2021/12/27
In the evening
Dinner
- Eat spaghetti
- Drink wine
A scatterplot.
Going to sleep
- Get in bed
- Count sheep
Exploring COVID-19 data
nCov2019 ( https://github.com/yulab-smu/nCov2019 ) 以地理地圖增進COVID-19流行病學數據的可視化
knitr::include_graphics("nCov2019.png")
{nCov2019}
安裝package
#remotes::install_github("yulab-smu/nCov2019", dependencies = TRUE)
Statistic query
Data query is simple as one command:
library("nCov2019")
res <- query()
## last update: 2021-12-20 ## Gloabl total 275036482 cases; and 5371168 deaths ## Gloabl total affect country or areas: 224 ## Gloabl total recovered cases: 101413 ## last update: 2021-12-20 ## Total Candidates Programs : 51 ## Total Candidates Programs : 84
Data types
names(res)
## [1] "latest" "global" "historical" "vaccine" "therapeutics"
x<- res$latest print(x)
## last update: 2021-12-20
Global
head(x["Global"]) #return all global countries
## country cases deaths recovered active todayCases todayDeaths ## 1 UK 11361387 147218 9783355 1430814 82886 45 ## 2 USA 51765714 827323 40539875 10398516 66751 109 ## 3 France 8625849 121493 7506909 997447 48473 75 ## 4 Russia 10214790 297203 8984921 932666 27967 1023 ## 5 Italy 5389155 135641 4891239 362275 24259 97 ## 6 Germany 6812736 108963 5753300 950473 24190 127 ## todayRecovered population tests updated ## 1 41501 68408203 384287490 2021-12-20 ## 2 24730 333844797 786478329 2021-12-20 ## 3 8194 65485052 178020146 2021-12-20 ## 4 32655 146026119 235100000 2021-12-20 ## 5 9403 60331591 130368736 2021-12-20 ## 6 29200 84176291 87852398 2021-12-20
x[c("USA","India")] # return only for USA and India
## country cases deaths recovered active todayCases todayDeaths ## 2 USA 51765714 827323 40539875 10398516 66751 109 ## 14 India 34746838 477554 34178940 90344 6563 132 ## todayRecovered population tests updated ## 2 24730 333844797 786478329 2021-12-20 ## 14 7469 1399865149 664109365 2021-12-20
s
x[c("USA","India")] # return only for USA and India
## country cases deaths recovered active todayCases todayDeaths ## 2 USA 51765714 827323 40539875 10398516 66751 109 ## 14 India 34746838 477554 34178940 90344 6563 132 ## todayRecovered population tests updated ## 2 24730 333844797 786478329 2021-12-20 ## 14 7469 1399865149 664109365 2021-12-20
j
df = x["Global"] head(df[order(df$cases, decreasing = T),])
## country cases deaths recovered active todayCases todayDeaths ## 2 USA 51765714 827323 40539875 10398516 66751 109 ## 14 India 34746838 477554 34178940 90344 6563 132 ## 37 Brazil 22213762 617838 21414318 181606 1419 54 ## 1 UK 11361387 147218 9783355 1430814 82886 45 ## 4 Russia 10214790 297203 8984921 932666 27967 1023 ## 7 Turkey 9171119 80415 8785600 305104 16910 171 ## todayRecovered population tests updated ## 2 24730 333844797 786478329 2021-12-20 ## 14 7469 1399865149 664109365 2021-12-20 ## 37 0 214771691 63776166 2021-12-20 ## 1 41501 68408203 384287490 2021-12-20 ## 4 32655 146026119 235100000 2021-12-20 ## 7 22858 85667433 114375538 2021-12-20
C
x = res$latest head(x$detail) # more detail data
