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Bio: Scientist @fredhutch, studying viruses, evolution and immunity. Collection of #COVID19 threads here: bedford.io/misc/twitter/
Location: Seattle, WA
That’s a really great figure. Could I ask for it’s reference? Also, I strongly doubt that this effect came from viral evolution. Would explanation be that secondary infections of 15-35y cohort aren’t as deadly due to immunity after high attack rates in 1918-1919?
Our new Situation Report is now available:
nextstrain.org/narratives/nco…
8 months into the global transmission of #SARSCoV2 #COVID19, we look at the changing patterns of transmission we can see through viral genetics, including the initial fast spread & the effects of lockdown
1/3 pic.twitter.com/1h9GaQppOB
nextstrain.org/narratives/nco…
I agree with this.
This was my take as well. Tragic that we’re in this situation.
My impression was that false positives that occasionally occur are commonly from lab handing errors (contamination / swapped samples). The background rate of positivity will affect the FPR in this case.
I’m certainly not proposing this as a strategy. Just trying to understand why we see epidemics wax and wane. BTW Texas has had 528k confirmed cases in a population of 29M for a floor of 1.8% infected. We don’t catch every infection as a case probably only like 1 in 5 or so.
Effect of NPIs and effect of immunity should be multiplicative and not additive.
twitter.com/trvrb/status/1…
Interesting. I was assuming that 1 in 4 was something of a floor just due to spectrum of disease. Asymptomatic infections won’t seek out testing and many mild cases won’t either.
I hadn’t seen this. Thanks for sharing.
I believe a ratio of 1:6 is generally quite reasonable (based on recent CDC seroprevalence estimates).
Follow up #1: Some helpful further details to these estimates to consider from @youyanggu
twitter.com/youyanggu/stat…
Hi Youyang. Really good points here. Thank you. I’d obviously agree with the 4-8X reporting spread. And yes should take into account seroprevalence today vs when today’s confirmed cases were infected. And yes, there will be undereporting of deaths as well.
It looks like just accounting for lag in reporting of deaths gives consistent estimates for IFR: mobile.twitter.com/trvrb/status/1…. About 0.7% IFR assuming 10% infected and about half that with 20% infecting.
mobile.twitter.com/trvrb/status/1…
Seasonal flu has disappeared from circulation in Australia (during their regular flu season). Current societal experiment seems enough to drop flu’s normal R0 of ~1.5 to below 1. Curious if we’ll have a flu season in the US this winter.
I hadn’t seen this. Thanks for sharing. I’ll look into it.
Based on mentions, it was one of the most controversial things I’ve posted. mobile.twitter.com/trvrb/status/1…
mobile.twitter.com/trvrb/status/1…
I don’t have real data here, but my assumption has been that people are behaving based on perceived risk, with older individuals, on average, adhering more to social distancing.
I’ve commented on this previously: mobile.twitter.com/trvrb/status/1…. There has definitely been a shift in age of infections from March / April.
mobile.twitter.com/trvrb/status/1…
My best guess for fraction of Florida infected is about 12%, ie 525k cases ⨉ 5X ratio / 21.48M, though I could believe between 8% and 16% as reasonable. Lower than the 20% I threw out yesterday, but still high enough to impact epidemic spread if behavioral Rt is ~1.2. 16/16
But overall, I take this as congruent with recent seroprevalance estimates suggesting a 5-6X ratio of cases to infections and IFR of still ~0.5% at this point. 15/16
twitter.com/trvrb/status/1…
IFR of course depends on specific population infected, and I could believe there could be a slight improvement from early epidemic IFR of between 0.5% and 1% due to shift of disease burden towards younger individuals. 14/16
For Arizona, we have 4140 deaths reported as of Aug 8 and 144k cases reported as of July 19. This implies between 576k infections and 1.15M infections on July 19. This equates to a lag-adjusted IFR of between 0.4% and 0.7%. 13/16
For Texas, we have 8866 deaths reported as of Aug 8 and 325k cases reported as of July 19. This implies between 1.3M infections and 2.6M infections on July 19. This equates to a lag-adjusted IFR of between 0.3% and 0.7%. 12/16
For Florida, we have 8108 deaths reported as of Aug 8 and 350k cases reported as of July 19. This implies between 1.4M infections and 2.8M infections on July 19. This equates to a lag-adjusted IFR of between 0.3% and 0.6%. 11/16
Here, I assume a range of underreporting of catching between 1 infection in 4 as a case to 1 infection in 8. With 1 in 4, total infections are 4X total cases. With 1 in 8, total infections are 8X total cases. 10/16
We can ballpark a lag-adjusted IFR by comparing total deaths reported today to estimated infections 20 days ago, ie July 19. 9/16
Across these three states, this lagged correlation is maximized at a 20 day reporting lag between cases and deaths. This 20 day reporting lag fits expectations from other sources (cdc.gov/nchs/nvss/vsrr…). 8/16
We can estimate the reporting lag between cases and deaths more rigorously by computing the lagged correlation between timeseries of daily cases and timeseries of daily deaths. 7/16 pic.twitter.com/M9uiypKr2m
Arizona also appears similar with cases starting to rise ~May 27 and deaths starting to rise ~June 23 (27 days). Additionally, cases appear to peak ~July 6, while deaths appear to peak 17 days later on ~July 23. 6/16 pic.twitter.com/WWAKIlZSkm
We see a similar pattern in Texas, with cases starting to increase ~June 10 and deaths starting to increase ~July 6 (26 day lag). 5/16 pic.twitter.com/AeDKo6Y7CM
If we look at Florida, we see cases start to increase ~June 6 and deaths start to increase ~July 3 (27 day lag). This fits expectation from disease progression alongside delays associated with reporting deaths. 4/16 pic.twitter.com/TgNzqiLpVf
This thread walks through a ballpark version of implied IFR that takes into account reporting delays in Florida, Texas and Arizona in their recent epidemic surge. Data and figures that follow from @COVID19Tracking. 3/16
Multiple people expressed skepticism that 20% seroprevalence in Florida is reasonable. Others thought that 20% was patently impossible due to implied crude infection fatality ratio (IFR). 2/16
twitter.com/beta_rank_fb/s…
A follow up to yesterday's controversial thread on societal behavior, population immunity and Rt to specifically address issue of what fraction of the population in Florida, Texas and Arizona may have had COVID-19. 1/16
twitter.com/trvrb/status/1…
That's a good concise framing.
Estimates of Rt are generally pretty straight forward measurements of the slope of the case curve. If I look at Wisconsin here (nytimes.com/interactive/20…), I see the 7-day average peaking on Jul 26 and coming down slightly to Aug 7. This is what Rt of 0.89 is measuring.
Interesting data. Thanks for sharing.
Yeah. I was definitely seeing this. A substantial fraction of responses was: the HIT is actually 20% and not 60% (due to heterogeneity and/or T cells?) and another fraction was: you're wrong, HIT is 60%. We need a non-political understanding of why epidemics wax and wane.
I usually think of seasonality (in influenza, etc...) as purely mediating transmission due to things like crowding, school term forcing and physical stability of virions. I'm not familiar with work ascribing changes to mortality rate to seasonality.
Properly randomized and longitudinal serosurveys would be hugely helpful. Especially for age groups. And yes, didn't expect it to be so controversial to say that with 10% immunity and strong NPIs dropping Rt we expect some effect of immunity in reducing spread.