37	0	0	366	5	0	10
17-05-2020	Maule	22	0	0	573	14	0	8
17-05-2020	Ñuble	21	0	0	942	20	0	5
17-05-2020	Biobío	25	0	0	1,099	9	0	14
17-05-2020	Araucanía	19	1	0	1,606	44	0	17
17-05-2020	Los Ríos	1	0	0	220	6	0	0
17-05-2020	Los Lagos	3	0	0	647	11	0	9
17-05-2020	Aysén	0	0	0	8	0	0	0
17-05-2020	Magallanes	9	0	0	961	16	0	11
17-05-2020	No Informado	0	0	1,199	0	0	19,213	NA
18-05-2020	Arica y Parinacota	20	0	0	398	7	0	8
18-05-2020	Tarapacá	81	0	0	1,056	4	0	16
18-05-2020	Antofagasta	84	1	0	1,589	14	0	47
18-05-2020	Atacama	8	0	0	157	0	0	3
18-05-2020	Coquimbo	18	0	0	212	2	0	1
18-05-2020	Valparaíso	79	1	0	1,553	30	0	32
18-05-2020	Metropolitana	1,767	24	0	34,451	294	0	620
18-05-2020	O’Higgins	52	1	0	418	6	0	9
18-05-2020	Maule	29	0	0	602	14	0	8
18-05-2020	Ñuble	43	1	0	985	21	0	5
18-05-2020	Biobío	45	0	0	1,144	9	0	17
18-05-2020	Araucanía	33	0	0	1,639	44	0	18
18-05-2020	Los Ríos	4	0	0	224	6	0	0
18-05-2020	Los Lagos	5	0	0	652	11	0	11
18-05-2020	Aysén	0	0	0	8	0	0	0
18-05-2020	Magallanes	10	0	0	971	16	0	12
18-05-2020	No Informado	0	0	952	0	0	20,165	NA
19-05-2020	Arica y Parinacota	24	0	0	422	7	0	8
19-05-2020	Tarapacá	53	0	0	1,109	4	0	18
19-05-2020	Antofagasta	59	2	0	1,648	16	0	50
19-05-2020	Atacama	1	0	0	158	0	0	3
19-05-2020	Coquimbo	27	0	0	239	2	0	1
19-05-2020	Valparaíso	86	2	0	1,639	32	0	34
19-05-2020	Metropolitana	3,140	24	0	37,591	318	0	682
19-05-2020	O’Higgins	24	1	0	442	7	0	9
19-05-2020	Maule	38	0	0	640	14	0	8
19-05-2020	Ñuble	3	0	0	988	21	0	7
19-05-2020	Biobío	28	0	0	1,172	9	0	18
19-05-2020	Araucanía	27	0	0	1,666	44	0	18
19-05-2020	Los Ríos	5	0	0	229	6	0	0
19-05-2020	Los Lagos	4	1	0	656	12	0	10
19-05-2020	Aysén	0	0	0	8	0	0	0
19-05-2020	Magallanes	1	1	0	972	17	0	10
19-05-2020	No Informado	0	0	1,342	0	0	21,507	NA
20-05-2020	Arica y Parinacota	2	0	0	424	7	0	7
20-05-2020	Tarapacá	30	0	0	1,139	4	0	18
20-05-2020	Antofagasta	83	1	0	1,731	17	0	50
20-05-2020	Atacama	5	0	0	163	0	0	3
20-05-2020	Coquimbo	24	0	0	263	2	0	0
20-05-2020	Valparaíso	131	3	0	1,770	35	0	38
20-05-2020	Metropolitana	3,588	30	0	41,179	348	0	705
20-05-2020	O’Higgins	13	0	0	455	7	0	14
20-05-2020	Maule	40	0	0	680	14	0	8
20-05-2020	Ñuble	23	0	0	1,011	21	0	7
20-05-2020	Biobío	52	0	0	1,224	9	0	19
20-05-2020	Araucanía	14	0	0	1,680	44	0	18
20-05-2020	Los Ríos	18	0	0	247	6	0	0
20-05-2020	Los Lagos	10	0	0	666	12	0	8
20-05-2020	Aysén	0	0	0	8	0	0	0
20-05-2020	Magallanes	5	1	0	977	18	0	9
20-05-2020	No Informado	0	0	997	0	0	22,504	NA
21-05-2020	Arica y Parinacota	29	0	0	453	7	0	7
21-05-2020	Tarapacá	107	0	0	1,246	4	0	21
21-05-2020	Antofagasta	47	0	0	1,778	17	0	50
21-05-2020	Atacama	5	0	0	168	0	0	3
21-05-2020	Coquimbo	5	0	0	268	2	0	1
21-05-2020	Valparaíso	117	3	0	1,887	38	0	38
21-05-2020	Metropolitana	3,462	40	0	44,641	388	0	730
21-05-2020	O’Higgins	10	0	0	465	7	0	18
21-05-2020	Maule	51	1	0	731	15	0	10
21-05-2020	Ñuble	9	0	0	1,020	21	0	8
21-05-2020	Biobío	39	0	0	1,263	9	0	23
21-05-2020	Araucanía	35	0	0	1,715	44	0	17
21-05-2020	Los Ríos	11	0	0	258	6	0	0
21-05-2020	Los Lagos	27	1	0	693	13	0	8
21-05-2020	Aysén	0	0	0	8	0	0	0
21-05-2020	Magallanes	10	0	0	987	18	0	9
21-05-2020	No Informado	0	0	1,488	0	0	23,992	NA
22-05-2020	Arica y Parinacota	18	0	0	471	7	0	8
22-05-2020	Tarapacá	147	1	0	1,393	5	0	18
22-05-2020	Antofagasta	63	0	0	1,841	17	0	44
22-05-2020	Atacama	7	0	0	175	0	0	3
22-05-2020	Coquimbo	13	0	0	281	2	0	3
22-05-2020	Valparaíso	133	2	0	2,020	40	0	37
22-05-2020	Metropolitana	3,709	35	0	48,350	423	0	771
22-05-2020	O’Higgins	27	1	0	492	8	0	22
22-05-2020	Maule	51	0	0	782	15	0	16
22-05-2020	Ñuble	5	0	0	1,025	21	0	9
22-05-2020	Biobío	48	1	0	1,311	10	0	21
22-05-2020	Araucanía	22	1	0	1,737	45	0	18
22-05-2020	Los Ríos	4	0	0	262	6	0	0
22-05-2020	Los Lagos	24	0	0	717	13	0	9
22-05-2020	Aysén	0	0	0	8	0	0	0
22-05-2020	Magallanes	5	0	0	992	18	0	7
22-05-2020	No Informado	0	0	1,350	0	0	25,342	NA
23-05-2020	Arica y Parinacota	11	0	0	482	7	0	9
23-05-2020	Tarapacá	78	0	0	1,471	5	0	17
23-05-2020	Antofagasta	56	4	0	1,897	21	0	44
23-05-2020	Atacama	7	0	0	182	0	0	3
23-05-2020	Coquimbo	13	0	0	294	2	0	3
23-05-2020	Valparaíso	96	4	0	2,116	44	0	46
23-05-2020	Metropolitana	3,049	32	0	51,399	455	0	832
23-05-2020	O’Higgins	26	3	0	518	11	0	24
23-05-2020	Maule	41	0	0	823	15	0	15
23-05-2020	Ñuble	52	0	0	1,077	21	0	10
23-05-2020	Biobío	43	0	0	1,354	10	0	23
23-05-2020	Araucanía	37	0	0	1,774	45	0	19
23-05-2020	Los Ríos	12	0	0	274	6	0	0
23-05-2020	Los Lagos	10	0	0	727	13	0	10
23-05-2020	Aysén	0	0	0	8	0	0	0
23-05-2020	Magallanes	5	0	0	997	18	0	7
23-05-2020	No Informado	0	0	1,204	0	0	26,546	NA
24-05-2020	Arica y Parinacota	16	0	0	498	7	0	8
24-05-2020	Tarapacá	93	2	0	1,564	7	0	17
24-05-2020	Antofagasta	50	0	0	1,947	21	0	46
24-05-2020	Atacama	0	0	0	182	0	0	3
24-05-2020	Coquimbo	29	0	0	323	2	0	4
24-05-2020	Valparaíso	114	4	0	2,230	48	0	50
24-05-2020	Metropolitana	3,145	39	0	54,544	494	0	851
24-05-2020	O’Higgins	36	0	0	554	11	0	26
24-05-2020	Maule	69	0	0	892	15	0	18
24-05-2020	Ñuble	25	0	0	1,102	21	0	9
24-05-2020	Biobío	60	0	0	1,414	10	0	22
24-05-2020	Araucanía	34	0	0	1,808	45	0	19
24-05-2020	Los Ríos	16	0	0	290	6	0	0
24-05-2020	Los Lagos	9	0	0	736	13	0	10
24-05-2020	Aysén	0	0	0	8	0	0	0
24-05-2020	Magallanes	13	0	0	1,010	18	0	7
24-05-2020	No Informado	0	0	1,602	0	0	28,148	NA
25-05-2020	Arica y Parinacota	15	0	0	513	7	0	8
25-05-2020	Tarapacá	66	0	0	1,630	7	0	15
25-05-2020	Antofagasta	68	0	0	2,015	21	0	47
25-05-2020	Atacama	3	0	0	185	0	0	2
25-05-2020	Coquimbo	15	0	0	338	2	0	6
25-05-2020	Valparaíso	75	5	0	2,305	53	0	54
25-05-2020	Metropolitana	4,386	35	0	58,930	529	0	884
25-05-2020	O’Higgins	34	2	0	588	13	0	26
25-05-2020	Maule	61	0	0	953	15	0	22
25-05-2020	Ñuble	37	1	0	1,139	22	0	12
25-05-2020	Biobío	44	0	0	1,458	10	0	23
25-05-2020	Araucanía	49	0	0	1,857	45	0	18
25-05-2020	Los Ríos	7	0	0	297	6	0	1
25-05-2020	Los Lagos	27	0	0	763	13	0	10
25-05-2020	Aysén	2	0	0	10	0	0	0
25-05-2020	Magallanes	6	0	0	1,016	18	0	7
25-05-2020	No Informado	0	0	1,154	0	0	29,302	NA
26-05-2020	Arica y Parinacota	9	0	0	522	7	0	8
26-05-2020	Tarapacá	107	1	0	1,737	8	0	16
26-05-2020	Antofagasta	48	2	0	2,063	23	0	49
26-05-2020	Atacama	3	0	0	188	0	0	2
26-05-2020	Coquimbo	46	0	0	384	2	0	6
26-05-2020	Valparaíso	150	4	0	2,455	57	0	66
26-05-2020	Metropolitana	3,355	38	0	62,285	567	0	927
26-05-2020	O’Higgins	66	0	0	654	13	0	28
26-05-2020	Maule	60	0	0	1,013	15	0	25
26-05-2020	Ñuble	8	0	0	1,147	22	0	12
26-05-2020	Biobío	44	0	0	1,502	10	0	26
26-05-2020	Araucanía	26	0	0	1,883	45	0	19
26-05-2020	Los Ríos	7	0	0	304	6	0	1
26-05-2020	Los Lagos	18	0	0	781	13	0	10
26-05-2020	Aysén	2	0	0	12	0	0	0
26-05-2020	Magallanes	15	0	0	1,031	18	0	7
26-05-2020	No Informado	0	0	1,613	0	0	30,915	NA
27-05-2020	Arica y Parinacota	19	0	0	541	7	0	9
27-05-2020	Tarapacá	74	3	0	1,811	11	0	16
27-05-2020	Antofagasta	74	2	0	2,137	25	0	48
27-05-2020	Atacama	9	0	0	197	0	0	2
27-05-2020	Coquimbo	71	0	0	455	2	0	9
27-05-2020	Valparaíso	121	1	0	2,576	58	0	74
27-05-2020	Metropolitana	3,726	28	0	66,011	595	0	959
27-05-2020	O’Higgins	37	1	0	691	14	0	30
27-05-2020	Maule	53	0	0	1,066	15	0	29
27-05-2020	Ñuble	27	0	0	1,174	22	0	12
27-05-2020	Biobío	63	0	0	1,565	10	0	26
27-05-2020	Araucanía	33	0	0	1,916	45	0	18
27-05-2020	Los Ríos	5	0	0	309	6	0	1
27-05-2020	Los Lagos	10	0	0	791	13	0	10
27-05-2020	Aysén	0	0	0	12	0	0	0
27-05-2020	Magallanes	6	0	0	1,037	18	0	8
27-05-2020	No Informado	0	0	2,625	0	0	33,540	NA
28-05-2020	Arica y Parinacota	21	0	0	562	7	0	9
28-05-2020	Tarapacá	146	1	0	1,957	12	0	23
28-05-2020	Antofagasta	111	3	0	2,248	28	0	48
28-05-2020	Atacama	5	0	0	202	0	0	2
28-05-2020	Coquimbo	27	0	0	482	2	0	9
28-05-2020	Valparaíso	154	2	0	2,730	60	0	73
28-05-2020	Metropolitana	3,904	43	0	69,915	638	0	977
28-05-2020	O’Higgins	14	0	0	705	14	0	33
28-05-2020	Maule	54	0	0	1,120	15	0	33
28-05-2020	Ñuble	31	0	0	1,205	22	0	12
28-05-2020	Biobío	112	0	0	1,677	10	0	29
28-05-2020	Araucanía	38	0	0	1,954	45	0	17
28-05-2020	Los Ríos	9	0	0	318	6	0	2
28-05-2020	Los Lagos	23	0	0	814	13	0	14
28-05-2020	Aysén	2	0	0	14	0	0	0
28-05-2020	Magallanes	3	0	0	1,040	18	0	8
28-05-2020	No Informado	0	0	2,610	0	0	36,150	NA
29-05-2020	Arica y Parinacota	20	0	0	582	7	0	11
29-05-2020	Tarapacá	136	2	0	2,093	14	0	23
29-05-2020	Antofagasta	70	0	0	2,318	28	0	54
29-05-2020	Atacama	5	0	0	207	0	0	2
29-05-2020	Coquimbo	55	0	0	537	2	0	12
29-05-2020	Valparaíso	112	1	0	2,842	61	0	79
29-05-2020	Metropolitana	2,995	47	0	72,910	685	0	1,014
29-05-2020	O’Higgins	40	0	0	745	14	0	33
29-05-2020	Maule	60	0	0	1,180	15	0	36
29-05-2020	Ñuble	37	0	0	1,242	22	0	12
29-05-2020	Biobío	101	1	0	1,778	11	0	31
29-05-2020	Araucanía	35	2	0	1,989	47	0	20
29-05-2020	Los Ríos	12	0	0	330	6	0	2
29-05-2020	Los Lagos	8	0	0	822	13	0	14
29-05-2020	Aysén	2	0	0	16	0	0	0
