Marlon N. Gonzaga, Marcelo Martins de Oliveira, A. A. Faria
{"title":"Spatial Scale Effects in COVID-19 Spread Models","authors":"Marlon N. Gonzaga, Marcelo Martins de Oliveira, A. A. Faria","doi":"10.25088/complexsystems.32.1.71","DOIUrl":null,"url":null,"abstract":"The COVID-19 pandemic has highlighted epidemiological models as important forecasting methods and planning strategies, with studies conducted using a wide variety of analytical and computational techniques. Knowing that more pandemic episodes may occur, it is essential that epidemiological modeling present increasingly credible results. From this perspective, this paper aims to highlight the influence of spatial distribution on an epidemic dynamic, using agent-based modeling. To calibrate the behavioral profile of the population, data was taken on mobility, population pyramid, individual behavior and government policies of a real population during the pandemic. Two different initial spatial distribution scenarios are tested and the robustness of the infection is analyzed. Totalistic rules were designed to assess the influence of infected individuals in the vicinity of an agent, a factor that must not be ignored in modeling respiratory diseases with viruses capable of spreading by aerosols, such as SARS-CoV-2. It is shown that the scenario with nonuniform distribution of agents is much more robust, generating an epidemic process even when uniform distribution, for the same parameters, did not propagate the infection. Our results also suggest that herd immunity is attained in different levels of recovered individuals, showing higher values in denser regions. In conclusion, it is reinforced that the nonuniform feature of the spatial distribution of individuals plays a key role in the infection dynamics and should receive more attention when building epidemiological models.","PeriodicalId":50871,"journal":{"name":"Advances in Complex Systems","volume":"6 1","pages":"71-87"},"PeriodicalIF":0.7000,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Complex Systems","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.25088/complexsystems.32.1.71","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 2
Abstract
The COVID-19 pandemic has highlighted epidemiological models as important forecasting methods and planning strategies, with studies conducted using a wide variety of analytical and computational techniques. Knowing that more pandemic episodes may occur, it is essential that epidemiological modeling present increasingly credible results. From this perspective, this paper aims to highlight the influence of spatial distribution on an epidemic dynamic, using agent-based modeling. To calibrate the behavioral profile of the population, data was taken on mobility, population pyramid, individual behavior and government policies of a real population during the pandemic. Two different initial spatial distribution scenarios are tested and the robustness of the infection is analyzed. Totalistic rules were designed to assess the influence of infected individuals in the vicinity of an agent, a factor that must not be ignored in modeling respiratory diseases with viruses capable of spreading by aerosols, such as SARS-CoV-2. It is shown that the scenario with nonuniform distribution of agents is much more robust, generating an epidemic process even when uniform distribution, for the same parameters, did not propagate the infection. Our results also suggest that herd immunity is attained in different levels of recovered individuals, showing higher values in denser regions. In conclusion, it is reinforced that the nonuniform feature of the spatial distribution of individuals plays a key role in the infection dynamics and should receive more attention when building epidemiological models.
期刊介绍:
Advances in Complex Systems aims to provide a unique medium of communication for multidisciplinary approaches, either empirical or theoretical, to the study of complex systems. The latter are seen as systems comprised of multiple interacting components, or agents. Nonlinear feedback processes, stochastic influences, specific conditions for the supply of energy, matter, or information may lead to the emergence of new system qualities on the macroscopic scale that cannot be reduced to the dynamics of the agents. Quantitative approaches to the dynamics of complex systems have to consider a broad range of concepts, from analytical tools, statistical methods and computer simulations to distributed problem solving, learning and adaptation. This is an interdisciplinary enterprise.