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Research Article | Open Access

Computational modeling, analysis and simulation for lockdown dynamics of COVID-19 and domestic violence

Comfort Ohajunwa1Carmen Caiseda2( )Padmanabhan Seshaiyer3
Governor's School @ Innovation Park, USA
Inter American University of Puerto Rico, Bayamon, USA
George Mason University, USA
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Abstract

As potential strategies to control the spread of COVID-19, governments all across the globe have implemented interventions such as lockdowns and confinement. While these strategies have helped to control the spread, there have also been evidence of widespread increase in Domestic Violence (DV) which is often under-reported. In this work, we have developed two new models that will help study the relationship between lockdowns, the spread of COVID-19 and DV in the hope of mitigating the social problems that follow such drastic measures. Two different models, in increasing level of complexity have been employed to simulate the effect of the lockdown strategy in the spread of COVID-19 and DV. One of the models simulates the spread of DV under three different lockdown scenarios: one long period, two and three shorter intervals that comprise the same interval of time since onset of the Pandemic. Another model studies the interaction between COVID-19 and DV during confinement in relation to the length of the lockdowns. Our findings indicate multiple lockdowns are safer for DV but not necessarily for controlling spread of COVID-19. We also present a derivation of the basic reproduction number for the model involving the interaction between COVID-19 and DV.

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Electronic Research Archive
Pages 2446-2464

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Cite this article:
Ohajunwa C, Caiseda C, Seshaiyer P. Computational modeling, analysis and simulation for lockdown dynamics of COVID-19 and domestic violence. Electronic Research Archive, 2022, 30(7): 2446-2464. https://doi.org/10.3934/era.2022125

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Received: 15 October 2021
Revised: 03 January 2022
Accepted: 05 January 2022
Published: 15 July 2022
©2022 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)