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Bhattacharyya R, Kundu R, Bhaduri R, Ray D, Beesley LJ, Salvatore M, Mukherjee B. Incorporating false negative tests in epidemiological models for SARS-CoV-2 transmission and reconciling with seroprevalence estimates. Sci Rep 2021; 11:9748. [PMID: 33963259 PMCID: PMC8105357 DOI: 10.1038/s41598-021-89127-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 04/21/2021] [Indexed: 12/24/2022] Open
Abstract
Susceptible-Exposed-Infected-Removed (SEIR)-type epidemiologic models, modeling unascertained infections latently, can predict unreported cases and deaths assuming perfect testing. We apply a method we developed to account for the high false negative rates of diagnostic RT-PCR tests for detecting an active SARS-CoV-2 infection in a classic SEIR model. The number of unascertained cases and false negatives being unobservable in a real study, population-based serosurveys can help validate model projections. Applying our method to training data from Delhi, India, during March 15-June 30, 2020, we estimate the underreporting factor for cases at 34-53 (deaths: 8-13) on July 10, 2020, largely consistent with the findings of the first round of serosurveys for Delhi (done during June 27-July 10, 2020) with an estimated 22.86% IgG antibody prevalence, yielding estimated underreporting factors of 30-42 for cases. Together, these imply approximately 96-98% cases in Delhi remained unreported (July 10, 2020). Updated calculations using training data during March 15-December 31, 2020 yield estimated underreporting factor for cases at 13-22 (deaths: 3-7) on January 23, 2021, which are again consistent with the latest (fifth) round of serosurveys for Delhi (done during January 15-23, 2021) with an estimated 56.13% IgG antibody prevalence, yielding an estimated range for the underreporting factor for cases at 17-21. Together, these updated estimates imply approximately 92-96% cases in Delhi remained unreported (January 23, 2021). Such model-based estimates, updated with latest data, provide a viable alternative to repeated resource-intensive serosurveys for tracking unreported cases and deaths and gauging the true extent of the pandemic.
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Affiliation(s)
- Rupam Bhattacharyya
- Department of Biostatistics, School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI, 48109-2029, USA
| | - Ritoban Kundu
- Indian Statistical Institute, Kolkata, West Bengal, 700108, India
| | - Ritwik Bhaduri
- Indian Statistical Institute, Kolkata, West Bengal, 700108, India
| | - Debashree Ray
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Lauren J Beesley
- Department of Biostatistics, School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI, 48109-2029, USA
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Maxwell Salvatore
- Department of Biostatistics, School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI, 48109-2029, USA
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Bhramar Mukherjee
- Department of Biostatistics, School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI, 48109-2029, USA.
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI, 48109, USA.
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