With the publication of the Science letter, the Overton window for discussion of "lab leak" hypothesis has shifted dramatically. We now have mainstream scientific opinions that largely range between "lab leak can be dismissed" and "both zoonosis and lab leak are viable". 1/8
I am in the both are plausible camp. The data (as it exists) is consistent with zoonosis, but it's also consistent with lab leak. Parsing the relative probabilities of the two depends on multiple lines of evidence and is necessarily assumption ridden. 2/8
However, I think that there is a philosophical divide among scientists in how to assess hypotheses that perhaps explains some of the gap in opinion. Ie, is zoonosis the "null" hypothesis that we need significant evidence to reject or are we comparing two competing hypotheses? 3/8
Zoonoses occur constantly. We've had >26 Ebola outbreaks, 100s of MERS spillover events, abundant one-off human infections by avian influenza, etc... This is the prior many scientists are working from and suggests that zoonosis should be the null hypothesis. 4/8
On the other hand, of the recent influenza pandemics (H2N2 in 1957, H3N2 in 1968, H1N1 in 1977, H1N1 in 2009), one of the four (1977 H1N1) is conclusively a reintroduction into the human population via some lab intermediary. Lab accidents do happen. 5/8
If one takes this as naive prior of 1/4, one comes to a very different conclusion and would treat both zoonosis and lab leak as competing hypotheses for the (sparse) data. 6/8
I'm not sure how best to frame a prior in terms of origins of SARS-CoV-2, but I do agree that we'd benefit from a dispassionate evidence-based discourse on this issue. I also think that barring a major new datapoint, it's unlikely we'll come to definitive conclusions. 7/8
Regardless of COVID-19 origins, we should treat zoonosis and lab escape as potential pandemic risks and set up structures to mitigate these risks. On the lab side, this includes review of biosafety protocols and strong "no fault" reporting of laboratory acquired infections. 8/8

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More from @trvrb

25 May
#COVID19 cases in the US reported by @CDCGov have continued their week-after-week exponential decline that began in mid-April. This is exceptionally welcome news, although I'm now watching closely for variants driving sub-epidemics despite overall cases falling. 1/10
If we look at state-level cases with a log-axis we can see exponential growth and then exponential decline visible as straight lines on the log plot. Some states have had recent precipitous declines (NY, MA, MI), while others have been more stable (WA, CO, OR). 2/10
Using genomic data shared to @GISAID, we can plot frequency of different variant lineages through time and across states to get a sense of competitive dynamics. Here, I'm plotting lineage frequency on a logit axis, so that logistic growth is visible as a straight-line fit. 3/10
Read 10 tweets
11 May
The drivers of the #COVID19 epidemic in India are certainly multifactorial, but we have now seen the viral lineage B.1.617 linked to this epidemic continue to increase in frequency in India and spread rapidly outside of the country. 1/10
Looking within India there are three primary viral lineages of consequence: B.1.1.7 (in blue) and B.1.351 (in green) introduced into India repeatedly from outside the country and B.1.617 (in yellow) emerging endogenously from within India (nextstrain.org/ncov/asia?c=em…). 2/10
Tracking frequencies over time in sequence data shared to @gisaid shows a continued increase in B.1.617, while recent weeks have shown a decline in B.1.1.7. 3/10
Read 10 tweets
7 May
From Aug 2020 to Mar 2021, the lagged case fatality rate (CFR) of the US #COVID19 epidemic had remained largely constant at ~1.5% and provided a simple method to predict subsequent deaths from current cases. 1/6
I've rerun the previous analysis correlating state-level reported cases with state-level reported deaths with different lags. Using @CDCgov data since Aug 2020, I find that a 19 day lag of cases to deaths maximizes average state-level correlation coefficient. 2/6
This shows the resulting projection for deaths where the gray dashed line shows a lookahead projection where 1.5% of reported cases result in reported deaths 19 days later. This can be compared to the solid red line showing realized 7-day average of reported deaths. 3/6
Read 6 tweets
3 May
Just as we can decompose the US #COVID19 epidemic into a B.1.1.7 epidemic and a non-B.1.1.7 epidemic, we can further partition by variants of concern B.1.1.7, B.1.351 and P.1, where it's clear that P.1 has been gaining ground. 1/13
Here, using data from @GISAID, we see that in terms of frequencies across the US, P.1 has been undergoing more rapid logistic growth in frequency than B.1.1.7, while B.1.351 has been slower than B.1.1.7. 2/13
I'm plotting this with the unusual "logit" y-axis (with 1%, 10%, 50%, etc...) because a straight line in logit space is indicative of logistic growth. This sort of plot makes it easy to compare logistic growth rate of frequency between lineages with different frequencies. 3/13
Read 13 tweets
26 Apr
There are effectively two #COVID19 epidemics in the US at this moment; one largely resolving epidemic comprised of non-variant viruses and one growing epidemic of B.1.1.7. Together they have resulted in a near-plateau of cases throughout much of the spring. 1/10
If we look at virus frequencies in the US using data in @GISAID, we can see that the 7-day weighted frequency of B.1.1.7 has been growing consistently since January and is now over 50% in the US. 2/10
This pattern is repeated across individual states. These six were chosen as states with plentiful genomic data and to provide geographic diversity. B.1.1.7 is dominating throughout the US, except for New York and surroundings where B.1.526 is co-circulating. 3/10
Read 10 tweets
23 Apr
It's hard for me to infer the degree to which new variants are driving the surge in cases in India, but we are seeing rapid growth in frequency of multiple viral variants. 1/5
Here is a @nextstrain view of @GISAID data that focuses on viruses from India and highlights emerging lineages B.1.1.7 (in blue), B.1.351 (in green) and B.1.617 (in orange). Interactive version at nextstrain.org/ncov/asia?c=em…. 2/5
We can fit a logistic growth model to the full genomic dataset from India for these three lineages, where we see logistic growth as "linear on a logit scale". Each of these lineages is estimated to have similar logistic growth rates of ~0.3 per week. 3/5
Read 5 tweets

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