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The Donald Must Vacate Project: Pandemic statistics for the DMV

Northern Virginia, D.C., Montgomery County, and Prince George's County compose a metropolitan area that should not be considered as 3 independent jurisdictions. Additionally, southwestern Virginia is culturally different, and distant, from northern Virginia. Saying "cases in Virginia are rising" can mean that cases in northern VA are flat while those in southwestern VA are rising. Here are pandemic statistics relevant to the immediate and complete environment of the DMV.

First is the daily number of new cases as a function of time for the agglomeration.

Second is an estimate of the instantaneous reproduction number for SARS-CoV-2 as a function of time.

Third is an estimate of the probability that at least 1 person in a random group of 10 is infected.

Fourth is the fraction of the regional population that is fully vaccinated---two shots for either the Pfizer or the Moderna vaccine or one shot for the J&J vaccine.

Background information on the reproduction number

The reproduction number, R, is the population-wide average number of secondary infections caused by exposure to primary infectors. "Instantaneous" refers to the estimate calculated here. Another estimate is of the cohort reproduction number. From Cori et al. (2013):

The distinction between [cohort R] and [instantaneous R] is similar to the distinction between the actual life span of individuals born in 2013, which we can measure only retrospectively after all individuals have died (i.e., in a century), and life expectancy in 2013, estimated now by assuming that death rates in the future will be similar to those in 2013.

To be clear, instantaneous R is like the latter.

R0 is the reproduction number at time = 0, the time a virus is introduced to a population. For SARS-CoV-2 in China, this event was (likely) in November, 2019; in the U.S., this event was (likely) in January, 2020. R0 for SARS-CoV-2 is uncertain. Plausible values are between 2.0 and 4.0.

As the population takes countermeasures, such as physical distancing, wearing of masks, and contact tracing, R at time > 0 differs from R0---hopefully decreasing as time goes on. To end a pandemic before most of the population contracts the virus, the contagious population must infect fewer people than its current size. R must be < 1 for an extended period of time.

Background information on the probability estimate

An infected person is contagious for an estimated 10 days. This number is uncertain to a few days.

At the beginning of the pandemic the number of contagious people was estimated to be 11 times the number of people who tested positive. The true multiplicative factor may be a function of region. State-by-state estimates may be low. I've assumed a value of 5.

Let's assume that for every 1 person who tests positive and isolates for 10 days, 4 other people are contagious and free to infect the population. If 1,000 people have tested positive in the past 10 days, 4,000 people are roaming free.

If you go to a gathering of 10 random people, some of these people won't be infected. The remainder may be. The probability shown in the bottom plot is the probability that at least 1 person in the random group of 10 is infected. If the probability is 1, one or more people are infected---there is no doubt. If the probability is 0, no person is infected---there is no doubt. If the probability is 0.2, you are more certain that one or more people are infected than if the probability is 0.1. To have a feel for a reasonable risk threshold: 0.05 is the probability of getting 8 or more heads after flipping a coin 10 times.

Credit for this idea goes to Chande et al. (2020). The model is very simple and should be taken with many grains of salt.

Data

Daily incidence data---number of new cases each day---are taken from 3 sources: the departments of health of Virginia, D.C., and Maryland.

Included VDH health districts: Arlington, Fairfax, Alexandria, Loudoun, Prince William. Included MD counties: Montgomery and Prince George's.

Calculating R(t)

I use the EpiEstim package to estimate the instantaneous reproduction number as a function of time from the incidence time series. Parameters for the serial interval come from a meta-analysis.

Disclaimer

This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

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