Estimating viral prevalence with data fusion 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 in reservoir hosts. Pooled testing can be an efficient tool for learning pathogen prevalence in a population. Typically, pooled testing requires a secondphase 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. 2. 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.3. 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.4. The manuscript presents guidance on implementing the first-phase and secondphase 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.

Reconstructing prevalence dynamics of wildlife pathogens from pooled and individual samples

“…To date, most approaches have used binary test data for estimating prevalence using pooled samples [14,[16][17][18]. Most assays, however, provide quantitative data, which are then turned into a binary negative/positive result based on a threshold value, and the additional information provided by the quantitative assay is lost.…”

“…Most studies on the analysis of pooled samples focus on testing protocols for cost reduction, with the goal of eventually identifying the positive individuals [3,14,15]. Perhaps for this reason, few methods have been developed for explicitly using pooled samples to estimate prevalence in the population [10,12,[16][17][18][19], and even fewer have attempted to use the actual concentration of the infectious agent in the pooled sample to estimate how many of the contributing individuals are positive [12,13]. Furthermore, to our knowledge no methods exist that are able to estimate the proportion of positive individuals using concentration when the underlying distribution of test values does not follow a standard-family (e.g.…”