We then multiply the true prevalence ratio by the reported cases to obtain an estimate of the true daily new infections. We add a 14-day lag to account for the delay between exposure and positive test confirmation.pic.twitter.com/75PF9jN23B
Epidemiologist. Writer (Guardian, Observer etc). "Well known research trouble-maker". PhDing at @UoW Host of @senscipod Email gidmk.healthnerd@gmail.com he/him
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We then multiply the true prevalence ratio by the reported cases to obtain an estimate of the true daily new infections. We add a 14-day lag to account for the delay between exposure and positive test confirmation.pic.twitter.com/75PF9jN23B
We can use this formula to estimate the new daily infections on the US nationally. Or we can use the test positivity rates to back out the true infections on a state-by-state basis, and then add them up to get the US national estimate. The results are similar.pic.twitter.com/WZLz9z0XxZ
While I derived the above relationship independently, I have since become aware of similar work by @ellis2013nz, @_stah, and @FLCovid.
Of course, this relationship isn't perfect. Recent positivity rates may skew high because some states do not report repeated negative results.
The other way I estimate true prevalence is by using the http://covid19-projections.com model, which only uses reported deaths. Even though the data source is exclusive, the resulting shape of new infections is similar and shows a higher peak of ~450k infections/day in July.pic.twitter.com/VDTFFCBAkm
There are 13 states with >10% test positivity. 10-15% - UT, KS, AR, GA, MO, TX, SC 15-20% - ID, AZ, FL, NV, AL 26% - MS It's very unfortunate that we are still seeing >10% positivity 5 months into the pandemic. As many have iterated, we need a coordinated national testing plan.pic.twitter.com/WyIxGSbTo6
2) Lower infection fatality rate (IFR) After we have an estimate of the true infections, we can compute the implied infection fatality rate by factoring in deaths from approximately 4 weeks later. We can do this for the US nationally, or on a state-by-state basis.
The case fatality rate and implied infection fatality rate have all decreased significantly over the past few months (US is currently at ~0.25% IIFR). I believe the largest contributor is lower median age of infection. There's also some elements of improved treatments.pic.twitter.com/uVN11fGrgZ
Using CDC's COVIDView data, one can see that the proportion of confirmed cases for individuals above age 65 dropped significantly from April to June. Once we factor in test positivity rate to get true prevalence, the difference is even more pronounced. https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/07312020/commercial-labs.html …pic.twitter.com/vyzFGO5RxN
Since the IFR of those age 65+ is 20-50x higher than those under age 50, it's no surprise that we've seen a steep reduction in the overall IFR across the US. This is further helped by improved treatments and earlier detection. One caution is that this trend is slowly reversing.
3) Lower herd immunity threshold (HIT) Infections are now declining in almost all heavily-impacted states, despite no clear policy interventions. Estimating the HIT based on the current effective reproduction numbers results in a ~10-35% effective HIT. (Formula: HIT = 1-1/Rt)
In this context, I would say that HIT is the wrong term to use. HIT implies (long-lasting) immunity, whereas this is a potentially transient result of behaviour shortly after reopening. In many places, these behaviours have not lasted and thus Rt climbs
Honestly, I'm not sure. Immunity implies, well, immunity, which this isn't exactly. HIT is really only used traditionally in the vaccination context, although I know everyone wants it to be applicable to COVID-19
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