29-05-2020	Magallanes	7	1	0	1,047	19	0	7
29-05-2020	No Informado	0	0	2,448	0	0	38,598	NA
30-05-2020	Arica y Parinacota	6	0	0	588	7	0	12
30-05-2020	Tarapacá	157	4	0	2,250	18	0	23
30-05-2020	Antofagasta	111	1	0	2,429	29	0	52
30-05-2020	Atacama	8	0	0	215	0	0	2
30-05-2020	Coquimbo	60	0	0	597	2	0	10
30-05-2020	Valparaíso	195	2	0	3,037	63	0	88
30-05-2020	Metropolitana	3,341	43	0	76,251	728	0	1,022
30-05-2020	O’Higgins	11	2	0	756	16	0	39
30-05-2020	Maule	76	0	0	1,256	15	0	39
30-05-2020	Ñuble	14	1	0	1,256	23	0	10
30-05-2020	Biobío	110	0	0	1,888	11	0	32
30-05-2020	Araucanía	71	0	0	2,060	47	0	19
30-05-2020	Los Ríos	15	0	0	345	6	0	2
30-05-2020	Los Lagos	38	0	0	860	13	0	14
30-05-2020	Aysén	0	0	0	16	0	0	0
30-05-2020	Magallanes	7	0	0	1,054	19	0	7
30-05-2020	No Informado	0	0	1,833	0	0	40,431	NA
31-05-2020	Arica y Parinacota	31	0	0	619	7	0	12
31-05-2020	Tarapacá	111	3	0	2,361	21	0	25
31-05-2020	Antofagasta	81	2	0	2,510	31	0	48
31-05-2020	Atacama	3	0	0	218	0	0	2
31-05-2020	Coquimbo	31	0	0	628	2	0	11
31-05-2020	Valparaíso	130	3	0	3,167	66	0	95
31-05-2020	Metropolitana	4,253	47	0	80,504	775	0	1,033
31-05-2020	O’Higgins	29	2	0	785	18	0	40
31-05-2020	Maule	41	0	0	1,297	15	0	38
31-05-2020	Ñuble	1	0	0	1,257	23	0	10
31-05-2020	Biobío	91	0	0	1,979	11	0	30
31-05-2020	Araucanía	11	0	0	2,071	47	0	19
31-05-2020	Los Ríos	11	0	0	356	6	0	2
31-05-2020	Los Lagos	3	0	0	863	13	0	14
31-05-2020	Aysén	1	0	0	17	0	0	0
31-05-2020	Magallanes	2	0	0	1,056	19	0	4
31-05-2020	No Informado	0	0	2,296	0	0	42,727	NA
01-06-2020	Arica y Parinacota	17	1	0	636	8	0	11
01-06-2020	Tarapacá	165	1	0	2,526	22	0	26
01-06-2020	Antofagasta	75	1	0	2,585	32	0	51
01-06-2020	Atacama	14	0	0	232	0	0	2
01-06-2020	Coquimbo	40	0	0	668	2	0	11
01-06-2020	Valparaíso	181	4	0	3,348	70	0	96
01-06-2020	Metropolitana	4,735	49	0	85,239	824	0	1,088
01-06-2020	O’Higgins	28	0	0	813	18	0	40
01-06-2020	Maule	44	2	0	1,341	17	0	39
01-06-2020	Ñuble	33	0	0	1,290	23	0	11
01-06-2020	Biobío	91	0	0	2,070	11	0	33
01-06-2020	Araucanía	40	0	0	2,111	47	0	18
01-06-2020	Los Ríos	7	1	0	363	7	0	2
01-06-2020	Los Lagos	0	0	0	863	13	0	14
01-06-2020	Aysén	0	0	0	17	0	0	0
01-06-2020	Magallanes	1	0	0	1,057	19	0	4
01-06-2020	No Informado	0	0	2,219	0	0	44,946	NA
02-06-2020	Arica y Parinacota	18	0	0	654	8	0	11
02-06-2020	Tarapacá	68	1	0	2,594	23	0	29
02-06-2020	Antofagasta	64	2	0	2,649	34	0	53
02-06-2020	Atacama	4	0	0	236	0	0	2
02-06-2020	Coquimbo	19	0	0	687	2	0	10
02-06-2020	Valparaíso	247	2	0	3,595	72	0	94
02-06-2020	Metropolitana	2,955	70	0	88,194	894	0	1,090
02-06-2020	O’Higgins	30	0	0	843	18	0	36
02-06-2020	Maule	40	0	0	1,381	17	0	39
02-06-2020	Ñuble	0	0	0	1,290	23	0	12
02-06-2020	Biobío	12	0	0	2,082	11	0	37
02-06-2020	Araucanía	30	0	0	2,141	47	0	17
02-06-2020	Los Ríos	5	0	0	368	7	0	1
02-06-2020	Los Lagos	31	0	0	894	13	0	15
02-06-2020	Aysén	1	0	0	18	0	0	0
02-06-2020	Magallanes	3	0	0	1,060	19	0	5
02-06-2020	No Informado	0	0	41,034	0	0	85,980	NA
03-06-2020	Arica y Parinacota	47	0	0	701	8	0	11
03-06-2020	Tarapacá	88	5	0	2,682	28	0	32
03-06-2020	Antofagasta	67	2	0	2,716	36	0	54
03-06-2020	Atacama	6	0	0	242	0	0	2
03-06-2020	Coquimbo	31	2	0	718	4	0	9
03-06-2020	Valparaíso	174	5	0	3,769	77	0	92
03-06-2020	Metropolitana	3,997	67	0	92,191	961	0	1,112
03-06-2020	O’Higgins	88	2	0	931	20	0	31
03-06-2020	Maule	214	2	0	1,595	19	0	43
03-06-2020	Ñuble	39	1	0	1,329	24	0	13
03-06-2020	Biobío	133	0	0	2,215	11	0	39
03-06-2020	Araucanía	32	0	0	2,173	47	0	17
03-06-2020	Los Ríos	4	1	0	372	8	0	1
03-06-2020	Los Lagos	19	0	0	913	13	0	14
03-06-2020	Aysén	0	0	0	18	0	0	0
03-06-2020	Magallanes	3	0	0	1,063	19	0	5
03-06-2020	No Informado	0	0	4,768	0	0	90,748	NA
04-06-2020	Arica y Parinacota	35	0	0	736	8	0	11
04-06-2020	Tarapacá	128	0	0	2,810	28	0	32
04-06-2020	Antofagasta	146	2	0	2,862	38	0	54
04-06-2020	Atacama	16	0	0	258	0	0	2
04-06-2020	Coquimbo	54	0	0	772	4	0	10
04-06-2020	Valparaíso	248	1	0	4,017	78	0	90
04-06-2020	Metropolitana	3,699	73	0	95,890	1,034	0	1,128
04-06-2020	O’Higgins	36	1	0	967	21	0	31
04-06-2020	Maule	137	2	0	1,732	21	0	48
04-06-2020	Ñuble	43	0	0	1,372	24	0	12
04-06-2020	Biobío	86	2	0	2,301	13	0	43
04-06-2020	Araucanía	9	0	0	2,182	47	0	17
04-06-2020	Los Ríos	7	0	0	379	8	0	1
04-06-2020	Los Lagos	13	0	0	926	13	0	12
04-06-2020	Aysén	1	0	0	19	0	0	0
04-06-2020	Magallanes	6	0	0	1,069	19	0	5
04-06-2020	No Informado	0	0	4,883	0	0	95,631	NA
05-06-2020	Arica y Parinacota	46	1	0	782	9	0	12
05-06-2020	Tarapacá	155	0	0	2,965	28	0	34
05-06-2020	Antofagasta	62	4	0	2,924	42	0	46
05-06-2020	Atacama	11	0	0	269	0	0	2
05-06-2020	Coquimbo	60	1	0	832	5	0	11
05-06-2020	Valparaíso	249	10	0	4,266	88	0	92
05-06-2020	Metropolitana	3,176	71	0	99,066	1,105	0	1,147
05-06-2020	O’Higgins	49	1	0	1,016	22	0	35
05-06-2020	Maule	102	3	0	1,834	24	0	41
05-06-2020	Ñuble	57	0	0	1,429	24	0	13
05-06-2020	Biobío	78	0	0	2,379	13	0	47
05-06-2020	Araucanía	106	1	0	2,288	48	0	20
05-06-2020	Los Ríos	11	0	0	390	8	0	2
05-06-2020	Los Lagos	42	0	0	968	13	0	13
05-06-2020	Aysén	0	0	0	19	0	0	0
05-06-2020	Magallanes	3	0	0	1,072	19	0	6
05-06-2020	No Informado	0	0	3,727	0	0	99,358	NA
06-06-2020	Arica y Parinacota	37	0	0	819	9	0	12
06-06-2020	Tarapacá	129	2	0	3,094	30	0	39
06-06-2020	Antofagasta	109	5	0	3,033	47	0	49
06-06-2020	Atacama	12	0	0	281	0	0	2
06-06-2020	Coquimbo	66	0	0	898	5	0	11
06-06-2020	Valparaíso	314	3	0	4,580	91	0	86
06-06-2020	Metropolitana	4,128	79	0	103,194	1,184	0	1,144
06-06-2020	O’Higgins	95	3	0	1,111	25	0	35
06-06-2020	Maule	135	0	0	1,969	24	0	42
06-06-2020	Ñuble	41	0	0	1,470	24	0	13
06-06-2020	Biobío	111	0	0	2,490	13	0	50
06-06-2020	Araucanía	33	1	0	2,321	49	0	18
06-06-2020	Los Ríos	10	0	0	400	8	0	2
06-06-2020	Los Lagos	17	0	0	985	13	0	17
06-06-2020	Aysén	0	0	0	19	0	0	0
06-06-2020	Magallanes	9	0	0	1,081	19	0	4
06-06-2020	No Informado	0	0	4,459	0	0	103,817	NA
07-06-2020	Arica y Parinacota	31	1	0	850	10	0	11
07-06-2020	Tarapacá	156	2	0	3,250	32	0	41
07-06-2020	Antofagasta	121	0	0	3,154	47	0	51
07-06-2020	Atacama	25	0	0	306	0	0	2
07-06-2020	Coquimbo	92	0	0	990	5	0	10
07-06-2020	Valparaíso	282	2	0	4,862	93	0	91
07-06-2020	Metropolitana	5,268	89	0	108,462	1,273	0	1,174
07-06-2020	O’Higgins	72	0	0	1,183	25	0	36
07-06-2020	Maule	140	0	0	2,109	24	0	42
07-06-2020	Ñuble	20	1	0	1,490	25	0	13
07-06-2020	Biobío	105	0	0	2,595	13	0	47
07-06-2020	Araucanía	56	1	0	2,377	50	0	16
07-06-2020	Los Ríos	17	0	0	417	8	0	2
07-06-2020	Los Lagos	18	0	0	1,003	13	0	17
07-06-2020	Aysén	1	0	0	20	0	0	1
07-06-2020	Magallanes	1	0	0	1,082	19	0	4
07-06-2020	No Informado	0	0	4,333	0	0	108,150	NA
08-06-2020	Arica y Parinacota	24	-2	0	874	8	0	10
08-06-2020	Tarapacá	142	3	0	3,392	35	0	44
08-06-2020	Antofagasta	91	-2	0	3,245	45	0	53
08-06-2020	Atacama	33	0	0	339	0	0	2
08-06-2020	Coquimbo	133	0	0	1,123	5	0	11
08-06-2020	Valparaíso	287	-8	0	5,149	85	0	91
08-06-2020	Metropolitana	3,674	677	0	112,136	1,950	0	1,182
08-06-2020	O’Higgins	72	-3	0	1,255	22	0	36
08-06-2020	Maule	67	-6	0	2,176	18	0	49
08-06-2020	Ñuble	62	-7	0	1,552	18	0	16
08-06-2020	Biobío	56	-2	0	2,651	11	0	46
08-06-2020	Araucanía	33	-18	0	2,410	32	0	16
08-06-2020	Los Ríos	8	0	0	425	8	0	2
08-06-2020	Los Lagos	7	-1	0	1,010	12	0	18
08-06-2020	Aysén	1	0	0	21	0	0	1
08-06-2020	Magallanes	6	-4	0	1,088	15	0	4
08-06-2020	No Informado	0	0	4,098	0	0	112,248	NA
09-06-2020	Arica y Parinacota	40	0	0	914	8	0	12
09-06-2020	Tarapacá	99	0	0	3,491	35	0	45
09-06-2020	Antofagasta	157	0	0	3,402	45	0	55
09-06-2020	Atacama	9	0	0	348	0	0	2
09-06-2020	Coquimbo	52	0	0	1,175	5	0	13
09-06-2020	Valparaíso	242	0	0	5,391	85	0	95
09-06-2020	Metropolitana	2,990	18	0	115,126	1,968	0	1,172
09-06-2020	O’Higgins	62	1	0	1,317	23	0	34
09-06-2020	Maule	113	0	0	2,289	18	0	44
09-06-2020	Ñuble	32	0	0	1,584	18	0	16
09-06-2020	Biobío	43	0	0	2,694	11	0	46
09-06-2020	Araucanía	35	0	0	2,445	32	0	16
09-06-2020	Los Ríos	21	0	0	446	8	0	2
09-06-2020	Los Lagos	18	0	0	1,028	12	0	20
09-06-2020	Aysén	0	0	0	21	0	0	1
09-06-2020	Magallanes	0	0	0	1,088	15	0	4
09-06-2020	No Informado	0	0	5,113	0	0	117,361	NA
10-06-2020	Arica y Parinacota	16	2	0	930	10	0	13
10-06-2020	Tarapacá	100	11	0	3,591	46	0	42
10-06-2020	Antofagasta	157	11	0	3,559	56	0	58
10-06-2020	Atacama	17	0	0	365	0	0	1
10-06-2020	Coquimbo	81	1	0	1,256	6	0	17
10-06-2020	Valparaíso	223	14	0	5,614	99	0	98
10-06-2020	Metropolitana	4,620	147	0	119,746	2,115	0	1,179
10-06-2020	O’Higgins	125	3	0	1,442	26	0	37
10-06-2020	Maule	151	0	0	2,440	18	0	44
10-06-2020	Ñuble	32	2	0	1,616	20	0	13
10-06-2020	Biobío	152	0	0	2,846	11	0	45
10-06-2020	Araucanía	43	1	0	2,488	33	0	16
10-06-2020	Los Ríos	6	0	0	452	8	0	2
10-06-2020	Los Lagos	13	0	0	1,041	12	0	20
10-06-2020	Aysén	0	0	0	21	0	0	1
10-06-2020	Magallanes	1	0	0	1,089	15	0	4
10-06-2020	No Informado	0	0	4,479	0	0	121,840	NA

