Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: Results of a Bayesian evidence synthesis model

Abstract

Reported COVID-19 cases and deaths provide a delayed and incomplete picture of SARS-CoV-2 infections in the United States (US). Accurate estimates of both the timing and magnitude of infections are needed to characterize viral transmission dynamics and better understand COVID-19 disease burden. We estimated time trends in SARS-CoV-2 transmission and other COVID-19 outcomes for every county in the US, from the first reported COVID-19 case in January 13, 2020 through January 1, 2021. To do so we employed a Bayesian modeling approach that explicitly accounts for reporting delays and variation in case ascertainment, and generates daily estimates of incident SARS-CoV-2 infections on the basis of reported COVID-19 cases and deaths. The model is freely available as the covidestim R package. Nationally, we estimated there had been 49 million symptomatic COVID-19 cases and 404,214 COVID-19 deaths by the end of 2020, and that 28% of the US population had been infected. There was county-level variability in the timing and magnitude of incidence, with local epidemiological trends differing substantially from state or regional averages, leading to large differences in the estimated proportion of the population infected by the end of 2020. Our estimates of true COVID-19 related deaths are consistent with independent estimates of excess mortality, and our estimated trends in cumulative incidence of SARS-CoV-2 infection are consistent with trends in seroprevalence estimates from available antibody testing studies. Reconstructing the underlying incidence of SARS-CoV-2 infections across US counties allows for a more granular understanding of disease trends and the potential impact of epidemiological drivers.

Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S

“…These surveys and other convenience and representative seroprevalence studies ( Havers et al, 2020 ; Naranbhai et al, 2020 ; Anand et al, 2020 ; Menachemi et al, 2020 ; Venugopal et al, 2021 ; Bajema et al, 2021 ; Lamba et al, 2021 ; Bruckner et al, 2021 ; Kline et al, 2021 ; Kalish et al, 2021 ; Jones et al, 2021 ; Sullivan et al, 2022 ; Routledge et al, 2022 ; also see https://covid19serohub.nih.gov) have provided estimates of the cumulative proportion of the population with a history of at least one infection with SARS-CoV-2 in the United States at the national and local level. Modeling approaches have also used seroprevalence studies to improve estimates of critical parameters (e.g., the infection fatality rate) or to compare to model outputs ( Irons and Raftery, 2021 ; Lu et al, 2021 ; Chitwood et al, 2022 ).…”

Population-level relative effectiveness of the COVID-19 vaccines and the contribution of naturally acquired immunity

“…For the Alpha wave, we used the proportion of the total population ever infected in each state before vaccine introduction, which was estimated using the previously developed Bayesian model [8] (see the next section “Estimating the proportion of the population ever infected” for more information). We also used estimates of the proportion of the total population ever infected as of December 2020 in each state [9]. For the Omicron wave, we used the proportion of the total population ever infected as of November 14, 2022 [10].…”

“…Second, we estimated the proportion of the population ever infected based on serosurvey data [8] and also used estimates from other studies [9,10].…”

“…First, we used state-level seroprevalence reported by the CDC, assuming that antibodies and immune protection wane at a similar speed [1]. Second, we estimated the proportion of the population ever infected based on serosurvey data [8] and also used estimates from other studies [9,10].…”

Tracking Covid-19 cases and deaths in the United States: metrics of pandemic progression derived from a queueing framework

“…We compared metrics for CDC data to two estimated data sources: Covidestim and COVID-19 Projections. Covidestim is an experimental methodology using Bayesian evidence synthesis to adjust reported data, accounting for asymptomatic infections, undercounting due to lack of availability of testing, and delays in case and death counts [4]. The model was run every 28 days, given the lag time for observed data, and is parameterized by four health states: asymptomatic, symptomatic, severe, and death [4].…”