Informatics and Knowledge Management Graduate Program Universidade Nove de Julho, Brazil.
Spat Spatiotemporal Epidemiol. 2024 Aug;50:100674. doi: 10.1016/j.sste.2024.100674. Epub 2024 Jul 8.
This study examines the spread of COVID-19 in São Paulo, Brazil, using a combination of cellular automata and geographic information systems to model the epidemic's spatial dynamics. By integrating epidemiological models with georeferenced data and social indicators, we analyse how the virus propagates in a complex urban setting, characterized by significant social and economic disparities. The research highlights the role of various factors, including mobility patterns, neighbourhood configurations, and local inequalities, in the spatial spreading of COVID-19 throughout São Paulo. We simulate disease transmission across the city's 96 districts, offering insights into the impact of network topology and district-specific variables on the spread of infections. The study seeks to fine-tune the model to extract epidemiological parameters for further use in a statistical analysis of social variables. Our findings underline the critical importance of spatial analysis in public health strategies and emphasize the necessity for targeted interventions in vulnerable communities. Additionally, the study explores the potential of mathematical modelling in understanding and mitigating the effects of pandemics in urban environments.
本研究采用元胞自动机和地理信息系统相结合的方法，对巴西圣保罗市的 COVID-19 传播进行建模，以研究其空间动态。通过将流行病学模型与地理参考数据和社会指标相结合，我们分析了病毒在具有显著社会经济差异的复杂城市环境中的传播方式。该研究强调了各种因素的作用，包括流动模式、邻里结构和当地不平等现象，这些因素对 COVID-19 在圣保罗市的空间传播有重要影响。我们模拟了全市 96 个区的疾病传播，深入了解了网络拓扑结构和区特定变量对感染传播的影响。本研究旨在进一步对模型进行优化，以提取流行病学参数，用于对社会变量的统计分析。研究结果强调了空间分析在公共卫生策略中的重要性，以及在脆弱社区进行有针对性干预的必要性。此外，该研究还探讨了数学建模在理解和减轻城市环境中传染病影响的潜力。