## updated country countryInfo._id countryInfo.iso2 countryInfo.iso3 ## 1 2021-12-20 UK 826 GB GBR ## 2 2021-12-20 USA 840 US USA ## 3 2021-12-20 France 250 FR FRA ## 4 2021-12-20 Russia 643 RU RUS ## 5 2021-12-20 Italy 380 IT ITA ## 6 2021-12-20 Germany 276 DE DEU ## countryInfo.lat countryInfo.long countryInfo.flag ## 1 54.0000 -2.0000 https://disease.sh/assets/img/flags/gb.png ## 2 38.0000 -97.0000 https://disease.sh/assets/img/flags/us.png ## 3 46.0000 2.0000 https://disease.sh/assets/img/flags/fr.png ## 4 60.0000 100.0000 https://disease.sh/assets/img/flags/ru.png ## 5 42.8333 12.8333 https://disease.sh/assets/img/flags/it.png ## 6 51.0000 9.0000 https://disease.sh/assets/img/flags/de.png ## cases todayCases deaths todayDeaths recovered todayRecovered active ## 1 11361387 82886 147218 45 9783355 41501 1430814 ## 2 51765714 66751 827323 109 40539875 24730 10398516 ## 3 8625849 48473 121493 75 7506909 8194 997447 ## 4 10214790 27967 297203 1023 8984921 32655 932666 ## 5 5389155 24259 135641 97 4891239 9403 362275 ## 6 6812736 24190 108963 127 5753300 29200 950473 ## critical casesPerOneMillion deathsPerOneMillion tests testsPerOneMillion ## 1 875 166082 2152 384287490 5617564 ## 2 15741 155059 2478 786478329 2355820 ## 3 2933 131722 1855 178020146 2718485 ## 4 2300 69952 2035 235100000 1609986 ## 5 966 89326 2248 130368736 2160870 ## 6 4636 80934 1294 87852398 1043672 ## population continent oneCasePerPeople oneDeathPerPeople oneTestPerPeople ## 1 68408203 Europe 6 465 0 ## 2 333844797 North America 6 404 0 ## 3 65485052 Europe 8 539 0 ## 4 146026119 Europe 14 491 1 ## 5 60331591 Europe 11 445 0 ## 6 84176291 Europe 12 773 1 ## activePerOneMillion recoveredPerOneMillion criticalPerOneMillion ## 1 20915.82 143014.35 12.79 ## 2 31147.76 121433.30 47.15 ## 3 15231.67 114635.46 44.79 ## 4 6386.98 61529.55 15.75 ## 5 6004.73 81072.60 16.01 ## 6 11291.46 68348.22 55.07
Historical data
Z<- res$historical print(Z)
## last update: 2021-12-19
head(Z["Global"])
## country date cases deaths recovered ## 1 Afghanistan 2020-01-22 0 0 0 ## 197 Afghanistan 2020-01-23 0 0 0 ## 393 Afghanistan 2020-01-24 0 0 0 ## 589 Afghanistan 2020-01-25 0 0 0 ## 785 Afghanistan 2020-01-26 0 0 0 ## 981 Afghanistan 2020-01-27 0 0 0
Country
head(Z[c("China","UK","USA")]) #head(Z[country,province])
## country date cases deaths recovered ## 37 China 2020-01-22 548 17 28 ## 233 China 2020-01-23 643 18 30 ## 429 China 2020-01-24 920 26 36 ## 625 China 2020-01-25 1406 42 39 ## 821 China 2020-01-26 2075 56 49 ## 1017 China 2020-01-27 2877 82 58
convert
Vaccine
X <-res$ vaccine summary(X)
## phase candidates ## 1 Phase 3 10 ## 2 Phase 2/3 3 ## 3 Phase 2 2 ## 4 Phase 1/2 9 ## 5 Phase 1 13 ## 6 Pre-clinical 14
All
head(X["all"])
## id candidate ## 1 id1 BNT162 ## 2 id2 mRNA-1273 ## 3 id3 Ad5-nCoV ## 4 id4 AZD1222 ## 5 id5 CoronaVac ## 6 id6 Covaxin ## mechanism ## 1 mRNA-based vaccine ## 2 mRNA-based vaccine ## 3 Recombinant vaccine (adenovirus type 5 vector) ## 4 Replication-deficient viral vector vaccine (adenovirus from chimpanzees) ## 5 Inactivated vaccine (formalin with alum adjuvant) ## 6 Inactivated vaccine ## sponsors trialPhase ## 1 Pfizer, BioNTech Phase 3 ## 2 Moderna Phase 3 ## 3 CanSino Biologics Phase 3 ## 4 The University of Oxford Phase 3 ## 5 Sinovac Phase 3 ## 6 Bharat Biotech Phase 3 ## institutions ## 1 Multiple study sites in Europe, North America and China ## 2 Kaiser Permanente Washington Health Research Institute ## 3 Tongji Hospital ## 4 The University of Oxford,  ## 5 Sinovac Research and Development Co., Ltd. ## 6
summary
X <- res$therapeutics summary(X)
