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

doi:10.1016/j.compbiomed.2021.104421

Computers in Biology and Medicine

Volume 134, July 2021, 104421

https://doi.org/10.1016/j.compbiomed.2021.104421 Get rights and content

Highlights

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A new compartmental model that captures the nonlinear behavior of the COVID-19 pandemic.
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Findings on several key factors in explaining the behavior of COVID-19.
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Determination of the reproduction number and the herd immunity percentage for the US.
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Facilitating decision making and better explaining the behavior of the COVID-19 pandemic.

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

Compartmental modeling

COVID-19

Herd immunity

Nonlinear trend

Social awareness

Reproduction Number,s SEIRD

Cited by (0)

Somayeh Bakhtiari Ramezani is a Ph.D. Candidate at the Computer Science and Engineering Department, Mississippi State University, with research interests spanning compartmental modeling, optimization, machine learning, quantum computation, and time-series segmentation.

Amin Amirlatifi is an Assistant Professor of chemical and petroleum engineering at Swalm School of Chemical Engineering, Mississippi State University. His research interests include numerical modeling, artificial intelligence, and predictive maintenance.

Shahram Rahimi is professor and department head of the Computer Science and Engineering Department at the Mississippi State University. His research interests include Computational Intelligence, Soft Computing and Machine Learning, and Predictive Analytics with Game Theory.

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