Exo-SIR: an epidemiological model to analyze the impact of exogenous spread of infection.

Nirmal Kumar Sivaraman, Manas Gaur, Shivansh Baijal, Sakthi Balan Muthiah, Amit Sheth

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Article in International journal of data science and analytics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Nirmal Kumar SivaramanDepartment of Computer Science and Engineering, The LNM Institute of Information Technology, Jaipur, India.

Manas GaurAI Institute, University of South Carolina, Columbia, USA.

Shivansh BaijalDepartment of Computer Science and Engineering, The LNM Institute of Information Technology, Jaipur, India.

Sakthi Balan MuthiahDepartment of Computer Science and Engineering, The LNM Institute of Information Technology, Jaipur, India.

Amit ShethAI Institute, University of South Carolina, Columbia, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epidemics like Covid-19 and Ebola have impacted people's lives significantly. The impact of mobility of people across the countries or states in the spread of epidemics has been significant. The spread of disease due to factors local to the population under consideration is termed the endogenous spread. The spread due to external factors like migration, mobility, etc., is called the exogenous spread. In this paper, we introduce the Exo-SIR model, an extension of the popular SIR model and a few variants of the model. The novelty in our model is that it captures both the exogenous and endogenous spread of the virus. First, we present an analytical study. Second, we simulate the Exo-SIR model with and without assuming contact network for the population. Third, we implement the Exo-SIR model on real datasets regarding Covid-19 and Ebola. We found that endogenous infection is influenced by exogenous infection. Furthermore, we found that the Exo-SIR model predicts the peak time better than the SIR model. Hence, the Exo-SIR model would be helpful for governments to plan policy interventions at the time of a pandemic.

Indexed as

Compartment modelCovid-19EbolaEndogenous infectionEpidemic modelingExogenous infectionExo-SIRSIR

Identifiers

PMID35694047

DOI10.1007/s41060-022-00334-z

PMCPMC9169602

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.