Combining rapid antigen testing and syndromic surveillance improves community-based COVID-19 detection in a low-income country
Abstract
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.
Citation
Chadwick , F J , Clark , J , Chowdhury , S , Chowdhury , T , Pascall , D J , Haddou , Y , Andrecka , J , Kundegorski , M , Wilkie , C , Brum , E , Shirin , T , Alamgir , A S M , Rahman , M , Alam , A N , Khan , F , Swallow , B , Mair , F S , Illian , J , Trotter , C L , Hill , D L , Husmeier , D , Matthiopoulos , J , Hampson , K & Sania , A 2022 , ' Combining rapid antigen testing and syndromic surveillance improves community-based COVID-19 detection in a low-income country ' , Nature Communications , vol. 13 , 2877 . https://doi.org/10.1038/s41467-022-30640-w
Publication
Nature Communications
Status
Peer reviewed
ISSN
2041-1723Type
Journal article
Description
This work is supported by a grant from the Bill and Melinda Gates Foundation to FAO (INV-022851). F.J.C. is funded by EPSRC (EP/R513222/1), D.J.P. by the JUNIPER consortium (MR/V038613/1) and K.H. by Wellcome (207569/Z/17/Z).Collections
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