Ritwik Bhaduri

2021 · Scientific Reports

Incorporating false negative tests in epidemiological models for SARS-CoV-2 transmission and reconciling with seroprevalence estimates

Rupam Bhattacharyya, Ritoban Kundu, Ritwik Bhaduri, Debashree Ray, Lauren J. Beesley, Maxwell Salvatore, Bhramar Mukherjee

Journal article

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 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 (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% of cases in Delhi remained unreported. Updated calculations yield estimated underreporting factors for cases at 13–22 (deaths: 3–7) on January 23, 2021, consistent with the fifth round of serosurveys for Delhi with an estimated 56.13% IgG antibody prevalence. Such model-based estimates provide a viable alternative to repeated resource-intensive serosurveys for tracking unreported cases and deaths.

BibTeX

@article{bhattacharyya2021incorporating,
  title={Incorporating false negative tests in epidemiological models for {SARS-CoV-2} transmission and reconciling with seroprevalence estimates},
  author={Rupam Bhattacharyya and Ritoban Kundu and Ritwik Bhaduri and Debashree Ray and Lauren J. Beesley and Maxwell Salvatore and Bhramar Mukherjee},
  journal={Scientific Reports},
  year={2021},
  doi={10.1038/s41598-021-89127-1},
  abstract={SEIR-type epidemiologic models can predict unreported cases and deaths assuming perfect testing. We apply a method to account for the high false negative rates of diagnostic RT-PCR tests for SARS-CoV-2 in a classic SEIR model and validate projections against population-based serosurveys for Delhi, India. Applying our method to training data from Delhi (March 15-June 30, 2020), we estimate the underreporting factor for cases at 34-53 (deaths: 8-13), consistent with the first round of serosurveys with 22.86% IgG antibody prevalence. Such model-based estimates provide a viable alternative to resource-intensive serosurveys for tracking unreported cases and deaths.}
}