Prediction of COVID-19 Infected Population for Indian States through a State Interaction Network-based SEIR Epidemic Model

https://doi.org/10.1016/j.ifacol.2022.04.113Get rights and content

Abstract

Objective of this present study is to predict the COVID-19 trajectories in terms of infected population of Indian states. In this work, a state interaction network of sixteen Indian states with highest number of infected caseload is considered, based on networked Susceptible-Exposed-Infected-Recovered (SEIR) epidemic model. An intervention term has been introduced in order to capture the effect of lockdown with different stringencies at different periods of time. The model has been fitted using least absolute shrinkage and selection operator (LASSO). Machine learning methods have been used to train the parameters of the model, cross-validate the data, and predict the parameters. The predictions of infected population for each of the sixteen states have been shown using data considered from January 1, 2021 till writing this manuscript on June 25, 2021. Finally, the effectiveness of the model is manifested by the calculated mean error and confidence interval.

Keywords

Networked SEIR model
COVID-19
Epidemic model
Prediction
Intervention stingency

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This work is supported by Science and Engineering Research Board (SERB), Department of Science and Technology, Govt. of India with project reference no. MSC/2020/000039.

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