A hierarchical intervention scheme based on epidemic severity in a community network

No Thumbnail Available
Files
Paper2023(2).pdf(1.23 MB)
Date
2023-07
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Abstract
As there are no targeted medicines or vaccines for newly emerging infectious diseases,isolation among communities (villages, cities, or countries) is one of themost effectiveintervention measures. As such, the number of intercommunity edges (NIE) becomesone of themost important factor in isolating a place since it is closely related to normallife. Unfortunately, how NIE affects epidemic spread is still poorly understood. In this paper, we quantitatively analyzed the impact of NIE on infectious disease transmissionby establishing a four-dimensional SIR edge-based compartmental model with two communities. The basic reproduction number R0( l ) is explicitly obtained subjectto NIE l . Furthermore, according to R0(0) with zero NIE, epidemics spreadcould be classified into two cases. When R0(0) > 1 for the case 2, epidemics occurwith at least one of the reproduction numbers within communities greater than one,and otherwise when R0(0) < 1 for case 1, both reproduction numbers within communitiesare less than one. Remarkably, in case 1, whether epidemics break out stronglydepends on intercommunity edges. Then, the outbreak threshold in regard to NIEis also explicitly obtained, below which epidemics vanish, and otherwise break out.The above two cases form a severity-based hierarchical intervention scheme for epidemics.It is then applied to the SARS outbreak in Singapore, verifying the validityof our scheme. In addition, the final size of the system is gained by demonstrating theexistence of positive equilibrium in a four-dimensional coupled system. Thoretical results are also validated through numerical simulation in networks with the Poisson and Power law distributions, respectively. Our results provide a new insight into controlling epidemics.
Description
This article is published by Journal of Mathematical Biology 2022 and is also available at https://doi.org/10.1007/s00285-023-01964-y
Keywords
Citation
Journal of Mathematical Biology (2023) 87:29
URI
https://doi.org/10.1007/s00285-023-01964-y
 https://ir.knust.edu.gh/handle/123456789/15972
Collections
Articles