When I ran these simple lagged case fatality rate (CFR) calculations last week I was surprised and troubled at how big the predicted numbers of deaths in the coming weeks were. Since then #COVID19 daily case counts have continued to rise. 1/10
@alexismadrigal and @whet describe in much better detail the simple logic behind this calculation here: theatlantic.com/science/archiv… and they do a thorough job investigating assumptions of the method. 2/10
Ryan Tibshirani with the Carnegie Mellon Delphi Research Group very helpfully did an independent replication of the method here: htmlpreview.github.io/?https://githu…. 3/10
If we look at Rt in the last couple of weeks there's very little evidence of reductions in transmission rate at the US level. Data from rt.live. 4/10
You can see this clearly by looking at daily cases from @COVID19Tracking on a log scale for each state. The solid lines show 7-day average of state-level case counts and the dashed lines show simple linear-on-a-log-scale fits. Straight lines indicate exponential growth. 5/10
Though I would note that North Dakota seems to perhaps be slowing down in transmission rate after perhaps ~30% of state has been infected. Figure from @youyanggu's covid19-projections.com. 6/10
The logic of the lagged CFR calculation is that a fraction of individuals diagnosed with COVID-19 will succumb to their disease but that this takes time to occur and to be reported. We still expect 1.5% to 2.0% of cases to succumb and be reported 22 days later. 7/10
If this is used for forward projection to convert cases reported today into deaths reported 22 days from now we get the following. Here, 7-day average of daily reported deaths is shown as solid red line and 22-day lookahead projection from cases is shown as dashed gray line. 8/10
You can see that this simple projection has matched well since August. It predicts >2000 deaths per day starting in December. I really hope I'm wrong here and we see CFR drop in the coming weeks, but I don't see any obvious reasons to expect a sudden change in CFR. 9/10
Even if this short-term outcome is "baked into" already acquired infections, we can still do our best to reduce onward transmission. @zeynep expresses this extremely cogently (as always) here: theatlantic.com/health/archive…. 10/10
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One small note about these trials. It's often assumed that vaccines are only a proxy for immune response to natural infection. However, there's nothing that prevents vaccines from inducing better, more durable protection than natural infection. 3/8
The #COVID19 epidemic is rapidly growing throughout the US. What happens now? Here I try to make some predictions, but mostly try to explain how I think about the epidemic. 1/14
The US just reported ~170,000 cases in a day. However, there hasn't been a sudden increase in transmission. This is the same exponential growth process going on for weeks now. Rt has ticked up from a US average of ~1 in August to ~1.15. Data from rt.live. 2/14
This increase in Rt can be ascribed to seasonality of the virus. Seasonality of respiratory pathogens is (incredibly) not well understood but is thought to be due to a combination of indoor crowding and increased stability of viral particles in drier winter air. 3/14
A brief update on our work with the sequencing of the White House #COVID19 outbreak. Since posting on Nov 1, groups from all over the US have shared an additional 2798 #SARSCOV2 viral genomes via @gisaid and additional connections have emerged. 1/9
This sequencing has revealed additional viruses circulating in Virginia and collected between Aug and Oct that fall alongside the WH lineage, as well as three viruses from Michigan collected in Oct that are closely related to sequences from the White House outbreak. 2/9
These three viruses from Michigan possess 1 differentiating mutation and the two White House-associated viruses also possess 1 differentiating mutation. A molecular clock analysis places their common ancestor in Aug or Sep. Interactive figure at nextstrain.org/community/blab…. 3/9
After posting about sharply rising #COVID19 cases Friday, there were multiple replies to the effect of "but deaths aren't going up". As should be obvious to most at this point, (reported) deaths lag (reported) cases. This thread investigates. 1/8
There is a lag between when a case is diagnosed and when the individual may succumb to their disease and there is a further lag between date of death and when the death is reported. 2/8
Here, I compare state-level data from @COVID19Tracking for cases and deaths and find that a 22-day lag maximizes state-level correlations. 3/8
I know that everyone has been (justifiably) distracted by other things, but the #COVID19 epidemic in the US is looking pretty dire with 125,552 confirmed cases reported Friday by @COVID19Tracking. 1/10
Confirmed cases have continued to tick up across the US, though with the Midwest and Mountain West contributing to most of the recent increase. Data from @COVID19Tracking. 3/10
Separately, I wanted to address the question of "why do this?" with regards to sequencing of infections involved in the White House #COVID19 outbreak. 1/10
Although the origins of the White House outbreak were characterized as "unknowable", viral genome sequencing can offer important clues to how infections in a cluster are connected to each other and to the larger COVID-19 epidemic. 2/10
This technology is rapidly becoming a standard course of action for COVID-19 clusters of public health interest. This seems obvious, but we can use science to understand and track the spread of COVID-19. 3/10