Sample Size Calculations for Variant Surveillance in the Presence of Biological and Systematic Biases

As demonstrated during the SARS-CoV-2 pandemic, detecting and tracking the emergence and spread of pathogen variants is an important component of monitoring infectious disease outbreaks. Pathogen genome sequencing has emerged as the primary tool for variant characterization, so it is important to consider the number of sequences needed when designing surveillance programs or studies, both to ensure accurate conclusions and to optimize use of limited resources. However, current approaches to calculating sample size for variant monitoring often do not account for the biological and logistical processes that can bias which infections are detected and which samples are ultimately selected for sequencing. In this manuscript, we introduce a framework that models the full process-- including potential sources of bias--from infection detection to variant characterization, and we demonstrate how to use this framework to calculate appropriate sample sizes for sequencing-based surveillance studies. We consider both cross-sectional and continuous sampling, and we have implemented our method in a publicly available tool that allows users to estimate necessary sample sizes given a specific aim (e.g., variant detection or measuring variant prevalence) and sampling method. Our framework is designed to be easy to use, while also flexible enough to be adapted to other pathogens and surveillance scenarios.

A prospective study of asymptomatic SARS-CoV-2 infection among individuals involved in academic research under limited operations during the COVID-19 pandemic

BackgroundEarly in the pandemic, transmission risk from asymptomatic infection was unclear making it imperative to monitor infection in workplace settings. Further, data on SARS-CoV-2 seroprevalence within university populations has been limited. MethodsWe performed a longitudinal study of University research employees on campus July-December 2020. We conducted questionnaires on COVID-19 risk factors, RT-PCR testing, and SARS-CoV-2 serology using an in-house spike RBD assay, laboratory-based Spike NTD assay, and standard nucleocapsid platform assay. We estimated prevalence and cumulative incidence of seroconversion with 95% confidence intervals using the inverse of the Kaplan-Meier estimator. Results910 individuals were included in this analysis. At baseline, 6.2% (95% CI 4.29-8.19) were seropositive using the spike RBD assay; four (0.4%) were seropositive using the nucleocapsid assay, and 44 (4.8%) using the Spike NTD assay. Cumulative incidence was 3.61% (95% CI: 2.04-5.16). Six asymptomatic individuals had positive RT-PCR results. ConclusionsPrevalence and incidence of SARS-CoV-2 infections was low; however differences in target antigens of serological tests provided different estimates. Future research on appropriate methods of serological testing in unvaccinated and vaccinated populations is needed. Frequent RT-PCR testing of asymptomatic individuals is required to detect acute infections, and repeated serosurveys are beneficial for monitoring subclinical infection.