Ritwik Bhaduri

2022 · Statistics in Medicine

Extending the susceptible-exposed-infected-removed (SEIR) model to handle the false negative rate and symptom-based administration of COVID-19 diagnostic tests: SEIR-fansy

Ritwik Bhaduri, Ritoban Kundu, Soumik Purkayastha, Michael Kleinsasser, Lauren J. Beesley, Bhramar Mukherjee, et al.

medRxiv preprint Journal article

Abstract

False negative rates of diagnostic tests for SARS-CoV-2, together with selection bias due to prioritized testing, can result in inaccurate modeling of COVID-19 transmission dynamics based on reported case counts. We propose an extension of the widely used Susceptible-Exposed-Infected-Removed (SEIR) model that accounts for misclassification error and selection bias, and derive an analytic expression for the basic reproduction number R₀ as a function of false negative rates of the diagnostic tests and selection probabilities for getting tested. Analyzing data from the first two waves of the pandemic in India, we show that correcting for misclassification and selection leads to more accurate prediction in a test sample. We provide estimates of undetected infections and deaths between April 1, 2020 and August 31, 2021. At the end of the first wave in India, the estimated under-reporting factor for cases was 11.1 (95% CI: 10.7, 11.5) and for deaths 3.58 (95% CI: 3.5, 3.66) as of February 1, 2021, changing to 19.2 (95% CI: 17.9, 19.9) and 4.55 (95% CI: 4.32, 4.68) as of July 1, 2021. Extensive simulation studies demonstrate the effect of misclassification and selection on estimation of R₀ and prediction of future infections. An R-package SEIRfansy is developed for broader dissemination.

BibTeX

@article{bhaduri2022seirfansy,
  title={Extending the susceptible-exposed-infected-removed ({SEIR}) model to handle the false negative rate and symptom-based administration of {COVID}-19 diagnostic tests: {SEIR-fansy}},
  author={Ritwik Bhaduri and Ritoban Kundu and Soumik Purkayastha and Michael Kleinsasser and Lauren J. Beesley and Bhramar Mukherjee and others},
  journal={Statistics in Medicine},
  year={2022},
  doi={10.1002/sim.9357},
  abstract={False negative rates of diagnostic tests for SARS-CoV-2, together with selection bias due to prioritized testing, can result in inaccurate modeling of COVID-19 transmission dynamics based on reported case counts. We propose an extension of the widely used SEIR model that accounts for misclassification error and selection bias, and derive an analytic expression for the basic reproduction number R0 as a function of false negative rates of the diagnostic tests and selection probabilities for getting tested. Analyzing data from the first two waves of the pandemic in India, we show that correcting for misclassification and selection leads to more accurate prediction in a test sample. An R-package SEIRfansy is developed for broader dissemination.}
}