Estimating Viral Prevalence with Data Integration for Adaptive Two-Phase Pooled Sampling

Abstract

1. The COVID-19 pandemic has highlighted the importance of efficient sampling strategies and statistical methods for monitoring infection prevalence, both in humans and reservoir hosts. Pooled testing can be an efficient tool for learning pathogen prevalence in a population. Typically pooled testing requires a second phase follow up 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. 2. To estimate pathogen prevalence in a population, this manuscript presents an approach for data integration 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 integration procedure that combines pooled samples with individual samples for joint inferences about the population prevalence. 3. Data integration procedures result in more efficient estimation of prevalence than traditional procedures that only use individual samples or a single phase of pooled sampling. 4. The manuscript presents guidance on implementing the first phase and second phase sampling plans using data integration. 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.

From Protein to Pandemic: The Transdisciplinary Approach Needed to Prevent Spillover and the Next Pandemic

“…By contrast, cross-sectional sampling is more likely to identify pathogens that chronically infect the host; pathogens that cause transient infections or intermittent shedding such as filoviruses and paramyxoviruses may be missed altogether [ 49 ]. Optimal sampling methods, such as adaptive pooled sampling, Bayesian data integration, and model-guided sampling, may help determine the most efficient sampling frequency, intensity, and extent [ 18 , 56 , 57 ]. Metadata on host demography, condition, and immunity is critical to understanding how environmental stressors affect pathogen shedding [ 58 , 59 ], but a lack of reagents [ 60 ] and poor knowledge of bats’ novel antiviral defenses [ 61 ] impede these studies.…”

Pool samples to efficiently estimate pathogen prevalence dynamics

“…In possession of test results from pooled samples, estimating prevalence at the pool level is straightforward-one could, for example, employ a beta prior and binomial likelihood and proceed with a conjugate analysis analogous to the procedure described in Section 2.2.1 of [6]. However, when prevalence at the individual level is the quantity of interest, any inference on π must be related to p. To do so, we use the inverse prevalence transformation of [10] :…”

“…However, when individual results are not required and pathogen prevalence is the quantity of interest, as is the case in surveillance of reservoir host populations, follow-up testing can be omitted, reducing testing costs further. [6] found that pooled testing can be used to estimate prevalence as accurately and more efficiently than individual testing. However, their results are not directly applicable to observations that exhibit natural temporal correlation, such as multiple samples collected from the same site at different times.…”