Snowball Sampling Study Design for Serosurveys in the Early COVID-19 Pandemic

Abstract

Serological surveys can provide evidence of cases that were not previously detected, depict the spectrum of disease severity and estimate the proportion of asymptomatic infection. To capture these parameters, survey sample sizes may need to be very large, especially when the overall infection rate is still low. Therefore, we describe a novel method of “snowball sampling” to enrich serological surveys by using contact networks identified in the early SARS-CoV-2 pandemic. By testing all contacts of known index cases, snowball sampling efficiently builds a sample to answer many key questions about a new outbreak, such as estimating asymptomatic proportion of all infected cases, the probability of a given clinical presentation for a seropositive individual, or the association between characteristics of either the host or the infection and seropositivity among contacts of index individuals. Although clustering effects need to be considered since identified cases have common exposures, snowball sampling can be a more efficient way to achieve adequate statistical power than random sampling, as demonstrated in the COVID-19 example. We hope such study designs can be applied to provide valuable information to slow the onward spread of the pandemic as it enters its next stage.