#ableExtra::add_footnote("Full Information Obtained By ivanMSC/COVID19_Chile", notation = "none")

library(tidyverse)
library(gganimate)
library(gapminder)
library(gifski)

library(dplyr)
# ... but if an error occurs, tell me what happened: 
casos <- readr::read_csv("https://raw.githubusercontent.com/ivanMSC/COVID19_Chile/master/covid19_chile.csv")%>%
  dplyr::mutate(date_posixct=lubridate::parse_date_time(as.character(Fecha),orders="dmY")) 

## Parsed with column specification:
## cols(
##   Fecha = col_character(),
##   Region = col_character(),
##   `Nuevo Confirmado` = col_double(),
##   `Nuevo Muerte` = col_double(),
##   `Nuevo Recuperado` = col_double(),
##   `Acum Confirmado` = col_double(),
##   `Acum Muerte` = col_double(),
##   `Acum Recuperado` = col_double(),
##   `Casos UCI` = col_double()
## )

manualcolors<-c('black','forestgreen', 'red2', 'orange', 'cornflowerblue', 
                'magenta', 'darkolivegreen4',  
                'indianred1', 'tan4', 'darkblue', 
                'mediumorchid1','firebrick4',  'yellowgreen', 'gray20', 'tan3',
                "tan1",'darkgray', 'wheat4', '#DDAD4B', 'chartreuse', 'seagreen1',
                'moccasin', 'mediumvioletred', 'seagreen','cadetblue1',
                "darkolivegreen1" ,"tan2" ,   "tomato3" , "#7CE3D8","gainsboro")

#casos_region <- readr::read_csv("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto3/CasosTotalesCumulativo.csv")  %>%
#  reshape2::melt()%>%
#  dplyr::mutate(date_posixct=lubridate::parse_date_time(as.character(variable),orders="Ymd")) %>% 
#  dplyr::select(-variable)
casos_region <- casos %>% dplyr::select(Region, `Nuevo Confirmado`, date_posixct) %>% dplyr::rename("value"=`Nuevo Confirmado`)
gap  <- casos_region %>%
  dplyr::group_by(Region) %>%
  dplyr::mutate(confirmed_cases=cumsum(value)) %>%
  dplyr::ungroup() %>%
  dplyr::filter(Region!="Total")

total_gap_max  <- gap %>% dplyr::filter(Region!="Total")%>%
  dplyr::slice(which.max(confirmed_cases))
total_gap_max  <-as.numeric(unlist(total_gap_max["confirmed_cases"]))+10000

ggplot(gap, aes(x=reorder(Region, as.numeric(confirmed_cases)),y=confirmed_cases,fill=Region))+
  geom_bar(stat="identity")+
  theme(legend.position="non",axis.text.y=element_blank(),
        axis.title.y=element_blank())+
  geom_text(aes(label=paste0("",confirmed_cases),vjust=1,hjust=-0.1,color="black",size=3.5))+
  coord_flip()+
  labs(title=' Figure 7a. Cumulative Cases per Region,\n {frame_time}', x = "", y = "Cumulative Cases",
       caption = "Sources: Datasets from\nhttps://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto3/CasosTotalesCumulativo.csv")+
  theme(plot.title = element_text(hjust = 0, size = 22),
        axis.ticks.y = element_blank(),  # These relate to the axes post-flip
        axis.text.y  = element_blank(),  # These relate to the axes post-flip
        plot.margin = margin(1,1,1,3, "cm")) +
  guides(color = FALSE, fill = FALSE,size=F) +
  transition_time(as.Date(date_posixct))+
  theme_classic()+
  theme(plot.title = element_text(color="darkblue"))+
  ylim(0,total_gap_max)+
  ease_aes("linear")+
  theme(plot.caption = element_text(hjust = 0, face = "italic",size=9))+
  # shadow_mark() +
  enter_grow() +
  enter_fade()

