```
TARGET.cases <- "time_series_covid19_confirmed_US.csv"
TARGET.deaths <- "time_series_covid19_deaths_US.csv"
SURL <- "https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series"
## To download the latest version, delete the files and run again
for (target in c(TARGET.cases, TARGET.deaths))
{
if (!file.exists(target))
download.file(sprintf("%s/%s", SURL, target), destfile = target)
}
```

[This note was last updated using data downloaded on 2020-08-25. Here is the source of this analysis. Click here to show / hide the R code used. ]

```
covid.cases <- read.csv(TARGET.cases, check.names = FALSE, stringsAsFactors = FALSE)
covid.deaths <- read.csv(TARGET.deaths, check.names = FALSE, stringsAsFactors = FALSE)
## covid.deaths has an extra column called Population, which we drop for now
covid.deaths$Population <- NULL
if (!identical(colnames(covid.deaths), colnames(covid.cases)))
{
warning("Cases and death data have different columns (dates)... using common ones.")
common.colnames <- intersect(colnames(covid.deaths), colnames(covid.cases))
covid.deaths <- covid.deaths[colnames(covid.deaths) %in% common.colnames]
covid.cases <- covid.cases[colnames(covid.cases) %in% common.colnames]
}
if (!identical(rownames(covid.cases), rownames(covid.deaths)))
{
stop("Cases and death data have different rows... check versions.")
}
omit.regions <- c("Diamond Princess", "Grand Princess")
covid.deaths <- subset(covid.deaths, !(Province_State %in% omit.regions))
covid.cases <- subset(covid.cases, !(Province_State %in% omit.regions))
```

```
correctLag <- function(x)
{
n <- length(x)
stopifnot(n > 2)
for (i in seq(2, n-1))
if (x[i] == x[i-1])
x[i] <- sqrt(x[i-1] * x[i+1])
x
}
extractCasesTS <- function(d)
{
x <- t(data.matrix(d[, -c(1:11)]))
x[x == -1] <- NA
colnames(x) <- with(d, paste(Province_State, Admin2, sep = " / "))
apply(x, 2, correctLag)
}
tdouble <- function(x)
{
if (all(x == 0)) return (NA)
x <- c(0, x[x > 0])
i <- seq_along(x)
f <- approxfun(x, i)
diff(f(max(x) * c(0.5, 1)))
}
```

```
## State-wise totals
byState.cases <- split(covid.cases, covid.cases$Province_State)
byState.deaths <- split(covid.deaths, covid.deaths$Province_State)
state.cases <- sapply(byState.cases, function(d) colSums(data.matrix(d[, -c(1:11)])))
state.deaths <- sapply(byState.deaths, function(d) colSums(data.matrix(d[, -c(1:11)])))
D <- nrow(state.cases)
```

The following plot shows the current doubling time for deaths and cases, across US states, with states sorted by the total number of deaths.

```
stotal.deaths <- state.deaths[D, , drop = TRUE]
stotal.cases <- state.cases[D, , drop = TRUE]
sdt.deaths <- apply(state.deaths, 2, tdouble)
sdt.cases <- apply(state.cases, 2, tdouble)
state.names <- names(sdt.deaths)
state.names <- factor(state.names, levels = state.names,
labels = sprintf("%s (%g)", state.names, stotal.deaths))
dotplot(reorder(state.names, stotal.deaths) ~ sdt.deaths + sdt.cases,
scales = list(alternating = 3), xlab = "Doubling time in days",
par.settings = simpleTheme(pch = 16),
auto.key = list(space = "right", text = c("Deaths", "Cases")))
```

Generally speaking, the doubling times for cases are higher than that for days. This is a good indicator: as deaths lag behind cases, we can expect that around a week from now, the doubling time for deaths will increase to make up the difference.

