COVID-19 Plots
Intro
I liked the Financial Times plots for tracking the evolution of COVID-19 (https://www.ft.com/coronavirus-latest), but then they changed to different plots. So here I am more-or-less reproducing those plots (and adding some others). This is generated from an Rmarkdown document that I’ll be rerendering daily.
Country-level data
For now using the country data from https://covid.ourworldindata.org:
cases = read.csv("https://covid.ourworldindata.org/data/ecdc/total_cases.csv",
stringsAsFactors=FALSE)
cases$date = as.POSIXct(cases$date)
cases$doy = round(as.numeric(difftime(cases$date, as.POSIXct("2019-12-31"), units="days")))
deaths = read.csv("https://covid.ourworldindata.org/data/ecdc/total_deaths.csv",
stringsAsFactors=FALSE)
deaths$date = as.POSIXct(deaths$date)
deaths$doy = round(as.numeric(difftime(deaths$date, as.POSIXct("2019-12-31"), units="days")))
# World population data from worldbank.org:
temp = tempfile()
download.file("http://api.worldbank.org/v2/en/indicator/SP.POP.TOTL?downloadformat=csv", temp)
pop = read.csv(unz(temp, "API_SP.POP.TOTL_DS2_en_csv_v2_1120881.csv"),
skip=4, header=TRUE, stringsAsFactors=FALSE)
rownames(pop) = pop$Country.Name
#deaths.cty = colnames(deaths)
#deaths.cty = gsub(".", " ", deaths.cty, fixed=TRUE)
#ctymap = match(deaths.cty, rownames(pop))
# deaths.cty[which(is.na(ctymap))]
unlink(temp)Country-level deaths and cases
Daily deaths
Smoothed
Log-scale
Linear scale
Raw
Log-scale
Linear-scale
Daily new cases
Smoothed
Log-scale
Linear scale
Raw
Log-scale
Linear-scale
Cumulative deaths
Log-scale
Gray dashed lines are doubling times of 1, 2, 3, and 7 days (from steepest to shallowest)
Linear-scale
Cumulative cases
Log-scale
Gray dashed lines are doubling times of 1, 2, 3, and 7 days (from steepest to shallowest)
Linear-scale
Country-level naive case fatality rates
By “naive” I mean: at any point in time, divide the total cumulative number of deaths by the total cumulative number of confirmed cases. This will be biased high because the denominator is too small because not all cases are detected (due to lack of testing), but on the other hand will be biased low because some current active cases will result in deaths later. It should eventually converge on the true CFR if testing becomes widespread and the epidemic dies down.
Log-scale
Linear-scale
US state-level data
So far I’m using data collated by the NY Times. It is pretty strangely structured, but oh well:
states = read.csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv",
stringsAsFactors=FALSE)
states$date = as.POSIXct(states$date)
states$doy = round(as.numeric(difftime(states$date, as.POSIXct("2019-12-31"), units="days")))
# Get this into a more sensible structure:
us.deaths = t(tapply(states$deaths,
list(factor(states$state, levels=sort(unique(states$state))),
factor(states$doy, levels=min(states$doy):max(states$doy))),
identity))
us.deaths = as.data.frame(us.deaths)
dates = sort(unique(states$date))
us.deaths$date = dates
doys = sort(unique(states$doy))
us.deaths$doy = doys
us.cases = t(tapply(states$cases,
list(factor(states$state, levels=sort(unique(states$state))),
factor(states$doy, levels=min(states$doy):max(states$doy))),
identity))
us.cases = as.data.frame(us.cases)
us.cases$date = dates
us.cases$doy = doysUS state-level deaths and cases
Daily deaths
Smoothed
Log-scale
Linear scale
Raw
Log-scale
Linear-scale
Daily new cases
Smoothed
Log-scale
Linear scale
Raw
Log-scale
Linear-scale
Cumulative deaths
Log-scale
Gray dashed lines are doubling times of 1, 2, 3, and 7 days (from steepest to shallowest)
Linear-scale
Cumulative cases
Log-scale
Gray dashed lines are doubling times of 1, 2, 3, and 7 days (from steepest to shallowest)
Linear-scale
US state-level naive case fatality rates
Log-scale
Linear-scale
