Journal
PLOS BIOLOGY
Volume 19, Issue 6, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pbio.3001307
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Funding
- National Science Foundation (NSF) Ecology and Evolution of Infectious Diseases (EEID) program award Division of Environmental Biology (DEB) [1911962]
- Biotechnology and Biological Sciences Research Council (BBSRC) [BB/T004312/1]
- National Science Foundation (NSF) Division of Environmental Biology (DEB) COVID-19 RAPID [2028301, 2037885]
- Huck Institutes for the Life Sciences at The Pennsylvania State University
- National Institutes of Health
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service
- U.S.Geological Survey
- Li Ka Shing Foundation
- Department of Science and Innovation
- National Research Foundation (NRF)
- BBSRC [BB/T004312/1] Funding Source: UKRI
- Direct For Biological Sciences
- Division Of Environmental Biology [1911962, 2037885] Funding Source: National Science Foundation
- Direct For Biological Sciences
- Division Of Environmental Biology [2028301] Funding Source: National Science Foundation
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By focusing on specific objectives such as individual treatment or disease prediction and control, and drawing from capture-recapture methods to deal with nonrandom sampling and testing errors, public health objectives can be achieved even with limited test availability when testing programs are designed a priori to meet those objectives.
More than 1.6 million Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests were administered daily in the United States at the peak of the epidemic, with a significant focus on individual treatment. Here, we show that objective-driven, strategic sampling designs and analyses can maximize information gain at the population level, which is necessary to increase situational awareness and predict, prepare for, and respond to a pandemic, while also continuing to inform individual treatment. By focusing on specific objectives such as individual treatment or disease prediction and control (e.g., via the collection of population-level statistics to inform lockdown measures or vaccine rollout) and drawing from the literature on capture-recapture methods to deal with nonrandom sampling and testing errors, we illustrate how public health objectives can be achieved even with limited test availability when testing programs are designed a priori to meet those objectives.
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