## phase candidates ## 1 Phase 3 13 ## 2 Phase 2/3/4 3 ## 3 Phase 2/3 28 ## 4 Phase 1/2 1 ## 5 Phase 2 15 ## 6 Phase 3/4 2 ## 7 No longer being studied for COVID-19 4 ## 8 Various 1 ## 9 Phase 1 4 ## 10 Phase 2b/3 2 ## 11 No longer being developed for COVID-19 1 ## 12 Phase 1/2/3 1 ## 13 Phase 1/4 1 ## 14 Phase 1b/2a 1 ## 15 Phase 4 1 ## 16 Phase 2/2 1 ## 17 Phase 1b 4 ## 18 Phase 2/4 1
head
head(X["All"])
## id medicationClass ## 1 id1 Antiviral ## 2 id2 Monoclonal antibody ## 3 id3 Monoclonal antibody ## 4 id4 IL-6 receptor agonist ## 5 id5 Monoclonal antibody ## 6 id6 Anticoagulant ## tradeName ## 1 Molnupiravir (Lagevrio, formerly known as MK-4482 and EIDD-2801) ## 2 Evusheld (tixagevimab and cilgavimab ## 3 Regkirona (regdanvimab, CT-P59) ## 4 Actemra/RoActemra (tocilizumab) ## 5 Amubarvimab and romlusevimab (formerly BRII-196 and BRII-198) ## 6 Heparin (UF and LMW) ## developerResearcher sponsors trialPhase lastUpdate ## 1 Ridgeback Biotherapeutics Ridgeback Biotherapeutics Phase 3 2020-12-10 ## 2 AstraZeneca AstraZeneca Phase 3 2020-12-10 ## 3 Celltrion Celltrion Phase 3 2020-12-10 ## 4 Roche Various Phase 3 2020-12-10 ## 5 Brii Biosciences Limited NIAID Phase 3 2020-12-10 ## 6 NHLBI Operation Warp Speed Phase 2/3/4 2020-12-10
ID
X[ID="id1"]
## [1] "Background: Molnupiravir (Lagevrio, formerly known as MK-4482 and EIDD-2801) is an oral broad-spectrum antiviral that has shown effectiveness against infections such as influenza, chikungunya, Ebola and equine encephalitis. It has a similar mechanism of action to remdesivir and prevents replication of the virus. In animal models, molnupiravir inhibited the replication of SARS-CoV-2 and MERS in mice, SARS-CoV-2 in Syrian hamsters, and blocks transmission of SARS-CoV-2 in ferrets, according to preclinical papers. Regulatory actions: Australia: Provisional determination status has been granted. Bangladesh: Bangladesh has authorized the use of molnupiravir and is in the process of authorizing generic manufacturers to supply the drug to its citizens.Canada: Merck Canada has initiated a rolling submission. EU: EMA started a rolling review of molnupiravir as of 25 October and noted that CHMP will provide EU-wide recommendations for early use of the treatment prior to authorization, given increasing case numbers in the region. On 23 November, EMA received an application for marketing authorization of molnupiravir under the name Lagrevrio. The European Commission has listed molnupiravir in its portfolio of ten most promising COVID-19 therapeutics. UK: On 4 November, MHRA approved molnupiravir for use in patients with mild or moderate COVID-19 at high risk of developing severe disease. US: Merck and Ridgeback Bio have applied for an EUA; an advisory committee recommended FDA authorize molnupiravir for emergency use in a 13-10 vote. Trials: A Phase 1 trial of 130 participants who received molnupiravir or placebo has been completed (NCT04392219) as has a Phase 2a safety trial (NCT04405570). A Phase 2/3 trial, the MOVe-IN trial of hospitalized adults (NCT04575584) was terminated, while its outpatient version, MOVe-OUT (NCT04575597) is active, but no longer recruiting. A trial to assess molnupiravir's ability to reduce viral shedding of SARS-CoV-2 (NCT04405739) continues. The Phase 3 MOVe-AHEAD trial, evaluating molnupiravir as post-exposure prophylaxis for individuals living with a person who has tested positive for COVID-19, is currently recruiting (NCT04939428). A generics manufacturer producing and supplying molnupiravir for India, Hetero, is conducting