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

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

SARS-CoV-2/COVID-19 Testing: The Tower of Babel

Background and aim: Testing represents one of the main pillars of public health response to SARS-CoV-2/COVID-19 pandemic. This paper shows how accuracy and utility of testing programs depend not just on the type of tests, but on the context as well. Methods: We describe the testing methods that have been developed and the possible testing strategies; then, we focus on two possible methods of population-wide testing, i.e., pooled testing and testing with rapid antigen tests. We show the accuracy of split-pooling method and how, in different pre-test probability scenarios, the positive and negative predictive values vary using rapid antigen tests. Results: Split-pooling, followed by retesting of negative results, shows a higher sensitivity than individual testing and requires fewer tests. In case of low pre-test probability, a negative result with antigen test could allow to rule out the infection, while, in case of a positive result, a confirmatory molecular test would be necessary. Conclusions: Test performance alone is not enough to properly choose which test to use; goals and context of the testing program are essential. We advocate the use of pooled strategies when planning population-wide screening, and the weekly use of rapid tests for close periodic monitoring in low-prevalence populations