Brazilian Surveillance of COVID-19

Ministry of Health Database

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LINKS ÚTEIS

• WHO Dashboard
• JHC Dashboard
• Havard Dashboard
• COVID-19 BR Dashboard
• WHO Literature Database
• NCBI Literature Database
• Lancet Resources
• NEJM Resources
• BMJ Resources
• IVIS Platform
• COVID-19 BR Data
• COVID-19 BR Data
• COVID-19 Global Data
• COVID-19 Global Data
• R Tips

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1. CARREGAR PACOTES

library(tidyverse)
library(data.table)
library(lubridate)
library(hablar)
library(openxlsx)
library(forecast)
#library(coronabr)
library(brazilmaps)
library(sf)
library(leaflet)
library(xts)
library(dygraphs)
#library(fable)

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2. IMPORTAR DADOS

dados = "http://plataforma.saude.gov.br/novocoronavirus/resources/scripts/database.js" %>%
  readr::read_file() %>%
  stringr::str_remove("var database=") %>%
  jsonlite::fromJSON() %>%
  purrr::pluck("brazil") %>%
  dplyr::mutate(values = purrr::map(values, dplyr::mutate_all, as.character)) %>%
  tidyr::unnest(values) %>%
  dplyr::mutate(date = lubridate::dmy(date))

dados = fread("C:/Users/Ronaldo Alves/Desktop/COVID_19/coronavirus2.csv")

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3. CURADORIA DOS DADOS

uid =  data.frame(uid = c(11:17,21:29,31:33,35,41:43,50:53))
uid = setDT(uid)[, list(date = seq(min(ymd(dados$date)), max(ymd(dados$date)), by = "days")),
                 by = uid]

dados = dados %>% 
  mutate(date = ymd(date)) %>% # uid = as.factor(uid)
  full_join(uid, by = c("uid" = "uid", "date" = "date")) %>% 
  group_by(uid, date) %>% 
  summarise_all(sum_) %>% 
  mutate(id_date = dense_rank(date)) %>% 
  arrange(uid, date) %>% 
  ungroup() %>% 
  mutate_if(is.numeric, ~replace_na(., 0)) %>% 
  mutate(sigla = case_when(uid == 11 ~ "RO", uid == 12 ~ "AC", uid == 13 ~ "AM", uid == 14 ~ "RR",
                           uid == 15 ~ "PA", uid == 16 ~ "AP", uid == 17 ~ "TO", uid == 21 ~ "MA",
                           uid == 22 ~ "PI", uid == 23 ~ "CE", uid == 24 ~ "RN", uid == 25 ~ "PB",
                           uid == 26 ~ "PE", uid == 27 ~ "AL", uid == 28 ~ "SE", uid == 29 ~ "BA",
                           uid == 31 ~ "MG", uid == 32 ~ "ES", uid == 33 ~ "RJ", uid == 35 ~ "SP",
                           uid == 41 ~ "PR", uid == 42 ~ "SC", uid == 43 ~ "RS", uid == 50 ~ "MS",
                           uid == 51 ~ "MT", uid == 52 ~ "GO", uid == 53 ~ "DF")) %>% 
  select(id_date, uid, sigla, everything())

rm(uid)
dados

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5. ANÁLISE DESCRITIVA

dados %>% 
  summarise(confirmados = sum_(cases), 
            mortes = sum_(deaths), 
            estados = n_distinct(uid))

dados_uf = dados %>% 
  group_by(uid, sigla) %>% 
  summarise(confirmados = sum_(cases)) %>% 
  ungroup()

dados_uf %>% select(sigla, confirmados) %>% arrange(-confirmados)

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6. ANÁLISE ESPACIAL

ggplot(dados_uf, aes(x = reorder(sigla, -confirmados), y = confirmados)) + 
  geom_bar(stat = "identity", color = "black", fill = "white") + 
  geom_text(aes(label = confirmados), position = "stack", size = 3, vjust = -0.25) + 
  scale_y_continuous(limits = c(0, max(dados_uf$confirmados)+100), 
                     breaks = seq(0, max(dados_uf$confirmados)+100, 250), 
                     expand = c(0.01, 0)) + 
  labs(x = "", y = "# casos confirmados") + 
  theme_minimal()

map_uf = get_brmap("State") %>% 
  left_join(dados_uf, by = c("State" = "uid")) %>% 
  st_centroid()

data.frame(st_coordinates(map_uf), 
           confirmados = map_uf$confirmados,
           UF = map_uf$nome,
           sigla = map_uf$sigla) %>%  # str_to_title()
  filter(confirmados != 0) %>% 
  leaflet() %>% 
  addTiles() %>%
  addCircleMarkers(~X, ~Y, label = ~as.character(paste0(sigla, ":", confirmados, " casos")),
                   labelOptions = labelOptions(textsize = "12px"), #noHide = T, textOnly = T, direction = "auto"
                   radius = ~log(confirmados)*2,
                   stroke = F, 
                   fillOpacity = 0.5)

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7. ANÁLISE TEMPORAL

dados %>% 
  group_by(date) %>% 
  summarise(N = sum_(cases)) %>% 
  xts(x = .$N, order.by = .$date) %>% 
  dygraph(xlab = "Date of report", ylab = "# confirmed cases") %>% 
  dyOptions(drawPoints = T, pointSize = 4, fillGraph = T, fillAlpha = 0.4) %>% 
  dyRangeSelector() %>% 
  dySeries("V1", label = "confirmed cases") %>% 
  dyLegend(show = "auto", hideOnMouseOut = FALSE)

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dados %>% 
  group_by(date) %>% 
  summarise(N = sum_(cases)) %>% 
  filter(date >= "2020-02-26") %>% 
  ggplot(aes(x = date, y = log(N))) +
  geom_point(size = 3, shape = 21, fill = "orange") + 
  geom_smooth(method = "lm", formula = y ~ x, color = "red") + 
  scale_x_date(labels = scales::date_format("%d/%b"), breaks = "1 day") + 
  labs(x = "Date of report", y = "# confirmed cases (log scale)") + 
  theme_minimal() + 
  theme(axis.text.x = element_text(angle = 90, vjust = 0.4, hjust = 0.5))

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dados %>% 
  group_by(date) %>% 
  summarise(N = sum_(cases)) %>% 
  filter(date >= "2020-02-26") %>% 
  mutate(y = log10(N + 1), days = 1:nrow(.)) %>% 
  lm(y ~ days, data = ., na.action = na.exclude) %>% 
  summary()

Call:
lm(formula = y ~ days, data = ., na.action = na.exclude)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.41457  0.01152  0.09367  0.12422  0.31453 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -0.1325     0.1536  -0.863    0.399    
days          0.1190     0.0117  10.175 2.36e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.3481 on 20 degrees of freedom
Multiple R-squared:  0.8381,    Adjusted R-squared:   0.83 
F-statistic: 103.5 on 1 and 20 DF,  p-value: 2.363e-09

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dados %>% 
  #group_by(sigla, date) %>% 
  #summarise(N = sum_(cases)) %>% 
  filter(date >= "2020-02-26", sigla %in% c("SP","RJ","DF","RS","PR","PE","MG","SC")) %>% 
  ggplot(aes(x = date, y = cases, color = reorder(sigla, -cases))) +
  geom_point(size = 3) + 
  geom_line(size = 1) + 
  scale_color_brewer(palette = "Spectral") + 
  scale_x_date(labels = scales::date_format("%d/%b"), breaks = "1 day") + 
  labs(x = "Date of report", y = "# confirmed cases", color = "UF") + 
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.4, hjust = 0.5))

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