Phase 3 open-label studies on patients with mild and moderate COVID-19.Outcomes: Merck announced results from the Phase 3 MOVe-OUT trial in October 2021, which showed a 50% reduction in hospitalization for non-hospitalized patients with mild or moderate COVID-19 who received the drug. Further results from MOVe-OUT, announced on 26 November, lowered the drug's effectiveness at reducing the risk of hospitalization to 30%. Hetero, a manufacturer that has entered a non-exclusive licensing agreement with Merck regarding molnupiravir, announced results from their own Phase 3 open-label study that showed taking molnupiravir resulted in early clinical improvement and improved median time to clinical improvement compared with standard of care. Early phase results, including from the Phase 1 trial published in Antimicrobial Agents and Chemotherapy, showed molnupiravir was safe and well-tolerated in humans. Results from a Phase 2a trial of a secondary outcome in 202 participants, presented at the CROI meeting, showed a significant reduction in negative SARS-CoV-2 viral culture at 5 days compared with the placebo group. Status: Merck and Ridgeback Biotherapeutics announced on 15 April that they will proceed with the Phase 3 portion of the MOVe-OUT trial, but would not be continuing with the MOVe-IN trial after an analysis revealed molnupiravir was not effective for hospitalized adults with COVID-19. Pending an EUA, the US government has agreed to purchase 1.7 million 5-day treatment courses of molnupiravir for $1.2 billion. Other countries have also taken steps to procure molnupiravir following positive Phase 3 results, such as Australia, Malaysia, South Korea, and Thailand."
Visualization
Plot
X <- res$latest plot(X)
## x
X <- res$latest plot(X)
Green
plot(X, type = "tests", palette="Green")
## ggplot It could be also intuitively compare the number of new confirmed cases per day among different countries.
library(ggplot2) library(dplyr)
ggplot
X <- res$historical
tmp <- X["global"] %>%
group_by(country) %>%
arrange(country,date) %>%
mutate(diff = cases - lag(cases, default = first(cases))) %>%
filter(country %in% c("Australia", "Japan", "Italy", "Germany", "China"))
ggplot(tmp,aes(date, log(diff+1), color=country)) + geom_line() +
labs(y="Log2(daily increase cases)") +
theme(axis.text = element_text(angle = 15, hjust = 1)) +
scale_x_date(date_labels = "%Y-%m-%d") +
theme_minimal()
plot
Y <- res$historical plot(Y, region="Global" ,date = "2020-08-01", type="cases")
## Animations plot
Other plots
library(ggplot2)
x <- res$historical
d = x['Japan' ] # you can replace Anhui with any province
d = d[order(d$cases), ]
ggplot(d,
aes(date, cases)) +
geom_col(fill = 'firebrick') +
theme_minimal(base_size = 14) +
xlab(NULL) + ylab(NULL) +
scale_x_date(date_labels = "%Y/%m/%d") +
labs(caption = paste("accessed date:", max(d$date)))
library("dplyr")
library("ggrepel")
x <- res$latest
y <- res$historical
country_list = x["global"]$country[1:10]
y[country_list] %>%
subset( date > as.Date("2020-10-01") ) %>%
group_by(country) %>%
arrange(country,date) %>%
mutate(increase = cases - lag(cases, default = first(cases))) -> df
ggplot(df, aes(x=date, y=increase, color=country ))+
geom_smooth() +
geom_label_repel(aes(label = paste(country,increase)),
data = df[df$date == max(df$date), ], hjust = 1) +
labs(x=NULL,y=NULL)+
theme_bw() + theme(legend.position = 'none')
