Epidemic Modeling and Flattening the Infection Curve in Social Networks

Document Type : Research Paper

Authors

1 Assistant Professor, Faculty of Mechanical Engineering, Semnan University, Semnan, Iran

2 Assistant Professor, Semnan University of Medical Sciences, Semnan, Iran

3 Assistant Professor, Gastrointestinal and Liver Diseases Research Center, Iran University of Medical Sciences, Tehran, Iran

4 Assistant Professor, Iran Telecom Research Center (ITRC), Tehran, Iran

5 Professor, Faculty of Computer Engineering, Sharif University of Technology, Tehran, iran

Abstract

The main goal of this paper is to model the epidemic and flattening the infection curve of the social networks. Flattening the infection curve implies slowing down the spread of the disease and reducing the infection rate via social-distancing, isolation (quarantine) and vaccination. The nan-pharmaceutical methods are a much simpler and efficient way to control the spread of epidemic and infection rate. By specifying a target group with high centrality for isolation and quarantine one can reach a much flatter infection curve (related to Corona for example) without adding extra costs to health services. The aim of this research is, first, modeling the epidemic and, then, giving strategies and structural algorithms for targeted vaccination or targeted non-pharmaceutical methods for reducing the peak of the viral disease and flattening the infection curve. These methods are more efficient for nan-pharmaceutical interventions as finding the target quarantine group flattens the infection curve much easier. For this purpose, a few number of particular nodes with high centrality are isolated and the infection curve is analyzed. Our research shows meaningful results for flattening the infection curve only by isolating a few number of targeted nodes in the social network. The proposed methods are independent of the type of the disease and are effective for any viral disease, e.g., Covid-19.
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Keywords

Main Subjects

References

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Journal of Modeling in Engineering
Volume 22, Issue 76
May 2024
Pages 155-165

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History

  • Receive Date: 26 March 2023
  • Revise Date: 22 July 2023
  • Accept Date: 01 October 2023

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