2021/03/05 by Yair Daon, Daon, Yair, Amit Huppert +3
Biochemistry, Genetics and Molecular Biology · Mathematics · Medicine · #62K05 #92C60 (Primary) 62C10 (Secondary) #Applications (stat.AP) #COVID-19 epidemiological studies #Computation (stat.CO) #FOS: Biological sciences #FOS: Computer and information sciences #Quantitative Methods (q-bio.QM) #SARS-CoV-2 and COVID-19 Research #SARS-CoV-2 detection and testing #msc:62C10 #msc:62K05 #msc:92C60 #q-bio.QM #stat.AP #stat.CO
paper · pdf · doi:10.48550/arxiv.2103.03706
18 pages, 3 figures
arxiv created 2021/03/05 · openalex publication_date 2021/03/05 · arxiv updated 2021/03/08 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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. We present a novel pooling strategy that implements D-Optimal Pooling Experimental design (DOPE). 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 for maximizing it. DOPE outperforms common pooling strategies both in terms of lower error rates and fewer tests utilized. DOPE holds several additional advantages: 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. Hence, we believe that implementation of Bayesian D-optimal experimental design holds a great promise for the efforts of combating COVID-19 and other future pandemics.