DOPE: D-Optimal Pooling Experimental design with application for SARS-CoV-2 screening

Abstract

Objective Testing individuals for the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen causing the coronavirus disease 2019 (COVID-19), is crucial for curtailing transmission chains. Moreover, rapidly testing many potentially infected individuals is often a limiting factor in controlling COVID-19 outbreaks. Hence, pooling strategies, wherein individuals are grouped and tested simultaneously, are employed. Here, we present a novel pooling strategy that builds on the Bayesian D-optimal experimental design criterion. Materials and methods Our strategy, called DOPE (D-Optimal Pooling Experimental design), is built on a novel Bayesian formulation of pooling. DOPE defines optimal pooled tests as those maximizing the mutual information between data and infection states. We estimate said mutual information via Monte-Carlo sampling and employ a discrete optimization heuristic to maximize it. Results We compare DOPE to other, commonly used pooling strategies, as well as to individual testing. DOPE dominates the other strategies as it yields lower error rates while utilizing fewer tests. We show that DOPE maintains this dominance for a variety of infection prevalence values. Discussion DOPE has several additional advantages over common pooling strategies: it provides posterior distributions of the probability of infection, rather than only binary classification outcomes; it naturally incorporates prior information of infection probabilities and test error rates; and finally, it can be easily extended to include other, newly discovered information regarding COVID-19. Conclusion DOPE can substantially improve accuracy and throughput over current pooling strategies. Hence, DOPE can facilitate rapid testing and aid the efforts of combating COVID-19 and other future pandemics.

Optimization of Screening Strategies for COVID-19: Scoping Review

“…Wu et al [ 78 ] improved the current hypercube testing strategy by calculating the prevalence, edge, and dimension because every edge had a best performance range, and hypercube pooling with edge=3 may not be the optimal strategy in different outbreaks. Daon et al [ 79 ] used a Bayesian model to determine the best combination of pool size, detection steps, repeat detection, and split sample detection to maximize the mutual information between the infection status and testing results. However, this is limited to a simulation analysis.…”

Sample pooling: burden or solution?

“…Other alternatives—such as the prevalence spiralling method in conjunction with non-hierarchical matrix-based pooling strategies—can also be used, where high-risk individuals are clustered on one side of the array. Recently, Daon et al proposed a pooling strategy that uses a Bayesian D-Optimal pooling experimental design by maximizing the mutual information between the data and the infection states [ 46 ]. The authors report lower rates and fewer test utilizations.…”

Refuting Causal Relations in Epidemiological Time Series

“…neuroscience [5], geoscience [6], and infectious disease epidemiology [7]. The latter field has particularly grown in interest since the COVID-19 pandemic, as understanding causal relations during outbreaks may facilitate implementations of interventions such as vaccination [8][9][10][11], concentrated testing efforts [12][13][14][15][16] and other non-pharmaceutical interventions [17].…”