Spatial analysis of Dengue through the reproduction numbers relating to socioeconomic features: Case studies on two Brazilian urban centers

IF 8.8 3区医学 Q1 Medicine Infectious Disease Modelling Pub Date : 2023-12-22 DOI:10.1016/j.idm.2023.12.004

Ana T.C. Silva , Rejane C. Dorn , Lívia R. Tomás , Leonardo B.L. Santos , Lacita M. Skalinski , Suani T.R. Pinho

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Abstract

The study of the propagation of infectious diseases in urban centers finds a close connection with their population's social characteristics and behavior. This work performs a spatial analysis of dengue cases in urban centers based on the basic reproduction numbers, R₀, and incidence by planning areas (PAs), as well as their correlations with the Human Development Index (HDI) and the number of trips. We analyzed dengue epidemics in 2002 at two Brazilian urban centers, Belo Horizonte (BH) and Rio de Janeiro (RJ), using PAs as spatial units. Our results reveal heterogeneous spatial scenarios for both cities, with very weak correlations between R₀ and both the number of trips and the HDI; in BH, the values of R₀ show a less spatial heterogeneous pattern than in RJ. For BH, there are moderate correlations between incidence and both the number of trips and the HDI; meanwhile, they weakly correlate for RJ. Finally, the absence of strong correlations between the considered measures indicates that the transmission process should be treated considering the city as a whole.

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通过与社会经济特征相关的繁殖数量对登革热进行空间分析：巴西两个城市中心的案例研究

传染病在城市中心的传播与人口的社会特征和行为密切相关。本研究根据基本繁殖数 R0 和规划区（PAs）的发病率，以及它们与人类发展指数（HDI）和旅行次数的相关性，对城市中心的登革热病例进行了空间分析。我们以规划区为空间单位，分析了 2002 年巴西贝洛奥里藏特（BH）和里约热内卢（RJ）两个城市中心的登革热疫情。我们的研究结果表明，这两个城市的空间情况不尽相同，R0 与出行次数和人类发展指数之间的相关性很弱；与里约热内卢相比，贝洛奥里藏特的 R0 值显示出较小的空间异质性模式。在波黑，发病率与出行次数和人类发展指数之间存在中度相关性；而在 RJ，两者之间的相关性较弱。最后，所考虑的指标之间没有很强的相关性，这表明应将城市作为一个整体来处理传播过程。

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来源期刊

Infectious Disease Modelling Mathematics-Applied Mathematics

CiteScore

17.00

自引率

3.40%

发文量

审稿时长

17 weeks

期刊介绍： Infectious Disease Modelling is an open access journal that undergoes peer-review. Its main objective is to facilitate research that combines mathematical modelling, retrieval and analysis of infection disease data, and public health decision support. The journal actively encourages original research that improves this interface, as well as review articles that highlight innovative methodologies relevant to data collection, informatics, and policy making in the field of public health.