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Strategies for Reducing the Spread of COVID-19 Based on an Ant-Inspired Framework

Ghassan Ahmed Ali*

Information Systems Department, College of Computer Sciences, Najran University, 1988, Saudi Arabia

* Corresponding Author: Ghassan Ahmed Ali. Email: email

Intelligent Automation & Soft Computing 2021, 30(1), 351-360. https://doi.org/10.32604/iasc.2021.017453

Abstract

Many living organisms respond to pandemics using strategies such as isolation. This is true, for example, of social insects, for whom the spread of disease can pose a high risk to colony survival. In light of such behaviors, the present study investigated a different way of developing strategies to mitigate the effects of the coronavirus pandemic. Specifically, we considered the strategies ants use to handle epidemics and limit disease spread within colonies. To enhance our understanding of these strategies, we explored ants’ social systems and how they specifically respond to infectious diseases. The early warning threshold system reflects the importance of building mechanisms and making early decisions to enable appropriate actions to be taken to control the spread of disease and develop alternative plans. Determining thresholds is a complex process with several factors related to decision-making. For example, reliable warnings about pathogens among ants require the defense system to allocate more members to more valuable resources. The effectiveness of ants’ disease-control strategies is rooted in behaviors such as social coordination between members, organizational immunity, disease-outbreak reduction, behavioral plasticity, and risk-threshold determination. Understanding such behaviors among ants could inspire the development of new strategies for limiting the spread of COVID-19 as well as future pandemics.

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Cite This Article

G. Ahmed Ali and . , "Strategies for reducing the spread of covid-19 based on an ant-inspired framework," Intelligent Automation & Soft Computing, vol. 30, no.1, pp. 351–360, 2021.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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