Estimating viral prevalence with data fusion for adaptive two-phase pooled sampling.

Andrew Hoegh,Alison J Peel,Alex Washburne,Raina K Plowright,Wyatt Madden,Manuel Ruiz Aravena,Aaron Morris

doi:10.1002/ece3.8107

Abstract

The COVID‐19 pandemic has highlighted the importance of efficient sampling strategies and statistical methods for monitoring infection prevalence, both in humans and in reservoir hosts. Pooled testing can be an efficient tool for learning pathogen prevalence in a population. Typically, pooled testing requires a second‐phase retesting procedure to identify infected individuals, but when the goal is solely to learn prevalence in a population, such as a reservoir host, there are more efficient methods for allocating the second‐phase samples.To estimate pathogen prevalence in a population, this manuscript presents an approach for data fusion with two‐phased testing of pooled samples that allows more efficient estimation of prevalence with less samples than traditional methods. The first phase uses pooled samples to estimate the population prevalence and inform efficient strategies for the second phase. To combine information from both phases, we introduce a Bayesian data fusion procedure that combines pooled samples with individual samples for joint inferences about the population prevalence.Data fusion procedures result in more efficient estimation of prevalence than traditional procedures that only use individual samples or a single phase of pooled sampling.The manuscript presents guidance on implementing the first‐phase and second‐phase sampling plans using data fusion. Such methods can be used to assess the risk of pathogen spillover from reservoir hosts to humans, or to track pathogens such as SARS‐CoV‐2 in populations.

Highlights

The rapid pandemic spread of COVID-19 has overwhelmed health systems globally, from funding and supply chains to testing and hospital capacity
In studies of reservoir hosts, the research question is not necessarily whether an individual is infected, but rather the goal can be to estimate the prevalence in the reservoir host population, and how this changes over space and time
Estimating prevalence in reservoir hosts is critical for understanding drivers of pathogen spillover and precise estimates of population prevalence require testing a large number of samples (Plowright et al, 2017, 2019)

Summary

| INTRODUCTION

The rapid pandemic spread of COVID-19 has overwhelmed health systems globally, from funding and supply chains to testing and hospital capacity. In contrast studies where the goal is case identification (Zhang et al, 2013), individual results may not be required and population-level estimates of prevalence are often sufficient to identify hosts and understand transmission dynamics within their populations. In some cases, this will involve collecting new samples with purpose-fit sampling designs, or in other cases, sample banks may already exist that can be screened for coronaviruses.

| MATERIALS AND METHODS

| Conclusion

| DISCUSSION