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

Author

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  • Fergus J. Chadwick

    (Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow
    COVID-19 in LMICs Research Group, University of Glasgow)

  • Jessica Clark

    (Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow
    COVID-19 in LMICs Research Group, University of Glasgow)

  • Shayan Chowdhury

    (a2i, United Nations Development Program, ICT Ministry)

  • Tasnuva Chowdhury

    (Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow)

  • David J. Pascall

    (MRC Biostatistics Unit, University of Cambridge)

  • Yacob Haddou

    (Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow
    COVID-19 in LMICs Research Group, University of Glasgow)

  • Joanna Andrecka

    (Food and Agriculture Organisation of the United Nations in support of the UN Interagency Support Team)

  • Mikolaj Kundegorski

    (COVID-19 in LMICs Research Group, University of Glasgow
    School of Mathematics and Statistics, University of Glasgow)

  • Craig Wilkie

    (COVID-19 in LMICs Research Group, University of Glasgow
    School of Mathematics and Statistics, University of Glasgow)

  • Eric Brum

    (Food and Agriculture Organisation of the United Nations in support of the UN Interagency Support Team)

  • Tahmina Shirin

    (Institute of Epidemiology, Disease Control and Research, Ministry of Health)

  • A. S. M. Alamgir

    (Institute of Epidemiology, Disease Control and Research, Ministry of Health)

  • Mahbubur Rahman

    (Institute of Epidemiology, Disease Control and Research, Ministry of Health)

  • Ahmed Nawsher Alam

    (Institute of Epidemiology, Disease Control and Research, Ministry of Health)

  • Farzana Khan

    (Institute of Epidemiology, Disease Control and Research, Ministry of Health)

  • Ben Swallow

    (COVID-19 in LMICs Research Group, University of Glasgow
    School of Mathematics and Statistics, University of Glasgow)

  • Frances S. Mair

    (General Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow)

  • Janine Illian

    (COVID-19 in LMICs Research Group, University of Glasgow
    School of Mathematics and Statistics, University of Glasgow)

  • Caroline L. Trotter

    (Departments of Pathology and Veterinary Medicine, University of Cambridge)

  • Davina L. Hill

    (Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow
    COVID-19 in LMICs Research Group, University of Glasgow)

  • Dirk Husmeier

    (School of Mathematics and Statistics, University of Glasgow)

  • Jason Matthiopoulos

    (Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow
    COVID-19 in LMICs Research Group, University of Glasgow)

  • Katie Hampson

    (Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow
    COVID-19 in LMICs Research Group, University of Glasgow)

  • Ayesha Sania

    (Division of Developmental Neuroscience, Department of Psychiatry, Columbia University)

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.

Suggested Citation

  • 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. , 2022. "Combining rapid antigen testing and syndromic surveillance improves community-based COVID-19 detection in a low-income country," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30640-w
    DOI: 10.1038/s41467-022-30640-w
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    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Toxvaerd, Flavio & Makris, Miltiadis, 2021. "Introduction: Economic Contributions To Infection Control," National Institute Economic Review, National Institute of Economic and Social Research, vol. 257, pages 9-13, August.
    3. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
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