Combining rapid antigen testing and syndromic surveillance improves community-based COVID-19 detection in a low-income country.

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Diagnostics for COVID-19 detection are limited in many settings. Syndromic surveillance is often the only means to identify cases but lacks specificity. Rapid antigen testing is inexpensive and easy-to-deploy but can lack sensitivity. We examine how combining these approaches can improve surveillance for guiding interventions in low-income communities in Dhaka, Bangladesh. Rapid-antigen-testing with PCR validation was performed on 1172 symptomatically-identified individuals in their homes. Statistical models were fitted to predict PCR-status using rapid-antigen-test results, syndromic data, and their combination. Under contrasting epidemiological scenarios, the models' predictive and classification performance was evaluated. Models combining rapid-antigen-testing and syndromic data yielded equal-to-better performance to rapid-antigen-test-only models across all scenarios with their best performance in the epidemic growth scenario. These results show that drawing on complementary strengths across rapid diagnostics, improves COVID-19 detection, and reduces false-positive and -negative diagnoses to match local requirements; improvements achievable without additional expense, or changes for patients or practitioners.


Funder: Juniper Consortium MR/V038613/1

Funder: Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation)

Article, /692/699/255/2514, /692/700/478/174, /692/700/139, /692/1807, /631/326/596/4130, article
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Nat Commun
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Springer Science and Business Media LLC
Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation) (INV-022851, INV-022851)
RCUK | Engineering and Physical Sciences Research Council (EPSRC) (EP/R513222/1)
Wellcome Trust (Wellcome) (207569/Z/17/Z)