One of the most important unknowns in the #nCoV2019 epidemic is the total number of _infections_ as opposed to _cases_, as there may be many mild infections that do not rise to case definition. I sought to address this with phylodynamic methods here: https://bedford.io/projects/ncov-phylodynamics/ … 1/9
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Here, I started by following
@arambaut's work (http://virological.org/t/phylodynamic-analysis-67-genomes-08-feb-2020/356 …) and set up https://beast.community/ to estimate viral population dynamics from sequence data alone. 2/92 odpowiedzi 8 podanych dalej 39 polubionychPokaż ten wątek -
This measures how quickly observed genomes share ancestry to estimate the rate of exponential growth, arriving at an estimated doubling time of 7.2 days (95% CI 5.0-12.9), inline with previous modeling estimates from case data. 3/9pic.twitter.com/p9Vp1suPUW
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I convert estimates of coalescence to estimates of pathogen prevalence following phylodynamic methods described here https://en.wikipedia.org/wiki/Viral_phylodynamics#Compartmental_models …, assuming a range of heterogeneity in secondary transmissions ala https://www.biorxiv.org/content/10.1101/2020.01.23.917351v1 … 4/9
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Excellent, thanks. Let us know if you need the exact distribution of k. Our paper also got published in
@Eurosurveillanc on January 30: https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.4.2000058 …2 odpowiedzi 1 podany dalej 3 polubione -
That would be helpful. It would be great if you could share whatever your best bounds are for k. Thank you!
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Great, just sent you an email.
Wydaje się, że ładowanie zajmuje dużo czasu.
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