Résumé
Understanding how SARS-CoV-2 entered the human population, thereby causing the COVID-19 pandemic, is one of the most urgent questions in science today. Two hypotheses are widely acknowledged as being most likely to explain the pandemic's origin in late 2019: (i) the natural origin hypothesis that one or more cross-species transmissions from animals into humans occurred, most likely at the Huanan Seafood Market in Wuhan, China; (ii) the laboratory origin hypothesis, that scientific research activities led to the unintentional leak of SARS-CoV-2 from a laboratory into the general population. A recent analysis of SARS-CoV-2 genomes by Pekar et al. [Science 377:960-966 (2022)] claims to establish at least two separate spillover events from animals into humans, thus claiming to provide strong evidence for the natural origin hypothesis. However, here we use outbreak simulations to show that the findings of Pekar et al. are heavily impacted by two methodological artifacts: the dubious exclusion of informative SARS-CoV-2 genomes, and their reliance on unrealistic phylodynamic models of SARS-CoV-2. Absent models that incorporate these effects, one cannot conclude multiple SARS-CoV-2 spillovers into humans. Our results cast doubt on a primary point of evidence in favor of the natural origin hypothesis.
Sujets)
COVID-19Résumé
A novel SARS-CoV-2 variant, VOC 202012/01 (lineage B.1.1.7), emerged in southeast England in November 2020 and is rapidly spreading towards fixation. Using a variety of statistical and dynamic modelling approaches, we estimate that this variant has a 43-90% (range of 95% credible intervals 38-130%) higher reproduction number than preexisting variants. A fitted two-strain dynamic transmission model shows that VOC 202012/01 will lead to large resurgences of COVID-19 cases. Without stringent control measures, including limited closure of educational institutions and a greatly accelerated vaccine roll-out, COVID-19 hospitalisations and deaths across England in 2021 will exceed those in 2020. Concerningly, VOC 202012/01 has spread globally and exhibits a similar transmission increase (59-74%) in Denmark, Switzerland, and the United States.
Sujets)
COVID-19Résumé
Detection of SARS-CoV-2 infections to date has relied on RT-PCR testing. However, a failure to identify early cases imported to a country, bottlenecks in RT-PCR testing, and the existence of infections which are asymptomatic, sub-clinical, or with an alternative presentation than the standard cough and fever have resulted in an under-counting of the true prevalence of SARS-CoV-2. Here, we show how publicly available CDC influenza-like illness (ILI) outpatient surveillance data can be repurposed to estimate the detection rate of symptomatic SARS-CoV-2 infections. We find a surge of non-influenza ILI above the seasonal average and show that this surge is correlated with COVID case counts across states. By quantifying the number of excess ILI patients in March relative to previous years and comparing excess ILI to confirmed COVID case counts, we estimate the syndromic case detection rate of SARS-CoV-2 in the US to be less than 13%. If only 1/3 of patients infected with SARS-CoV-2 sought care, the ILI surge would correspond to more than 8.7 million new SARS-CoV-2 infections across the US during the three week period from March 8 to March 28. Combining excess ILI counts with the date of onset of community transmission in the US, we also show that the early epidemic in the US was unlikely to be doubling slower than every 4 days. Together these results suggest a conceptual model for the COVID epidemic in the US in which rapid spread across the US are combined with a large population of infected patients with presumably mild-to-moderate clinical symptoms. We emphasize the importance of testing these findings with seroprevalence data, and discuss the broader potential to use syndromic time series for early detection and understanding of emerging infectious diseases.