#also: margin(t, r, l, b)
#animate(pp, 200, fps = 10, duration = 20, width = 800, height = 600)
#animate(pp, renderer= gifski_renderer("gganim.gif"))
#gg_animate(pp)

#<br>

#Also, more cases reported are women (See Figure 7).
library(gghighlight)

#Cambiar a PCR 
rate <- readr::read_csv("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto12/bulk/producto7.csv")  %>% 
  dplyr::select(Region,Fecha,Tasa)%>%
  reshape2::melt()%>%
  dplyr::mutate(date_posixct=lubridate::parse_date_time(as.character(value),orders="Ymd")) %>% 
  dplyr::select(-variable)

rate %>%
  #dplyr::filter(Ventiladores!="total") %>%
  dplyr::mutate(Tasa=round(as.numeric(Tasa),2)) %>%
  dplyr::mutate(RM=ifelse(Region=="Metropolitana",1,0))%>%
  dplyr::group_by(RM)%>%
  dplyr::mutate(Mean_Rate=mean(Tasa,na.rm=T),Median_Rate=median(Tasa,na.rm=T))%>%
  dplyr::ungroup() %>%
  dplyr::mutate(tooltip=paste0(Region,"=",Tasa))%>%
  ggplot(aes(x=date_posixct,y=Tasa, group=RM))+
  scale_x_datetime(breaks=scales::date_breaks("1 week"),labels = scales::date_format("%d/%m"),
                   limits = as.POSIXct(c('2020-04-01 09:00:00',as.character(Sys.time()))))+
  stat_smooth(method = "lm", se = T) + 
  stat_summary(geom = "point", fun.y = mean, shape = 15, size = 3, alpha=0.5, color="blue") + 
  stat_quantile(quantiles = c(0.25, 0.5, 0.75), color= "black", linetype="dashed") +
  facet_grid(. ~ RM,labeller =as_labeller(c(`0` = "Rest of Regions", `1` = "Metropolitan")))+   
  #  geom_line(aes(x = date_posixct, y = Tasa, color=factor(Region)),size=1) +
  #  gghighlight(Region == "Metropolitana")+
  sjPlot::theme_sjplot2() +
  labs(title=' Figure 7b. Linear trends of Rate of PCRs', x="Dates",
       caption = "Sources: Datasets from\nraw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto20/NumeroVentiladores.csv")+
  theme(plot.title = element_text(color="darkblue"),
        legend.position = "bottom")+
  theme(plot.caption = element_text(hjust = 0, face = "italic",size=9))

#fig <- plotly::plot_ly(casos_sex, labels = ~Sexo, values = ~n, type = 'pie')
#fig <- fig %>% plotly::layout(title = 'Figure 7. Pie Chart of Cases by Sex')
#print(fig)
#library(rAmCharts)
# piechart_rjs(casos, cex = 1, plot = TRUE, jupyter = FALSE, dir = "Figure 7. Pie Chart of Cases by Sex")
#  amPie(data = vents)  %>%
##  amOptions(main = "Figure 7. Pie Chart of Vents", mainColor = "darkblue", mainSize = 15,
##           theme = "light", legend = TRUE, legendPosition = "bottom",
##            creditsPosition = "bottom-right")

#  ggPieDonut(casos_sex, aes(pies=n, colors=Sexo),interactive=TRUE, addPieLabel = TRUE, addDonutLabel = TRUE,
#  showRatioDonut = TRUE, showRatioPie = TRUE, title = "Figure 7. Pie Chart of Cases by Sex", labelposition = 2, polar = F, use.label= F, use.labels = TRUE)

casos_ventiladores <- readr::read_csv("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto20/NumeroVentiladores.csv")  %>%
  reshape2::melt()%>%
  dplyr::mutate(date_posixct=lubridate::parse_date_time(as.character(variable),orders="Ymd")) %>% 
  dplyr::select(-variable)

casos_ventiladores %>%
  dplyr::group_by(date_posixct) %>%
  dplyr::filter(Ventiladores!="total") %>%
  dplyr::mutate(ifelse(Ventiladores=="disponibles","Available","Occupied"))%>%
  dplyr::mutate(percent=round(value/sum(value),2)) %>% 
  dplyr::ungroup() %>%
  ggplot(aes(x=1,y=percent,fill=Ventiladores))+
  geom_col(width=1)+
  coord_polar(theta="y",start=1.2)+
  geom_text(aes(label=scales::percent(percent)),position= position_stack(vjust=.5))+
  guides(color = FALSE, size=F) +
  transition_time(as.Date(date_posixct))+
  theme_void()+
  labs(title=' Figure 7c. Pie Chart of Vents,\n {frame_time}',
       caption = "Sources: Datasets from\nraw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto20/NumeroVentiladores.csv")+
  theme(plot.title = element_text(color="darkblue"))+
  theme(plot.caption = element_text(hjust = 0, face = "italic",size=9))+
  # ylim(0,total_gap_max)+
  ease_aes("linear")

As shown in Figures 7a and b, Metropolitan Region has more cases with covid than the rest of the country, increasing the rate of diagnosed with covid by PCRs administered. There are no information regarding the amount of vents by region and their availability status, but at the national level, the percentage of occupied vents tend to increase by each day.

Also, we created a map with the cases by region (from data obtained in this link), and coloring those regions that have more cases. As can be seen, more cases tend to group in the Metropolitan Region

library(dplyr)
library(choroplethr)
library(choroplethrMaps)
library(choroplethrAdmin1)
#_#_#_#_#__#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#__#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#__#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#__#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#__#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#__#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#__#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#__#_#_#_#_#_#_#_#_#_#_#_

#"https://raw.githubusercontent.com/demm94/Covid19-CL/master/15-03-2020.csv"
##MODO JSON

casos <- readr::read_csv("https://raw.githubusercontent.com/ivanMSC/COVID19_Chile/master/covid19_chile.csv")

minsal_casos_sin_hosp_region<-as.data.frame(cbind(minsal_casos_sin_hosp,cod=rbind("15","01","02",
                                                                                  "03","04", "05", "13", "06", "07","16",
                                                                                  "08","09","14", "10", "11","12")))
library(ggplot2)
data(admin1.map, package="choroplethrAdmin1")
#get_admin1_regions("chile") # falta ñuble po la cresta
chile.map = admin1.map[admin1.map$admin == "chile" & admin1.map$long > -80, ]
#ggplot(chile.map, aes(long, lat, group=group)) + geom_polygon() + coord_map()

arica <- list(v ="01", n = "region de arica y parinacota")
tarapaca <- list(v = "02", n= "region de tarapaca")
antofagasta <- list(v = "6.24",n = "region de antofagasta")
atacama <- list(v = 6.66, n = "region de atacama")
coquimbo <- list(v = 6.93, n = "region de coquimbo")
valparaiso <- list(v = 7.19,n = "region de valparaiso")
metropolitana <- list(v = 6.33, n = "region metropolitana de santiago")
ohiggins <- list(v = 5.27, n = "region del libertador general bernardo o'higgins")
maule <- list(v = 5.99, n = "region del maule")
nuble <- list(v = 5.99, n = "region de ñuble")
biobio <- list(v = 8.07, n = "region del biobio")
araucania <- list(v = 6.07, n = "region de la araucania")
losrios <- list(v = 4.72, n = "region de los rios")
loslagos <- list(v = 3.82, n = "region de los lagos")
aysen <- list(v = 3.84, n = "region aisen del general carlos ibanez del campo")
magallanes <- list(v = 2.63, n = "region de magallanes y de la antartica chilena")

dat2 <- data.frame(
  region = c(arica$n,tarapaca$n,antofagasta$n,atacama$n,coquimbo$n,valparaiso$n,
             metropolitana$n,ohiggins$n,maule$n,nuble$n, biobio$n,araucania$n,losrios$n,
             loslagos$n,aysen$n,magallanes$n)
)

datos_region = chilemapas:: generar_regiones(mapa = chilemapas::mapa_comunas)

#chilemapas::codigos_territoriales %>% dplyr::group_by(nombre_region) %>% distinct(codigo_region)

dat2<-datos_region %>%
  dplyr:::left_join(as_tibble(minsal_casos_sin_hosp_region), by=c("codigo_region"="cod.V1")) %>%
  dplyr::mutate(tooltip=paste0(`Región`,"=",`Totales`),
                `Totales`= as.numeric(gsub(",","",`Totales`)))
#p <- admin1_choropleth(country.name = "chile", 
#                       df           = dat2, 
#                       title        = "Figure 8. Cases By Region", 
#                       legend       = "No. of cases")
#p+scale_x_continuous(limits=c(0, 152)) #Restringir a Chile continental

#admin1_region_choropleth(df= dat2,
#                         title        = "Figure 8. Cases By Region", 
#                         legend       = "No. of cases")

#x_map=purrr::map_dbl(dat2$geometry, ~sf::st_centroid(.x)[[1]])
#y_map=purrr::map_dbl(dat2$geometry, ~sf::st_centroid(.x)[[2]])

suppressPackageStartupMessages(library(ggiraph))
suppressPackageStartupMessages(library(sf))
library("RColorBrewer")

gg2<-ggplot(data=dat2) +
  #geom_sf(aes(fill=as.numeric(dat2$value))) +
  theme_minimal() +
  geom_sf_interactive(aes(fill = cut(as.numeric(dat2$`Totales`),round(quantile(as.numeric(dat2$`Totales`),na.rm=T),0), include.lowest=TRUE,dig.lab=10,right=T), tooltip = tooltip, data_id = codigo_region))+
  scale_fill_brewer(palette="Reds",na.value="grey80") +
  labs(title='Figure 8. Cases By Region', subtitle='By quantiles',
       caption='Source: https://www.gob.cl/coronavirus/casosconfirmados.html',fill="No. of cases\n(quantiles)")+
  theme(plot.title = element_text(color="darkblue"))
# geom_sf(data = dat2, aes(fill = value)) +
#  coord_sf( expand = T)
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

ggiraph(code = {print(gg2)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

Incidence Rate

Must keep in mind that the amount of cases are not enough for a statistically significant extrapolation. That is why we should remain cautious when interpreting and predicting the behaviour of this disease. Another limitation is that the date of reporting could be a biased estimate because it is different than the date of symptom onset. the date of onset The incidence rate is the rate at which new cases of the outcome of interest occur in a population (the speed of which cases develop). We also bootstrapped the incidence in order to produce a more reliable estimates.