The following states are somewhat poor performers in this respect: they have more than 40 deaths, and the difference in their doubling times is less that 2 days (although Washington is doing pretty well in terms of the absolute doubling times).

```
dotplot(reorder(state.names, sdt.cases - sdt.deaths) ~ sdt.deaths + sdt.cases,
subset = (sdt.cases - sdt.deaths < 2 & stotal.deaths > 40),
scales = list(alternating = 3), xlab = "Doubling time in days",
par.settings = simpleTheme(pch = 16),
auto.key = list(space = "right", text = c("Deaths", "Cases")))
```

The following plots show how the number of deaths have grown in these states since the count first exceeded 50, compared to the other counties.

```
deathsSince10 <- function(region, xdata)
{
x <- xdata[, region, drop = TRUE]
x <- x[x > 10]
if (length(x) == 0) return (NULL)
data.frame(region = region, day = seq_along(x),
deaths = x, total = tail(x, 1))
}
panel.glabel <- function(x, y, group.value, col.symbol, ...) # x,y vectors; group.value scalar
{
n <- length(x)
panel.text(x[n], y[n], label = group.value, pos = 4, col = col.symbol, srt = 40)
}
deaths.10 <- do.call(rbind, lapply(colnames(state.deaths), deathsSince10,
xdata = state.deaths))
bg <- xyplot(deaths ~ day, data = deaths.10, grid = TRUE,
scales = list(alternating = 3, y = list(log = 10, equispaced.log = FALSE)),
col = "grey", groups = region, type = "l")
fg <-
xyplot(deaths ~ day | reorder(region, -total), data = deaths.10,
xlab = "Days since number of deaths exceeded 10",
ylab = "Number of deaths", pch = ".", cex = 3,
scales = list(alternating = 3, y = list(log = 10, equispaced.log = FALSE)),
as.table = TRUE, between = list(x = 0.5, y = 0.5), layout = c(4, 5),
type = "o", ylim = c(NA, 20000))
fg + as.layer(bg, under = TRUE)
```

Most cases are concentrated in some specific counties. The following plot gives the current doubling time of deaths, compared to total number of deaths, across US counties / regions with at least 50 deaths.

```
keep <- covid.deaths[[length(covid.deaths)]] > 50 # at least 50 deaths
covid.cases <- covid.cases[keep, ]
covid.deaths <- covid.deaths[keep, ]
xcovid.cases <- extractCasesTS(covid.cases)
xcovid.deaths <- extractCasesTS(covid.deaths)
D <- nrow(xcovid.deaths)
total.deaths <- xcovid.deaths[D, , drop = TRUE]
dt.deaths <- apply(xcovid.deaths, 2, tdouble)
do.label.1 <- dt.deaths < quantile(dt.deaths, 0.05, na.rm = TRUE) | dt.deaths > quantile(dt.deaths, 0.95, na.rm = TRUE)
do.label.2 <- (total.deaths > max(total.deaths)/2) & !(do.label.1)
xyplot(dt.deaths ~ total.deaths, pch = 16, grid = TRUE,
ylab = "Doubling time in days", xlab = "Number of deaths",
scales = list(alternating = 3, x = list(log = 10, equispaced.log = FALSE))) +
layer(panel.text(x[do.label.1], y[do.label.1],
labels = names(total.deaths)[do.label.1],
pos = 4, col = "grey30")) +
layer(panel.text(x[do.label.2], y[do.label.2],
labels = names(total.deaths)[do.label.2],
pos = 2, col = "grey30"))
```

The following plots show how the number of deaths have grown in these counties since the count first exceeded 50, compared to the other counties.

```
deaths.10 <- do.call(rbind, lapply(colnames(xcovid.deaths), deathsSince10,
xdata = xcovid.deaths))
bg <- xyplot(deaths ~ day, data = deaths.10, grid = TRUE,
scales = list(alternating = 3, y = list(log = 10, equispaced.log = FALSE)),
col = "grey", groups = region, type = "l")
fg <-
xyplot(deaths ~ day | reorder(region, -total), data = deaths.10,
xlab = "Days since number of deaths exceeded 10",
ylab = "Number of deaths", pch = ".", cex = 3,
scales = list(alternating = 3, y = list(log = 10, equispaced.log = FALSE)),
as.table = TRUE, between = list(x = 0.5, y = 0.5), layout = c(4, 5),
type = "o", ylim = c(NA, 20000))
fg + as.layer(bg, under = TRUE)
```