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

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. Rapid antigen tests and syndromic surveillance for identification of COVID-19 cases are limited by low sensitivity and specificity, respectively. Here, the authors use data from Bangladesh and show that combining the two methods improves diagnostic accuracy in a range of epidemiological scenarios.

Automated summary

This study examines how combining rapid antigen testing and syndromic surveillance can improve COVID-19 detection and reduce false-positive and false-negative diagnoses in low-income communities in Dhaka, Bangladesh. The results show that this combination approach yields equal-to-better performance compared to using rapid antigen tests alone, with the best performance in the epidemic growth scenario.