#https://quizlet.com/218253657/cumulative-incidence-vs-incidence-rate-vs-prevalence-flash-cards/
#Raw incidence rate
#This function can be used to bootstrap incidence objects. Bootstrapping is done by sampling with replacement the original input dates. See details for more information on how this is implemented.

if (format(Sys.time(),"%H")<12) {
  today <- format(Sys.time()-3600*24, "%Y-%m-%d")
} else {
  today <- format(Sys.time(), "%Y-%m-%d")
}
#format(Sys.time()'%d-%m-%Y')
library(splitstackshape)

casos_sin_recuperados<-casos %>%
  dplyr::mutate(Fecha=lubridate::parse_date_time(Fecha,"dmY"))%>%
  dplyr::select(Fecha,`Nuevo Confirmado`) %>%
  dplyr::group_by(Fecha) %>%
  summarise(incidence=sum(`Nuevo Confirmado`))%>%
  splitstackshape::expandRows(., "incidence") %>%
  data.frame()

cl_incidence <- incidence::incidence(casos_sin_recuperados$Fecha, last_date=today)
cl_incidence <- bootstrap(cl_incidence)
#cl_incidence_cum <- bootstrap(cumulate(cl_incidence)) #no sirve de nada, conceptualmente es
#The proportion of an outcome-free population that develop the outcome of interest in a specified time period
print(cl_incidence)

## <incidence object>
## [148496 cases from days 2020-03-03 to 2020-06-10]
## 
## $counts: matrix with 100 rows and 1 columns
## $n: 148496 cases in total
## $dates: 100 dates marking the left-side of bins
## $interval: 1 day
## $timespan: 100 days
## $cumulative: FALSE

We fitted a log-linear model to our epidemic curve. Typically, two models are fitted, one for the growth-phase and one for the decay phase. Fow now, we cannot determine a decay phase, that is why we only fitted the growth rate

#Raw incidence rate
#The function fit fits two exponential models to incidence data, of the form: log(y) = r ∗ t + b
#where ’y’ is the incidence, ’t’ is time (in days), ’r’ is the growth rate, and ’b’ is the origin
#Fit exponential models to incidence da

cl_incidence_fit <- incidence::fit(cl_incidence, split = NULL)

# plot the incidence data and the model fit
plot(cl_incidence, color="black", alpha=.4) %>% 
  add_incidence_fit(cl_incidence_fit) +  
  sjPlot::theme_sjplot2() +
  ggtitle( "Figure 9. Incidence Rate of Cases By Days (Fitted in lines)")+
  labs(x="Days passed")+
  theme(axis.text.x = element_text(angle =25, hjust = 1, size = 8)) +
  theme(plot.title = element_text(color="darkblue"))

From that model, we can extract various (very preliminary at this early stage) parameters of interest: the growth rate is 0.07 (95% CI 0.06 – 0.08), which is equivalent to a doubling time of 9.78 days (95% CI 9 – 10.71 days). For a discussion about exponential growth in COVID-19, see Siegel (2020, March 17).

casos_sin_recuperados<- casos_sin_recuperados %>%
  dplyr::mutate(Sexo=ifelse(Sexo=="Hombre", "Masculino", Sexo))
cl_incidence_sex <- incidence(lubridate::parse_date_time(casos_sin_recuperados$Fecha,"dmY"), groups = casos_sin_recuperados$Sexo)
plot(cl_incidence_sex, stack=T, border="white",
     col_pal = incidence_pal1_light, labels_week = F)+
  sjPlot::theme_sjplot2() +
  ggtitle( "Figure 10. Incidence Rate of Cases By Sex")+
  labs(x="Days passed")+
  theme(legend.position="bottom")+
  theme(axis.text.x = element_text(angle =25, hjust = 1, size = 8)) +
  theme(plot.title = element_text(color="darkblue"))

library(dplyr)
library(incidence)

casos <- readr::read_csv("https://raw.githubusercontent.com/ivanMSC/COVID19_Chile/master/covid19_chile.csv")

if (format(Sys.time(),"%H")<12) {
  today <- format(Sys.time()-3600*24, "%Y-%m-%d")
} else {
  today <- format(Sys.time(), "%Y-%m-%d")
}

casos_sin_recuperados<-casos %>%
  dplyr::mutate(Fecha=lubridate::parse_date_time(Fecha,"dmY"))%>%
  dplyr::select(Fecha,`Nuevo Confirmado`) %>%
  dplyr::group_by(Fecha) %>%
  summarise(incidence=sum(`Nuevo Confirmado`))%>%
  splitstackshape::expandRows(., "incidence") %>%
  data.frame()

casos_muertos<-casos %>%
  dplyr::mutate(Fecha=lubridate::parse_date_time(Fecha,"dmY"))%>%
  dplyr::select(Fecha,`Nuevo Muerte`) %>%
  dplyr::group_by(Fecha) %>%
  summarise(incidence=sum(`Nuevo Muerte`))%>%
  splitstackshape::expandRows(., "incidence") %>%
  data.frame()


cl_incidence_muertos <- incidence::incidence(casos_muertos$Fecha,last_date=today)
cl_incidence_muertos <- bootstrap(cl_incidence_muertos)

cl_incidence_fit_deaths <- incidence::fit(cl_incidence_muertos, split = NULL)

# plot the incidence data and the model fit
plot(cl_incidence_muertos, color="black", alpha=.4) %>% 
  add_incidence_fit(cl_incidence_fit_deaths) +  
  sjPlot::theme_sjplot2() +
  ggtitle( "Figure 10. Incidence Rate of Deaths By Days (Fitted in lines)")+
  labs(x="Days passed")+
  #ylim(0,75)+
  theme(axis.text.x = element_text(angle =25, hjust = 1, size = 8)) +
  theme(plot.title = element_text(color="darkblue"))

Must take note that starting from March 19, sex is not published by MINSAL, so the incidence rate must be hardly estimated by sex. However, we have information of deaths up to the date (See Figure 10). The estimated growth rate is 0.05 (95% CI 0.05 – 0.06), which is equivalent to a doubling time of 12.84 days (95% CI 11.39 – 14.73 days). Also, must take note that of the total cases, only 1.67% are dead by now. Differently put, per 100,000 diagnosed, only 1667 would end dead.

tipo_caso <-casos %>%
  dplyr::select(Fecha,`Nuevo Confirmado`, `Nuevo Muerte`) %>%
  dplyr::mutate(date=lubridate::parse_date_time(casos$Fecha,"dmY")) %>%
  dplyr::select(-Fecha) %>%
  reshape2::melt(id = "date", 
                 measured.vars=c("Nuevo Confirmado","Nuevo Muerte","Nuevo Recuperado"),
                 na.rm = TRUE) %>%
  dplyr::mutate(variable= ifelse(variable=="Nuevo Confirmado", "Confirmed",
                                 ifelse(variable=="Nuevo Muerte", "Dead", variable))) %>%
  dplyr::mutate(value=as.numeric(value))%>%
                                        #ifelse(variable=="Nuevo Confirmado", "Confirmed", variable)))) %>%
  dplyr::filter(value>0)%>%
  #expandRows(., "value") %>%
  data.frame() %>%
tidyr::uncount(value, .remove = FALSE)


cl_incidence_tipo_caso <- incidence(tipo_caso$date, groups=tipo_caso$variable)

#cl_incidence_tipo_caso[,1:2]

plot(cl_incidence_tipo_caso[,1:2], stack=T, border=NA, labels_week = FALSE,
     color = c( Confirmed = "brown", Dead= "black"), alpha=.75)+ 
  theme(legend.position="bottom") +
  ggtitle( "Figure 11. Incidence Rate of Cases By Condition")+
  labs(caption= "Excluded recovered cateogry due to misleading formula to estimate recovered (+14 days)")+
  theme(panel.background = element_rect(fill = NA)) +  #para que nos e vea el fondo
  #  scale_x_date(labels = scales::date_format("%b %Y"))
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(angle = 35, hjust = 1, size = 8)) +
  theme(legend.position="bottom")+
  theme(plot.title = element_text(color="darkblue"))

Must take note that deaths have been increasing, but still show a low proportion in comparison to cases reported (See Figure 11).

Describing Transmission of COVID

We also estimated the basic reproduction number (R 0) in the growth phase of the epidemic. Differently put, is the estimated mean number of new secondary cases caused by a an infected individual over his/her infectious period, assuming a totally susceptible population. This number will let us infer how manageable can be an infectuous disease. There are a few studies that estimate serial intervals (SI) up to date: Li et al., (2020) fitted a gamma distribution model based on cases reported by Jan 22, 2020, getting an estimated SI (M=7.5, SD=3.4). A model using a using the lognormal distribution identifies an estimated mean serial interval of 4.7 days (SD= 2.9) in mainland China during Jan 21 to Feb 8, 2020 (Nishiura, Linton & Akhmetzhanov, 2020). However, Zhao, et al. (pre-print) using a gamma distribution, estimates a mean of 4.4 days (SD=3.0) in Hong-Kong from Jan 16 to Feb 15, 2020, but their findings are not published and peer-reviewed yet.

We used several informative priors distributions for several serial interval based on the abovementioned studies.

library(distcrete)
library(epitrix)
mu <- 7.5  # days
sigma <- 3.4  # days
param <- gamma_mucv2shapescale(mu, sigma/mu)

w0 <- distcrete("gamma", interval = 1, shape = param$shape, scale = param$scale, 
                w = 0)

growth_R00 <- lm2R0_sample(cl_incidence_fit$model, w0)
hist(growth_R00, col = "#009999", border = "white", main = "",
     xlab="Estimated growth")
title("Figure 12. Distribution of R0 (Li, et al.,2020)", col.main = "darkblue")

According a model assuming a gamma distribution and providing a distribution according Li, et al. (2020), the estimated mean of the serial interval in Chile is 1.6 (SD= 0.03), meaning that the disease spread around 4.7 times less than the estimates from Wuhan.

library(distcrete)
library(epitrix)
mu <- 4.7  # days
sigma <- 2.9  # days
param <- gamma_mucv2shapescale(mu, sigma/mu)

w <- distcrete("gamma", interval = 1, shape = param$shape, scale = param$scale, 
               w = 0)

growth_R0 <- lm2R0_sample(cl_incidence_fit$model, w)
hist(growth_R0, col = "darkolivegreen3", border = "white", main = "",
     xlab="Estimated growth")
title("Figure 13. Distribution of R0 (Nishiura, et al.,2020)", col.main = "darkblue")

According a model assuming a gamma distribution and providing a distribution according Nishiura, et al. (2020), the estimated mean of the serial interval in Chile is 1.32 (SD= 0.01), meaning that the disease spread around 3.6 times less than the estimates from mainland China.

library(distcrete)
library(epitrix)
mu <- 4.4  # days
sigma <- 3  # days
param <- gamma_mucv2shapescale(mu, sigma/mu)

w2 <- distcrete("gamma", interval = 1, shape = param$shape, scale = param$scale, 
                w = 0)

growth_R02 <- lm2R0_sample(cl_incidence_fit$model, w2)
hist(growth_R02, col = "antiquewhite3", alpha=.7, border = "white", main="",
     xlab="Estimated growth")
title("Figure 14. Distribution of R0 (Zhao, et al.)", col.main = "darkblue")

According a model assuming a gamma distribution and providing a distribution according Zhao, et al. (in press), the estimated mean of the serial interval in Chile is 1.29 (SD= 0.01), meaning that the disease spread around 3.4 times less than the estimates from Hong-Kong.

