Exo-SIR: An Epidemiological Model to Analyze the Impact of Exogenous Infection of COVID-19 in India
Nirmal Kumar Sivaraman, Manas Gaur, Shivansh Baijal, Ch V Radha Sai, Rupesh, Sakthi Balan Muthiah, Amit Sheth

TL;DR
This paper introduces the Exo-SIR model, a novel epidemiological framework that captures both exogenous and endogenous COVID-19 spread, revealing that exogenous infections significantly influence outbreak dynamics and peak timing.
Contribution
The paper presents a new model integrating exogenous and endogenous infection sources, analyzing their interplay and impact on COVID-19 spread in India.
Findings
Endogenous infection is affected by even minimal exogenous infection rates.
Presence of exogenous infection leads to earlier and higher infection peaks.
Model validated on real COVID-19 data showing practical relevance.
Abstract
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale free network. Third, we implemented the Exo-SIR model on a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCOVID-19 epidemiological studies · Complex Network Analysis Techniques · Mental Health Research Topics
