Ritwik Bhaduri

2021 · BMC Infectious Diseases

A comparison of five epidemiological models for transmission of SARS-CoV-2 in India

Soumik Purkayastha, Rupam Bhattacharyya, Ritwik Bhaduri, Ritoban Kundu, Xuelin Gu, Maxwell Salvatore, Debashree Ray, Swapnil Mishra, et al.

medRxiv preprint Journal article

Abstract

Many popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures, lockdowns, and other non-pharmaceutical interventions. We study how five epidemiological models forecast and assess the course of the pandemic in India: a baseline curve-fitting model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM). Using COVID-19 case-recovery-death count data reported in India from March 15 to October 15, 2020, we generate predictions from each of the five models from October 16 to December 31, 2020, and compare prediction accuracy with respect to reported cumulative and active case counts and death counts. For cumulative case counts, SMAPE values range from 2.25% (SAPHIRE) to 6.89% (baseline). Three models also return total (reported + unreported) cumulative case counts: the SEIR-fansy model yields an underreporting factor of 7.25 and the ICM model yields 4.54 for cumulative cases as of October 31, 2020. Overall, the SEIR-fansy model appeared to be a good choice with a publicly available R-package and desired flexibility plus accuracy.

BibTeX

@article{purkayastha2021comparison,
  title={A comparison of five epidemiological models for transmission of {SARS-CoV-2} in India},
  author={Soumik Purkayastha and Rupam Bhattacharyya and Ritwik Bhaduri and Ritoban Kundu and Xuelin Gu and Maxwell Salvatore and Debashree Ray and Swapnil Mishra and others},
  journal={BMC Infectious Diseases},
  year={2021},
  doi={10.1186/s12879-021-06077-9},
  abstract={We study how five epidemiological models forecast and assess the course of the COVID-19 pandemic in India: a baseline curve-fitting model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM). Using COVID-19 case-recovery-death count data reported in India from March 15 to October 15, 2020, we generate predictions and compare prediction accuracy. For cumulative case counts, SMAPE values range from 2.25% (SAPHIRE) to 6.89% (baseline). Overall, the SEIR-fansy model appeared to be a good choice with a publicly available R-package and desired flexibility plus accuracy.}
}