If we want to estimate the effective reproduction number on a daily basis to track the progression and transmissibility throughout an epidemic from the analysis of time series of incidence (Cori, Ferguson, Fraser, Cauchemez, 2013), we used the overall ranges provided by the three studies, by getting their average mean, std. deviation, and confindence intervals as minimum and maximum values in estimation.

It is necessary to capture the variation in the serial interval distribution between cases. We incorporated this uncertainty around the SI distribution. The estimates obtained were resampled 2,500 times to adjust confidence intervals.

#
#For the estimation of the force of infection λ, for the serial interval we’ll use a discrete γ distribution with a mean of 5.0 days and a standard deviation of 3.4.

#https://www.repidemicsconsortium.org/earlyR/
#https://en.wikipedia.org/wiki/Force_of_infection

#For estimating R, we need estimates of the mean and standard deviation of the serial interval, i.e. the delay between primary and secondary symptom onset dates. This has been quantified durin the West African EVD outbreak (WHO Ebola Response Team (2014) NEJM 371:1481–1495):

#effective reproduction number plots, and force-of-infection λ plots
#assessment of how well containment efforts are succeeding (assuming that case detection and reporting doesn’t change)

library(earlyR)

## estimate the reproduction number (method "uncertain_si")

#res <- get_R(cl_incidence, si_mean = mu, si_sd = sigma)

##FUENTES

#https://www.ijidonline.com/action/showPdf?pii=S1201-9712%2820%2930119-3
#mean and standard deviation (SD) of the serial interval were estimated at 
#4.7 days (95% CrI: 3.7, 6.0) and 2.9 days (95% CrI: 1.9, 4.9), 

#https://www.medrxiv.org/content/10.1101/2020.02.21.20026559v1.full.pdf
# 4.4 days (95%CI: 2.9−6.7) 43 and SD of SI at 3.0 days (95%CI: 1.8−5.8) 

#https://www.nejm.org/doi/full/10.1056/NEJMoa2001316
#With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), 
#the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9)

#incidence_cl_format<- casos %>%
#                      dplyr::filter(Tipo_de_caso=="Confirmado") %>%
#                      dplyr::group_by(Fecha) %>%
#                      dplyr::count() %>%
#                      dplyr::rename("I"="n", "dates"="Fecha") %>%
#                      dplyr::ungroup() %>%
#                      dplyr::mutate(dates=as.Date(dates, "%d-%m-%Y"))%>%
#                      data.frame()
incidence_cl_format<-casos %>%
  dplyr::select(Fecha,`Nuevo Confirmado`) %>%
  dplyr::mutate(Fecha=lubridate::parse_date_time(Fecha,"dmY")) %>%
  dplyr::group_by(Fecha) %>%
  dplyr::arrange(Fecha)%>%
  summarise(I=sum(`Nuevo Confirmado`))
#%>%
#  expandRows(., "incidence") %>%
#  data.frame()
#
m=mean(c(4.7,4.4,7.5))
m_sd=sd(c(4.7,4.4,7.5))
m_lo=mean(c(3.7,2.9,5.3))
m_up=mean(c(6.0,6.7,19))

sd=mean(c(2.9,3.0,2.2))
sd_sd=mean(c(2.9,3.0,2.2))
sd_lo=mean(c(1.9,1.8,1.4))
sd_up=mean(c(4.9,5.8,3.9))
res <- estimate_R(incidence_cl_format, method = "uncertain_si",
                  config = make_config(list(
                    mean_si = m, 
                    std_mean_si = m_sd,
                    min_mean_si = m_lo, 
                    max_mean_si = m_up,
                    std_si = sd, 
                    std_std_si = sd_sd,
                    min_std_si = sd_lo, 
                    max_std_si = sd_up,
                    n1 = 2500, n2 = 2500)))
#represent infectiousness over time 
#This figure shows the global force of infection over time
#Note that the vertical scale for the bars is arbitrary, and only represents the relative force of infection
#eSTIMATED MABDA FOR chile (asSSUMING SERIAL INTERVAL MEAN , SD)

#https://rdrr.io/github/reconhub/earlyR/src/R/plot.earlyR.R

plot(res,what = "R")+ ggtitle("Figure 15. Estimated R0")+
  theme(panel.background = element_rect(fill = "white")) +  
  labs(subtitle= "Assuming uncertain SI's, in range of literature", y="Basic Reproduction Number (R0)")+#para que nos e vea el fondo
  #  scale_x_date(labels = scales::date_format("%b %Y"))
  sjPlot::theme_sjplot2() +
  theme(legend.position="bottom") +
  theme(plot.title = element_text(color="darkblue"))

#plot(res, "lambdas", scale = length(lubridate::parse_date_time(casos$Fecha,"dmY")) + 1)
#abline(v = lubridate::parse_date_time(casos$Fecha,"dmY"), lwd = 3, col = "grey")
#abline(v = today, col = "blue", lty = 2, lwd = 2)
#points(lubridate::parse_date_time(casos$Fecha,"dmY"), seq_along(lubridate::parse_date_time(casos$Fecha,"dmY")), pch = 20, cex = 3) 
# labs(title = "Force of infection over time",
#               x = "Days",
#               y = "ylab")

#R: a dataframe containing: the times of start and end of each time window considered ; the posterior mean, std, and 0.025, 0.05, 0.25, 0.5, 0.75, 0.95, 0.975 quantiles of the reproduction number for each time window.
#
#method: the method used to estimate R, one of "non_parametric_si", "parametric_si", "uncertain_si", "si_from_data" or "si_from_sample"
#
#si_distr: a vector or dataframe (depending on the method) containing the discrete serial interval distribution(s) used for estimation
#
#SI.Moments: a vector or dataframe (depending on the method) containing the mean and std of the discrete serial interval distribution(s) used for estimation
#https://github.com/annecori/EpiEstim/tree/master/R
#https://rdrr.io/cran/EpiEstim/man/estimate_R.html
#https://www.ijidonline.com/action/showPdf?pii=S1201-9712%2820%2930119-3
#https://github.com/reconhub/earlyR/
#https://cran.r-project.org/web/packages/epiR/epiR.pdf
#https://timchurches.github.io/blog/posts/2020-03-01-analysing-covid-19-2019-ncov-outbreak-data-with-r-part-2/#modelling-epidemic-trajectory-in-hubei-province-using-log-linear-models
#https://cran.r-project.org/web/packages/EpiEstim/EpiEstim.pdf
#https://rdrr.io/cran/EpiEstim/src/R/estimate_R.R
# https://www.rdocumentation.org/packages/EpiEstim/versions/2.2-1/topics/estimate_R
#https://www.repidemicsconsortium.org/earlyR/articles/earlyR.html
#https://timchurches.github.io/blog/posts/2020-02-18-analysing-covid-19-2019-ncov-outbreak-data-with-r-part-1/#estimating-changes-in-the-effective-reproduction-number
#https://www.repidemicsconsortium.org/projections/

The last estimated reproduction number of an epidemic, given the incidence time series and the serial interval distribution, is around 1.09 (1.06- 1.14), which means that, each person with COVID-19 would, on average, infect 1.09 other people in a totally susceptible population. However, this index is starting to decrease, so we may take into account that simulations are based on the trend until a given time (10-06-2020).

Simulations of Possible Trajectories

We made around 100,000 simulations by determining each serial interval to proyect the cumulative incidence in 60 days.

cl_incidence_2_pred1 <- incidence::incidence(as.Date(as.character(casos_sin_recuperados$Fecha)))
#tuve q omitirla
cl_incidence_2_pred1 <- bootstrap(cl_incidence_2_pred1)

library(ggplot2)
library(projections)
set.seed(2754)
pred1 <- project(cl_incidence_2_pred1, R = res$R$"Mean(R)"[length(res$R$"Mean(R)")], si = growth_R0, n_days = 60, n_sim = 100000)
pred2 <- project(cl_incidence_2_pred1, R = res$R$"Mean(R)"[length(res$R$"Mean(R)")], si = growth_R00, n_days = 60, n_sim = 100000)
pred3 <- project(cl_incidence_2_pred1, R = res$R$"Mean(R)"[length(res$R$"Mean(R)")], si = growth_R02, n_days = 60, n_sim = 100000)

#agregar el intercept
cum_pred1_mean<- apply(cumulate(pred1),1,mean)
cum_pred2_mean<- apply(cumulate(pred2),1,mean)
cum_pred3_mean<- apply(cumulate(pred3),1,mean)

cum_pred1_lo<-apply(cumulate(pred1), 1, quantile,probs=c(.025,.975))[1,]
cum_pred2_lo<-apply(cumulate(pred2), 1, quantile,probs=c(.025,.975))[1,]
cum_pred3_lo<-apply(cumulate(pred3), 1, quantile,probs=c(.025,.975))[1,]
cum_pred1_up<-apply(cumulate(pred1), 1, quantile,probs=c(.025,.975))[2,]
cum_pred2_up<-apply(cumulate(pred2), 1, quantile,probs=c(.025,.975))[2,]
cum_pred3_up<-apply(cumulate(pred3), 1, quantile,probs=c(.025,.975))[2,]

num1=as.numeric(cum_pred1_mean[60])
num2=as.numeric(cum_pred2_mean[60])
num3=as.numeric(cum_pred3_mean[60])
#export proyections
#df1 <-as.data.table(cbind(M=apply(pred, 1, mean),
#              lo=apply(pred, 1, range)[1,],
#              up=apply(pred, 1, range)[2,]),keep.rownames = T) %>% dplyr::rename("date"="rn")
#ggplot(df1, aes(x = date, y = M)) +
#  geom_jitter(alpha = .3) + geom_smooth()
library(ggpubr)

library(gridExtra)
plot1<- plot(cumulate(pred1)+cumulate(cl_incidence_2_pred1)$n) + ggtitle( "Li, et al.")+ theme(axis.title.y = element_blank())
plot2<- plot(cumulate(pred2)+cumulate(cl_incidence_2_pred1)$n)+ ggtitle( "Nishiura, et al.") +theme(axis.title.y = element_blank())
plot3<- plot(cumulate(pred3)+cumulate(cl_incidence_2_pred1)$n)+ ggtitle( "Zhao, et al.")+ theme(axis.title.y = element_blank())
mezcla<-ggpubr::ggarrange(plot1, plot2, plot3, nrow=3,
                          label.y="Predictive Cumulative Incidence",
                          common.legend = TRUE, legend="right")
ggpubr::annotate_figure(mezcla,
                        top = ggpubr::text_grob("Figure 16. Projected Cum. Incidence Assuming Different SIs", color = "darkblue", size = 14))

Figure 16 shows that in the first (Li et al.) of the three proposed escenarios, we could get around 281,513 (CI95% 280525-282499) confirmed cases with COVID-19 in the next 60 days. In the second,we could get around 281,602 (CI95% 280612-282586) confirmed cases with COVID-19 in the next 60 days. In the third, we could get around 281,628 (CI95% 280639-282621) confirmed cases with COVID-19 in the next 60 days.

