A novel compartmental model to capture the nonlinear trend of COVID-19

Somayeh Bakhtiari Ramezani; Amin Amirlatifi; Shahram Rahimi

doi:10.1016/j.compbiomed.2021.104421

A novel compartmental model to capture the nonlinear trend of COVID-19

Comput Biol Med. 2021 Jul:134:104421. doi: 10.1016/j.compbiomed.2021.104421. Epub 2021 Apr 30.

Authors

Somayeh Bakhtiari Ramezani¹, Amin Amirlatifi², Shahram Rahimi³

Affiliations

¹ Computer Science and Engineering, Mississippi State University, Starkville, MS, USA. Electronic address: sb3182@msstate.edu.
² Swalm School of Chemical Engineering, Mississippi State University, Starkville, MS, USA.
³ Computer Science and Engineering, Mississippi State University, Starkville, MS, USA.

Abstract

The COVID-19 pandemic took the world by surprise and surpassed the expectations of epidemiologists, governments, medical experts, and the scientific community as a whole. The majority of epidemiological models failed to capture the non-linear trend of the susceptible compartment and were unable to model this pandemic accurately. This study presents a variant of the well-known SEIRD model to account for social awareness measures, variable death rate, and the presence of asymptomatic infected individuals. The proposed SEAIRDQ model accounts for the transition of individuals between the susceptible and social awareness compartments. We tested our model against the reported cumulative infection and death data for different states in the US and observed over 98.8% accuracy. Results of this study give new insights into the prevailing reproduction number and herd immunity across the US.

Keywords: COVID-19; Compartmental modeling; Herd immunity; Nonlinear trend; Reproduction Number,s SEIRD; Social awareness.

MeSH terms

COVID-19*
Humans
Models, Statistical
Pandemics*
SARS-CoV-2