The seroprevalence data, which provide an out-of-sample corroboration of the model fitting, were adjusted for the waning of antibody levels following adaptive immune response 11, 12 (Extended Data Fig. To further validate the fitting, we compared model estimates of cumulative infections to findings from US Centers for Disease Control and Prevention (CDC) seroprevalence surveys conducted at site and state levels 3. Distributions are obtained from n = 100 ensemble members. Monthly posterior estimates are presented for March to December 2020. The centre line shows the median, box bounds represent 25th and 75th percentiles, and whiskers show 2.5th and 97.5th percentiles. c, Distributions of estimated ascertainment rate in the United States and five metropolitan areas. Details on the serological survey are provided in Supplementary Information. Centres and whiskers show medians and 95% CIs, and colour indicates the sample collection date in each location. The inset shows residuals of inference (inferred percentage of infected population minus adjusted seroprevalence). b, Comparison between inferred percentage cumulative infections and seroprevalence in ten locations adjusted for antibody waning. Solid and dashed lines show the median estimate and 95% CIs, respectively. 3, 4, Supplementary Information).Ī, Model fitting to daily case numbers (blue dots) in the United States and the New York metropolitan area (inset). These inference results are robust to parameter settings and model configurations (Extended Data Figs. 1a), as well as in major metropolitan areas and at county scales (Extended Data Fig. The model fitting to observed case data captures the three waves of the outbreak as manifest at national scales (Fig. Synthetic tests indicate that the inference approach can recover key time-varying parameters across a diversity of simulation scenarios (Extended Data Fig. The Bayesian inference supports a fitting of the model to case observations and estimation of unobserved state variables (for example, population susceptibility within a county) and system parameters (for example, the ascertainment rate in each county). The model depicts both documented and undocumented infections and is coupled with an iterative Bayesian inference algorithm-the ensemble adjustment Kalman filter-which assimilates observations of daily cases in each county, as well as population movement between counties 9, 10 ( Supplementary Information). Here we use a county-resolved metapopulation model to simulate the transmission of SARS-CoV-2 within and between the 3,142 counties of the United States. To understand the transmission of the virus and better control its progression in the future, it is vital that the epidemiological features that have supported these outbreaks are quantified and analysed in both space and time. Over the course of the year, three pandemic waves took place: (1) a spring outbreak in select, mostly urban areas following the introduction of the virus to the United States (2) a summer wave that predominantly affected the southern half of the country and (3) an autumn–winter wave that remained pervasive until the spring of 2021. The first US COVID-19 case was identified in Washington state on 20 January 2020 2. By contrast, the infection fatality rate fell to 0.3% by year’s end.ĭuring 2020, the United States documented more COVID-19 cases and deaths than any other country in the world 1. Community infectious rates, the percentage of people harbouring a contagious infection, increased above 0.8% (0.6–1.0%) before the end of the year, and were as high as 2.4% in some major metropolitan areas. Population susceptibility at the end of the year was 69.0% (63.6–75.4%), indicating that about one third of the US population had been infected. The pandemic in the United States during 2020 was characterized by national ascertainment rates that increased from 11.3% (95% credible interval (CI): 8.3–15.9%) in March to 24.5% (18.6–32.3%) during December. Here we use a data-driven model-inference approach to simulate the pandemic at county-scale in the United States during 2020 and estimate critical, time-varying epidemiological properties underpinning the dynamics of the virus. Many of the epidemiological features responsible for observed rates of morbidity and mortality have been reported 4, 5, 6, 7, 8 however, the overall burden and characteristics of COVID-19 in the United States have not been comprehensively quantified. The COVID-19 pandemic disrupted health systems and economies throughout the world during 2020 and was particularly devastating for the United States, which experienced the highest numbers of reported cases and deaths during 2020 1, 2, 3.
0 Comments
Leave a Reply. |