SIR Models

We set the population for Chile in 18,730,000 people. Also, we created the vector Infected which takes in the info from the coronavirus data. SIR (Susceptible-Infected-Recovered) models, the dynamics of the outbreak and uses three differential equations, in order to do so: dS, dI, and dR, you can think of them as the rate of change for Susceptibles, Infected and Recovered for a given t. Beta is the parameter that controls the transition between Susceptibles and Infected and Gamma controls the transition between Infected and Recovered. These models assumes that the infection is quite short, so the population does not have enough time to change (1), There is a constant rate in infected and contacts (2), and elimination rate (recovery/death) is constant over time, We used a parameter of deaths of 1.67%, as stated above.

library(deSolve)
c1 <- coronavirus %>%
  #dplyr::filter(country == "Chile") %>%
  select(date,type,cases) %>%
  dplyr::group_by(date,type) %>%
  summarise(total_cases = sum(cases))
c2 <- c1[which(c1$type=="confirmed" ),]
c2$cumtotal_cases <- cumsum(c2$total_cases)

pop <-18730000 #población chile
Infected <- as.integer(c2$cumtotal_cases)
Dia <- 1:length(Infected)
SIR <- function(time, state, parameters) {
  par <- as.list(c(state, parameters))
  with(par, {
    dS <- -beta/pop * I * S
    dI <- beta/pop * I * S - gamma * I
    dR <- gamma * I
    list(c(dS, dI, dR))
  })
}

init <- c(S = pop-Infected[1], I = Infected[1], R = 0)
RSS <- function(parameters) {
  names(parameters) <- c("beta", "gamma")
  out <- ode(y = init, times = Dia, func = SIR, parms = parameters)
  fit <- out[ , 3]
  sum((Infected - fit)^2)
}

Opt <- optim(c(0.5, 0.5), RSS, method = "L-BFGS-B", lower = c(0, 0), upper = c(1, 1)) # optimize with some sensible conditions
Opt_par <- setNames(Opt$par, c("beta", "gamma"))
#Opt_par
t <- 1:150 # time in days
fit <- data.frame(ode(y = init, times = t, func = SIR, parms = Opt_par))

R0 <- setNames(Opt_par["beta"] / Opt_par["gamma"], "R0")
Height <- fit[fit$I == max(fit$I), "I", drop = FALSE] # height of pandemic
Max_Dead <- max(fit$I) * (dim(casos_muertos)[1]/dim(casos_sin_recuperados)[1]) # max deaths with supposed 2% mortality rate
require(scales)

days_passed_chile_max<-coronavirus_plot_tiempo_por_paises_prim_caso %>%
  dplyr::group_by(country) %>% 
  dplyr::filter(country=="Chile", days_passed==max(days_passed)) %>%
  dplyr::select(days_passed) %>% as.numeric()

graph11 <- fit %>% gather(key, value, -time)
bigplot <- ggplot(graph11, mapping = aes(x = time, y = value, color = key) ) +   
  geom_line(size =1.25)+  
  scale_color_manual( values = 
                        c("darkred", "darkolivegreen3","cadetblue4"))+ 
  theme(
    plot.title = element_text(size = 12, face = "bold",hjust = 0.5),
    plot.caption = element_text(size = 8, face = "italic"),
    legend.position="top",
    legend.title = element_blank(),
    legend.box = "horizontal" ,
    legend.text=element_text(size=8.5),
    panel.grid.minor = element_blank(), 
    panel.grid.major = element_line(color = "gray50", size = 0.5),
    panel.grid.major.x = element_blank(),
    panel.background = element_blank(),
    line = element_blank(),
    axis.ticks.length = unit(.15, "cm"),
    axis.ticks.y = element_blank(),
    axis.title.x = element_text(color="black", 
                                size=12),
    axis.title.y = element_text(color="black",
                                size=10,
                                face="italic"))+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = days_passed_chile_max) +
    scale_y_log10(labels = scales::comma) +

#  scale_y_continuous(expand = c(0, 0),
 ##                   breaks=seq(0.0,50000000,5000000), 
   #                  labels = scales::unit_format(unit = "M", scale = 1e-6),
    #                 name = "Number of subjects (in Millions)")+
  
  scale_x_continuous(expand = c(0, 0),
                     name = "Days")+
  labs(title = "Figure 17a.SIR Model 2019-nCov for Chilean population",
       subtitle= "Based on Worldwide dynamics", 
        y = "People (Log)",
       caption = "Info taken from RamiKrispin. Adapted model from Learning Machines.\nObtained from: https://medium.com/@daniel.pena.chaves/simple-coronavirus-model-using-r-cf6b1bc93949\n S=Susceptible, I=Infected,R=Recovered; Vertical Line= Last date of reported data")+
  theme(legend.position="bottom") +
  theme(plot.title = element_text(color="darkblue"))+
  theme(plot.caption = element_text(hjust = 0, face= "italic"))
bigplot

It assumes an R0, or the number of healthy people that get infected per number of infected people, of , the height is at day 150 with 13,173,252 infected, and a maximum of 219,560 dead. The transition between Susceptibles and Infected is around 0.08 and the transition between Infected and Recovered is around 0.

#https://www.statsandr.com/blog/covid-19-in-belgium/

# devtools::install_github("RamiKrispin/coronavirus")
`%>%` <- magrittr::`%>%`

# extract the cumulative incidence
df <- coronavirus %>%
  dplyr::filter(country == "Chile") %>%
  dplyr::group_by(date, type) %>%
  dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::arrange(date) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(active = confirmed - death - recovered) %>%
  dplyr::mutate(
    confirmed_cum = cumsum(confirmed),
    death_cum = cumsum(death),
    recovered_cum = cumsum(recovered),
    active_cum = cumsum(active)
  )

# put the daily cumulative incidence numbers for Belgium from
# Feb 4 to March 30 into a vector called Infected
library(lubridate)

days_passed_chile_max_char<-coronavirus_plot_tiempo_por_paises_prim_caso %>%
  dplyr::group_by(country) %>% 
  dplyr::filter(country=="Chile", days_passed==max(days_passed)) %>%
  dplyr::select(date_posixct) %>% dplyr::mutate(date_posixct=as.character(as.Date(date_posixct))) %>% 
  ungroup()%>% data.frame() %>% unlist()%>% as.character() %>% unlist()
days_passed_chile_min_char<-coronavirus_plot_tiempo_por_paises_prim_caso %>%
  dplyr::group_by(country) %>% 
  dplyr::filter(country=="Chile", days_passed==min(days_passed)) %>%
  dplyr::select(date_posixct) %>% dplyr::mutate(date_posixct=as.character(as.Date(date_posixct))) %>% 
  ungroup()%>% data.frame() %>% unlist()%>% as.character() %>% unlist()


sir_start_date <- days_passed_chile_min_char
sir_end_date <- days_passed_chile_max_char
sir_start_date <- as.vector(sir_start_date)[2]
sir_end_date <-as.vector(sir_end_date)[2]

Infected <- subset(df, date >= ymd(sir_start_date) & date <= ymd(sir_end_date))$active_cum

# Create an incrementing Day vector the same length as our
# cases vector
Day <- 1:(length(Infected))

# now specify initial values for N, S, I and R
N <- 18730000
init <- c(
  S = N - Infected[1],
  I = Infected[1],
  R = 0
)

# define a function to calculate the residual sum of squares
# (RSS), passing in parameters beta and gamma that are to be
# optimised for the best fit to the incidence data
RSS <- function(parameters) {
  names(parameters) <- c("beta", "gamma")
  out <- ode(y = init, times = Day, func = SIR, parms = parameters)
  fit <- out[, 3]
  sum((Infected - fit)^2)
}

# now find the values of beta and gamma that give the
# smallest RSS, which represents the best fit to the data.
# Start with values of 0.5 for each, and constrain them to
# the interval 0 to 1.0

# install.packages("deSolve")
library(deSolve)

Opt <- optim(c(0.5, 0.5),
             RSS,
             method = "L-BFGS-B",
             lower = c(0, 0),
             upper = c(1, 1)
)

# check for convergence
invisible(Opt$message)

#The absolute value introduces a singularity: you may want to use a square instead, especially for gradient-based methods (such as L-BFGS).
#The denominator of your function can be zero.
#The fact that the parameters appear in products and that you allow them to be (arbitrarily close to) zero can also cause problems.

Opt_par <- setNames(Opt$par, c("beta", "gamma"))
invisible(Opt_par)


# time in days for predictions
t <- 1:as.integer(ymd(sir_end_date) + 1 - ymd(sir_start_date))

# get the fitted values from our SIR model
fitted_cumulative_incidence <- data.frame(ode(
  y = init, times = t,
  func = SIR, parms = Opt_par
))

# add a Date column and the observed incidence data
library(dplyr)
fitted_cumulative_incidence <- fitted_cumulative_incidence %>%
  mutate(
    Date = ymd(sir_start_date) + days(t - 1),
    Country = "Belgium",
    cumulative_incident_cases = Infected
  )

R0 <- as.numeric(Opt_par[1] / Opt_par[2])

invisible(R0)

# time in days for predictions
t <- 1:150

# get the fitted values from our SIR model
fitted_cumulative_incidence <- data.frame(ode(
  y = init, times = t,
  func = SIR, parms = Opt_par
))

# add a Date column and join the observed incidence data
fitted_cumulative_incidence <- fitted_cumulative_incidence %>%
  mutate(
    Date = ymd(sir_start_date) + days(t - 1),
    Country = "Belgium",
    cumulative_incident_cases = c(Infected, rep(NA, length(t) - length(Infected)))
  )

# plot the data
sir_corrected<-fitted_cumulative_incidence %>%
  ggplot(aes(x = Date)) +
  geom_line(aes(y = I, colour = "red"),size=1) +
  geom_line(aes(y = S, colour = "black"),size=1) +
  geom_line(aes(y = R, colour = "green"),size=1) +
  geom_point(aes(y = cumulative_incident_cases, colour = "blue")) +
  scale_y_log10(labels = scales::comma) +
  labs(
    y = "People (Log)",
    title = "Figure 17b. SIR Model 2019-nCov for Chilean population",
    subtitle= "Based on Chilean dynamics",
    caption = "Note. Vertical Line= Last reported data; Adapted model from https://www.statsandr.com/blog/covid-19-in-belgium/")+
  scale_colour_manual(
    name = "",
    values = c(red = "darkred", black = "cadetblue4", green = "darkolivegreen3", blue = "black"),
    labels = c("Susceptible", "Observed", "Recovered", "Infectious")
  ) +
  scale_x_date(date_breaks = "1 month", date_minor_breaks = "1 week", date_labels = "%b/%y")+
  sjPlot::theme_sjplot2() +
  theme(plot.title = element_text(color="darkblue", size=13.5))+
  theme(legend.position="bottom",
        panel.grid.minor = element_blank(), 
        panel.grid.major = element_line(color = "gray50", size = 0.3),
        panel.grid.major.x = element_blank(),
        panel.background = element_blank())+
  geom_vline(xintercept = as.Date(sir_end_date))+
  guides(size=F)

fit <- fitted_cumulative_incidence

# peak of pandemic
invisible(fit[fit$I == max(fit$I), c("Date", "I")])

# severe cases
max_infected <- max(fit$I)

# máximos muertos
invisible(max_infected* (dim(casos_muertos)[1]/dim(casos_sin_recuperados)[1]))

print(sir_corrected)

The estimated reproduction number (R0) in this model is estimated in 1.25. The possible peak of the pandemic would come in 2020-07-11 with an amount of 400,424 people infected. The maximum amount of deaths can be estimated in 6,674 people. The transition between Susceptibles and Infected is around 0.56 and the transition between Infected and Recovered is around 0.44.

