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A multi-city epidemiological model based on cellular automata and complex networks for the COVID-19

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Abstract

Multi-patch and multi-city models have been proposed to investigate the dynamics of disease propagation in discrete regions characterized by varying connectivity and movement rates. This paper aims to comprehensively explore the impact of network topology and travel rates on disease spread within a population represented by random networks of interconnected cities. This approach enables a more comprehensive analysis of disease outbreaks across spatial domains compared to conventional multi-patch models, which typically consider a limited number of homogeneous links among subpopulations within distinct spatial units (i.e., patches). In this study, cities are represented using a probabilistic cellular automaton model that incorporates local interactions among individuals, while the network’s edges represent the travel rates between pairs of cities (i.e., nodes). Two types of complex networks are considered, namely small-world and Barabási–Albert networks. By employing a flexible numerical model, this study surpasses previous models in its capacity to accommodate a larger number of patches or cities. The primary findings of this research reveal that reducing the travel rates within the network can potentially “flatten the curve” of infected cases; however, it does not impact the population’s basic reproduction number. To effectively reduce the reproduction number, localized interventions targeting disease transmissibility are required. Additionally, concentrating control efforts on “central” cities within the network may prove crucial in impeding the rapid propagation of the disease. In summary, this study employs a rigorous framework to investigate the influence of network topology and travel rates on disease spreading within a population represented by random networks of interconnected cities. The findings contribute to a comprehensive understanding of disease dynamics in complex spatial settings and inform targeted intervention strategies for controlling and mitigating infectious disease outbreaks.

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Data Availability

The datasets generated during and/or analyzed during the current study are available in the link https://bit.ly/3MloXCc. It is not in a repository due to the large size of the files.

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Funding

PHTS is supported by grants #440025/2020-6, #307194/2019-1 and #402874/2016-1 of Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and grant #2017/12671-8, São Paulo Research Foundation (FAPESP).

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  1. Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, Rua Vergueiro, 235/249, São Paulo, SP, 01525-000, Brazil

    Cássio de Lima Quiroga & Pedro Henrique Triguis Schimit

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  1. Cássio de Lima Quiroga
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  2. Pedro Henrique Triguis Schimit
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Correspondence to Pedro Henrique Triguis Schimit.

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Quiroga, C.d.L., Schimit, P.H.T. A multi-city epidemiological model based on cellular automata and complex networks for the COVID-19. Comp. Appl. Math. 42, 288 (2023). https://doi.org/10.1007/s40314-023-02401-y

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