library('tidyr')
library('ggrepel')
library('ggplot2')
y <- res$historical
country = "India"
y[country] -> d
d <- gather(d, curve, count, -date, -country)
ggplot(d, aes(date, count, color = curve)) + geom_point() + geom_line() +
labs(x=NULL,y=NULL,title=paste("Trend of cases, recovered and deaths in", country)) +
scale_color_manual(values=c("#f39c12", "#dd4b39", "#00a65a")) +
theme_bw() +
geom_label_repel(aes(label = paste(curve,count)),
data = d[d$date == max(d$date), ], hjust = 1) +
theme(legend.position = "none",
axis.text = element_text(angle = 15, hjust = 1)) +
scale_x_date(date_labels = "%Y-%m-%d")
library('tidyr')
library('ggrepel')
library('ggplot2')
y <- res$historical
d <- y["global"]
d <- d[d$cases > 0,]
length(unique(d$country))
## [1] 196
d <- subset(d,date <= as.Date("2020-3-19"))
max_time <- max(d$date)
min_time <- max_time - 7
d <- d[d$date >= min_time,]
dd <- d[d$date == max(d$date,na.rm = TRUE),]
d$country <- factor(d$country,
levels=unique(dd$country[order(dd$cases)]))
breaks = c(0,1000, 10000, 100000, 10000000)
ggplot(d, aes(date, country)) +
geom_tile(aes(fill = cases), color = 'black') +
scale_fill_viridis_c(trans = 'log', breaks = breaks,
labels = breaks) +
xlab(NULL) + ylab(NULL) +
scale_x_date(date_labels = "%Y-%m-%d") + theme_minimal()
require(dplyr)
y <- res$historical
d <- y["global"]
time = as.Date("2020-03-19")
dd <- filter(d, date == time) %>%
arrange(desc(cases))
dd = dd[1:40, ]
dd$country = factor(dd$country, levels=dd$country)
dd$angle = 1:40 * 360/40
require(ggplot2)
p <- ggplot(dd, aes(country, cases, fill=cases)) +
geom_col(width=1, color='grey90') +
geom_col(aes(y=I(5)), width=1, fill='grey90', alpha = .2) +
geom_col(aes(y=I(3)), width=1, fill='grey90', alpha = .2) +
geom_col(aes(y=I(2)), width=1, fill = "white") +
scale_y_log10() +
scale_fill_gradientn(colors=c("darkgreen", "green", "orange", "firebrick","red"), trans="log") +
geom_text(aes(label=paste(country, cases, sep="\n"),
y = cases *.8, angle=angle),
data=function(d) d[d$cases > 700,],
size=3, color = "white", fontface="bold", vjust=1) +
geom_text(aes(label=paste0(cases, " cases ", country),
y = max(cases) * 2, angle=angle+90),
data=function(d) d[d$cases < 700,],
size=3, vjust=0) +
coord_polar(direction=-1) +
theme_void() +
theme(legend.position="none") +
ggtitle("COVID19 global trend", time)
p
require(dplyr) require(ggplot2) require(shadowtext)
y <- res$historical
d <- y["global"]
dd <- d %>%
as_tibble %>%
filter(cases > 1000000) %>%
group_by(country) %>%
mutate(days_since_1m = as.numeric(date - min(date))) %>%
ungroup
breaks=c(1000, 10000, 20000, 50000, 500000,500000,5000000,20000000)
p <- ggplot(dd, aes(days_since_1m, cases, color = country)) +
geom_smooth(method='lm', aes(group=1),
data = dd,
color='grey10', linetype='dashed') +
geom_line(size = 0.8) +
geom_point(pch = 21, size = 1) +
scale_y_log10(expand = expansion(add = c(0,0.1)),
breaks = breaks, labels = breaks) +
scale_x_continuous(expand = expansion(add = c(0,1))) +
theme_minimal(base_size = 14) +
theme(
panel.grid.minor = element_blank(),
legend.position = "none",
plot.margin = margin(3,15,3,3,"mm")
) +
coord_cartesian(clip = "off") +
geom_shadowtext(aes(label = paste0(" ",country)), hjust=0, vjust = 0,
data = . %>% group_by(country) %>% top_n(1,days_since_1m),
bg.color = "white") +
labs(x = "Number of days since 1,000,000th case", y = "",
subtitle = "Total number of cases")
print(p)