Mobility Reports from Chile

#install.packages('gsheet')
library(gsheet)
google_date<-gsheet2tbl('https://docs.google.com/spreadsheets/d/1Kd8SLHFLohGKCuJHa3cxbhkTH9691BCd_1ryCKUM_UA/edit#gid=398361765')

## Warning: Missing column names filled in: 'X1' [1], 'X2' [2], 'X3' [3], 'X4' [4]

library(stringr)

hora_google<-substr(google_date[7,],unlist(str_locate_all(pattern ='[0-9][0-9].', google_date[7,]))[1],unlist(str_locate_all(pattern ='[0-9][0-9].', google_date[7,]))[1]+9)

We scrapped the data report from google generated at 2020-06-09 from this link. Apple mobility reports are available from this link. This approach was applied by Joachim Gassen (https://github.com/joachim-gassen/tidycovid19). Apple trends are understood as percentage x 100 of changes in each term relative to the baseline of Jan 13, 2020. Google Community Mobility Reports data for the frequency that people visit retail and recreation places expressed as a percentage x 100 change relative to the baseline period Jan 3 to Feb 6, 2020.

#bitmaps <- tidycovid19:::extract_line_graph_bitmaps("https://www.gstatic.com/covid19/mobility/2020-04-05_CL_Mobility_Report_en.pdf", 1)
#png_file <- tempfile("bitmap_", fileext = ".png")
#writePNG(bitmaps[[1]][[1]], "bitmap.png")
#df <- tidycovid19:::parse_line_graph_bitmap(bitmaps[[1]][[1]])

merged_dta <- download_merged_data(cached = TRUE, silent = TRUE)

merged_dta %>% 
  dplyr::filter(iso3c == "CHL", date >= "2020-03-03") %>%
  dplyr::mutate(gov_interventions = (soc_dist + mov_rest)/
                  max(soc_dist + mov_rest, na.rm = TRUE),
                lockdown = lockdown == 1) %>%
  dplyr::select(date, lockdown, starts_with("gcmr_"), starts_with("apple_")) %>%
  dplyr::select(-apple_mtr_transit)%>%
  tidyr::pivot_longer(cols = c("gcmr_retail_recreation","gcmr_grocery_pharmacy","gcmr_parks","gcmr_transit_stations","gcmr_workplaces","gcmr_residential","apple_mtr_driving","apple_mtr_walking"), names_to = "measure", values_to = "value") %>%
  na.omit() -> dta

merged_dta %>% 
  dplyr::filter(iso3c == "CHL", date < "2020-03-03") %>%
  dplyr::mutate(gov_interventions = (soc_dist + mov_rest)/
                  max(soc_dist + mov_rest, na.rm = TRUE),
                lockdown = lockdown == 1) %>%
  dplyr::select(date, lockdown, starts_with("gcmr_"), starts_with("apple_")) %>%
  dplyr::select(-apple_mtr_transit)%>%
  tidyr::pivot_longer(cols = c("gcmr_retail_recreation","gcmr_grocery_pharmacy","gcmr_parks","gcmr_transit_stations","gcmr_workplaces","gcmr_residential","apple_mtr_driving","apple_mtr_walking"), names_to = "measure", values_to = "value") %>%
  dplyr::filter(measure=="apple_mtr_driving"|measure=="apple_mtr_walking")%>%
  dplyr::group_by(measure) %>%   dplyr::summarise(mean=mean(value,na.rm=T),median=quantile(value,.5,na.rm=T),n=n()) -> dta_apple 

dta<- dta %>%
  dplyr::mutate(value=ifelse(measure=="apple_mtr_driving",((value-dta_apple$mean[1])/dta_apple$mean[1])*100,value))%>%
  dplyr::mutate(value=ifelse(measure=="apple_mtr_walking",((value-dta_apple$mean[2])/dta_apple$mean[2])*100,value))%>%
  dplyr::mutate(label=recode(measure, `gov_interventions` = "Intervent",
                             `gcmr_retail_recreation` = "Retail",
                             `gcmr_grocery_pharmacy` = "Grocery",
                             `gcmr_parks` = "Parks",
                             `gcmr_transit_stations` = "Stat",
                             `gcmr_workplaces` = "Workpl",
                             `gcmr_residential` = "Resid",
                             `apple_mtr_driving` = "Driven",
                             `apple_mtr_walking` = "Walked"
  ),
  measure=recode(measure, `gov_interventions` = "Goverment Interventions",
                 `gcmr_retail_recreation` = "Retail & recreation",
                 `gcmr_grocery_pharmacy` = "Grocery & pharmacy",
                 `gcmr_parks` = "Parks",
                 `gcmr_transit_stations` = "Transit stations",
                 `gcmr_workplaces` = "Workplaces",
                 `gcmr_residential` = "Residential",
                 `apple_mtr_driving` = "Meters Driven (Apple)",
                 `apple_mtr_walking` = "Meters Walked (Apple)"
  ))

dta$tooltip <- paste0(dta$label,"=",paste0(round(dta$value),"%"))
gg_google <- 
  #ggplot(dta, aes(x = date, y = value, group = measure, color = measure)) + 
  ggplot(dta,aes( hover_css = "fill:none;")) +#, ) +
  theme_minimal() +
  annotate("rect", xmin = min(dta$date[dta$lockdown]), xmax = max(dta$date), 
           ymin = -Inf, ymax = Inf, fill = "lightblue", color = NA, alpha = 0.2) +
  ggiraph::geom_point_interactive(aes(x = date,y = value, color=measure, tooltip=tooltip),size = .75) +
  scale_y_continuous(limits = c(-100,100)) +
  geom_line(aes(x = date, y = value, group = measure, color = measure),size=.9)   +
  labs(caption= "Note. value= Percentages of increase or decrease of mobility compared to the baseline\nShaded area: Date of Lockdown Policy from GOVERMENT MEASURES DATASET (ACAPS);\n Values could be lagged because of Data is from approximately 2-3 days prior.", title="Figure 18. Mobility Trends From Mobility Reports") +
  theme(legend.position="bottom") +
  scale_colour_ochre(name= "Measures",
                     palette="emu_woman_paired") +
  theme(plot.title = element_text(color="darkblue"))+
  labs(y=NULL, x=NULL)+
  theme(legend.text = element_text(size=7))+
  theme(legend.title = element_text(size=9))+
  theme(plot.caption = element_text(hjust = 0, face= "italic"))

tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg_google)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .65 )
x <- girafe(ggobj = gg_google)
x <- girafe_options(x,
                    opts_zoom(min = 1, max = 3) )

#The dots represents the lockdowns policies in the GOVERNMENT MEASURES DATASET available in this  [link](https://www.acaps.org/covid19-government-measures-dataset).

#fecha de cuarentena 
#https://www.minsal.cl/ministro-de-salud-anuncio-cuarentena-total-para-siete-comunas-de-la-rm/

x

#save.image(pasteo("G:/Mi unidad/covid19/prueba",format(Sys.time()-3600*24, '%Y_%m_%d'),".RData"))
save.image("G:/Mi unidad/covid19/prueba.RData")

Unfortunately and as said earlier, apple trends had a baseline of only one day, so we constructed a robust baseline using the mean of trends previous to our first case (2020-03-03) as a reference. These percentages must be interpreted as the part of the mean before the first diagnosed case in Chile that has increased/decreased from the same average meters driven/walked (115.68 and 97.52 meters, respectively).

As seen in Figure 18, mobility trends started decreasing since March 15, while policies were implemented since March 26 (more information on this link). The trends that diminished less are those related to grocery and pharmacy and workplaces, while the residential trend incremented from the baseline. One interesting fact is that mobility to parks decreased from last days of february, possibly due to other factors, such as the beginning of the school year.

Link to Control Methods… In construction

Academic Resources

• Churches (2020, March 1). Tim Churches Health Data Science Blog: Analysing COVID-19 (2019-nCoV) outbreak data with R - part 2. Retrieved from https://timchurches.github.io/blog/posts/2020-03-01-analysing-covid-19-2019-ncov-outbreak-data-with-r-part-2/
• Cori, A. et al. (2013). A new framework and software to estimate time-varying reproduction numbers during epidemics. American Journal of Epidemiology, 178(9): 1505–1512. doi: 10.1093/aje/kwt133
• Szklo, M., & Nieto, F. (2007). Epidemiology: Beyond the Basics (2nd Edition ed.). Boston: Jones and Bartlett Publishers.
• Siegel, E. (2020, March 17). Why ‘Exponential Growth’ Is So Scary For The COVID-19 Coronavirus. Forbes. https://www.forbes.com/sites/startswithabang/2020/03/17/why-exponential-growth-is-so-scary-for-the-covid-19-coronavirus/#56ee866b4e9b
• Nishiura, H., Linton, N. M., & Akhmetzhanov, A. R. (2020). Serial interval of novel coronavirus (COVID-19) infections. International Journal of Infectious Diseases. doi:10.1016/j.ijid.2020.02.060
• Zhao, S., et al. (In press). Estimating the serial interval of the novel coronavirus disease (COVID-19): A statistical analysis using the public data in Hong Kong from January 16 to February 15, 2020. medRxiv 2020.02.21.20026559; doi: https://doi.org/10.1101/2020.02.21.20026559
• Li, et al. (2020). Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia. The New England Journal of Medicine. doi: 10.1056/NEJMoa2001316