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

Autor: Fergus J. Chadwick, Jessica Clark, Shayan Chowdhury, Tasnuva Chowdhury, David J. Pascall, Yacob Haddou, Joanna Andrecka, Mikolaj Kundegorski, Craig Wilkie, Eric Brum, Tahmina Shirin, A. S. M. Alamgir, Mahbubur Rahman, Ahmed Nawsher Alam, Farzana Khan, Ben Swallow, Frances S. Mair, Janine Illian, Caroline L. Trotter, Davina L. Hill, Dirk Husmeier, Jason Matthiopoulos, Katie Hampson, Ayesha Sania
Přispěvatelé: Chadwick, Fergus J [0000-0001-8650-1938], Clark, Jessica [0000-0003-1692-899X], Chowdhury, Shayan [0000-0001-5153-9055], Chowdhury, Tasnuva [0000-0003-0660-9784], Pascall, David J [0000-0002-7543-0860], Kundegorski, Mikolaj [0000-0002-0982-9371], Wilkie, Craig [0000-0003-0805-0195], Brum, Eric [0000-0002-0244-7178], Rahman, Mahbubur [0000-0001-8577-8281], Alam, Ahmed Nawsher [0000-0002-7962-0725], Swallow, Ben [0000-0002-0227-2160], Illian, Janine [0000-0002-6130-2796], Trotter, Caroline L [0000-0003-4000-2708], Hill, Davina L [0000-0001-9085-6192], Husmeier, Dirk [0000-0003-1673-7413], Matthiopoulos, Jason [0000-0003-3639-8172], Hampson, Katie [0000-0001-5392-6884], Apollo - University of Cambridge Repository, Pascall, David [0000-0002-7543-0860], Trotter, Caroline [0000-0003-4000-2708], University of St Andrews. School of Mathematics and Statistics
Jazyk: angličtina
Rok vydání: 2022
Předmět:
ISSN: 2041-1723
Popis: Funder: Juniper Consortium MR/V038613/1
Funder: Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation)
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.
Databáze: OpenAIRE
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