Last updated: 2022-01-21-06:29 UTC

Updates daily at 06:00 UTC

On this page are US per-state plots sorted by per capita cumulative deaths, from highest to lowest.

Note that data on recoveries is not available and so is estimated, and active cases then inferred from this estimate. See the bottom of this page for details.

Larger plots for individual states are at the following links:

- Daily cases and daily deaths are plotted with 5-day exponential smoothing
- Recoveries are estimated based on the assumption that 85% of surviving cases are mild and recover after 17±4 days, and 15% are severe and recover after 32±12 days.

For the mathematically inclined: Recoveries*R*_{estimated}(*t*) are computed as the convolution of living cases (confirmed cases*C*(*t*) minus deaths*D*(*t*)) with a probability distribution for the time to recovery, equal to the weighted sum of two normal distributions, one for severe cases and one for mild cases:

*R*_{estimated}(*t*) = (*C*(*t*) -*D*(*t*)) ∗ (*P*_{m}*N*_{μmσm}(*t*) +*P*_{s}*N*_{μsσs}(*t*))

where:*P*_{m}= 0.85 is the proportion of cases that are mild*P*_{s}= 0.15 is the proportion of cases that are severe*N*_{μσ}(*t*) is the probability density function for a normal distribution with mean*μ*and standard deviation*σ**μ*_{m}= 17 days is the mean recovery time for a mild case*σ*_{m}= 4 days is the standard deviation of recovery time for a mild case*μ*_{s}= 32 days is the mean recovery time for a severe case*σ*_{s}= 12 days is the standard deviation of recovery time for a severe case- ∗ is the convolution operation

*"the median time from onset to clinical recovery [...] is 3-6 weeks for patients with severe or critical disease"*, and for mild cases from a study of 21 Chinese COVID-19 patients, who were discharged*"after a mean hospitalized period of 17±4 days"*. - Doubling time of active cases on each day is computed from an exponential fit to the previous 5 days of data. Projections are based on the most recent such exponential fit. This is a rough answer to the question "What will happen if nothing changes about testing rates or control measures?" This can model both growth and decline in active cases, both of which should be approximately exponential while conditions remain fixed (and whilst most of the population has not been infected yet).
- Shaded region in the growth rate plot represents 1σ uncertainty range of the growth rate computed from the fit parameters. Additional labels on the right side of the growth rate plot show the doubling times corresponding to the growth rates (negative doubling times are halving times)
- The "Δ doubling/halving in" statistic for deaths is number of days it takes for the
*daily number of deaths*to double, not the total number of deaths, since the latter is less meaningful. It is calculated from the ratio of deaths over the last 5 days to that of the preceding 5 days - this is less fraught than attempting a fit of the data when it is very noisy. The uncertainty in the doubling/halving time is based on assuming sqrt(N) uncertainty in the number of deaths in a any time interval. - Data quality is limited by testing and reporting within each state, and the fact that these conditions are changing in time.

Source for case numbers: New York Times COVID dataset on GitHub

Source for vaccination numbers: Centers for Civic Impact COVID dataset on GitHub

Plots by Chris Billington. Contact: chrisjbillington [at] gmail [dot] com

Python script for producing the plots can be found at https://github.com/chrisjbillington/chrisjbillington.github.io/blob/master/covid.py. The script is messy, as things are in a state of flux and I've been experimenting and switching data sources. It will likely be cleaner once it becomes clear which data source is best.