Estimating COVID-19 Hospitalizations in the United States with surveillance data using a Bayesian Hierarchical model (preprint)

IntroductionIn the United States, COVID-19 is a nationally notifiable disease, cases and hospitalizations are reported to the CDC by states. Identifying and reporting every case from every facility in the United States may not be feasible in the long term. Creating sustainable methods for estimating burden of COVID-19 from established sentinel surveillance systems is becoming more important. We aimed to provide a method leveraging surveillance data to create a long-term solution to estimate monthly rates of hospitalizations for COVID-19. MethodsWe estimated monthly hospitalization rates for COVID-19 from May 2020 through April 2021 for the 50 states using surveillance data from COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) and a Bayesian hierarchical model for extrapolation. We created a model for six age groups (0-17, 18-49, 50-64, 65-74, 75-84, and [≥]85 years), separately. We identified covariates from multiple data sources that varied by age, state, and/or month, and performed covariate selection for each age group based on two methods, Least Absolute Shrinkage and Selection Operator (LASSO) and Spike and Slab selection methods. We validated our method by checking sensitivity of model estimates to covariate selection and model extrapolation as well as comparing our results to external data. ResultsWe estimated 3,569,500 (90% Credible Interval:3,238,000 - 3,934,700) hospitalizations for a cumulative incidence of 1,089.8 (988.6 - 1,201.3) hospitalizations per 100,000 population with COVID-19 in the United States from May 2020 through April 2021. Cumulative incidence varied from 352 - 1,821per 100,000 between states. The age group with the highest cumulative incidence was aged [≥]85 years (5,583.1; 5,061.0 - 6,157.5). The monthly hospitalization rate was highest in December (183.8; 154.5 - 218.0). Our monthly estimates by state showed variations in magnitudes of peak rates, number of peaks and timing of peaks between states. ConclusionsOur novel approach to estimate COVID-19 hospitalizations has potential to provide sustainable estimates for monitoring COVID-19 burden, as well as a flexible framework leveraging surveillance data.

Methods for Estimation of SARS-CoV-2 Seroprevalence and Reported COVID-19 Cases in U.S. Children, August 2020 - May 2021 (preprint)

Background and ObjectivesCase-based surveillance of pediatric COVID-19 cases underestimates the prevalence of SARS-CoV-2 infections among children and adolescents. Our objectives were to: 1) estimate monthly SARS-CoV-2 antibody seroprevalence among children aged 0-17 years and 2) calculate ratios of SARS-CoV-2 infections to reported COVID-19 cases among children and adolescents in 14 U.S. states. MethodsUsing data from commercial laboratory seroprevalence surveys, we estimated monthly SARS-CoV-2 antibody seroprevalence among children aged 0-17 years from August 2020 through May 2021. Seroprevalence estimates were based on SARS-CoV-2 anti-nucleocapsid immunoassays from February to May 2021. We compared estimated numbers of children infected with SARS-CoV-2 by May 2021 to cumulative incidence of confirmed and probable COVID-19 cases from case-based surveillance, and calculated infection: case ratios by state and type of anti-SARS-CoV-2 nucleocapsid immunoassay used for seroprevalence testing. ResultsAnalyses included 67,321 serum specimens tested for SARS-CoV-2 antibodies among children in 14 U.S. states. Estimated ratios of SARS-CoV-2 infections to reported confirmed and probable COVID-19 cases among children and adolescents varied by state and type of immunoassay, ranging from 0.8-13.3 in May 2021. ConclusionsThrough May 2021, the majority of children in selected states did not have detectable SARS-CoV-2 nucleocapsid antibodies. Case-based surveillance underestimated the number of children infected with SARS-CoV-2, however the predicted extent of the underestimate varied by state, immunoassay, and over time. Continued monitoring of pediatric SARS-CoV-2 antibody seroprevalence should inform prevention and vaccination strategies.

COVID-19 case definitions, diagnostic testing criteria, and surveillance across the pandemic's 25 highest burden countries (preprint)

Objective: We compared suspect, probable, and confirmed case definitions, as well as diagnostic testing criteria, used in the COVID-19 pandemic's 25 highest burden countries to aid interpretation of global and national surveillance data. Methods: We identified the COVID-19 pandemic's 25 countries with the highest disease burden based on the number of cumulative reported cases to the World Health Organization (WHO) as of 1 October 2020. We searched official websites of these countries for suspect, probable, and confirmed case definitions. Given that confirmation of COVID-19 usually requires diagnostic testing, we also searched for diagnostic testing eligibility criteria in these countries. Extracted case definitions and testing criteria were managed in a database and analyzed in Microsoft Excel. Findings: We identified suspect, probable, and confirmed case definitions in 96%, 64%, and 100% of countries, respectively. Testing criteria were identified in 100% of countries. 56% of identified countries followed WHO recommendations for using a combination of clinical and epidemiological criteria as part of the suspect case definition. 75% of identified countries followed WHO recommendations on using clinical, epidemiological, and diagnostic criteria for probable cases. 72% of countries followed WHO recommendations on using PCR testing for confirming a case of COVID-19. Finally, 64% of countries used testing eligibility criteria at least as permissive as WHO. Conclusion: There is marked heterogeneity in who is eligible for testing in countries and how countries define a case of COVID-19. This affects the ability to compare burden, transmission, and response impact estimates derived from case surveillance data across countries.