Universidade Federal de Pernambuco, Departamento de Ciências Geográficas, Recife, PE, Brasil.
University of Lisbon, Department of Medicine, Lisbon, Portugal.
Rev Soc Bras Med Trop. 2020 Sep 25;53:e20200027. doi: 10.1590/0037-8682-0027-2020. eCollection 2020.
In this study, we aim to compare spatial statistic models to estimate the spatial distribution of Zika and Chikungunya infections in the city of Recife, Brazil. We also aim to establish the relationship between the diseases and the analyzed geographical conditions.
The models were defined by combining three categories: type of spatial unit, calculation of the dependent variable format, and estimation methods (Geographical Weighted Regression [GWR] and Ordinary Least Square [OLS]). We identified the most accurate model to estimate the spatial distribution of the diseases. After selecting the model that provided best results, the relationship between the geographical conditions and the incidence of the diseases was analyzed.
It was observed that the matrix of 100 meters (as the spatial unit) showed the highest efficiency to estimate the diseases. The best results were observed in the models that utilized the kernel density estimation (as the calculation of the dependent variable). In all models, the GWR method showed the best results. By considering the OLS coefficient values, it was observed that all geographical conditions are related to the incidence of Zika and Chikungunya, while the GWR coefficient values showed where this relationship was more noticeable.
The model that utilized the combination of the matrix of 100 meters, kernel density estimation (as the calculation of the dependent variable) and GWR method showed the highest efficiency in estimating the spatial distribution of the diseases. The coefficient values showed that all analyzed geographical conditions are related to the illnesses' incidence.
本研究旨在比较空间统计模型，以估计巴西累西腓市寨卡和基孔肯雅热感染的空间分布。我们还旨在确定疾病与分析的地理条件之间的关系。
通过组合三类模型来定义模型：空间单元类型、因变量格式的计算和估计方法（地理加权回归[GWR]和普通最小二乘法[OLS]）。我们确定了最准确的模型来估计疾病的空间分布。在选择提供最佳结果的模型后，分析了地理条件与疾病发病率之间的关系。
观察到以 100 米矩阵（作为空间单元）估计疾病的效率最高。在利用核密度估计（作为因变量的计算）的模型中观察到最佳结果。在所有模型中，GWR 方法都显示出了最好的结果。考虑 OLS 系数值，观察到所有地理条件都与寨卡和基孔肯雅热的发病率有关，而 GWR 系数值则显示出这种关系更为明显的地方。
利用 100 米矩阵、核密度估计（作为因变量的计算）和 GWR 方法组合的模型在估计疾病的空间分布方面显示出最高的效率。系数值表明，所有分析的地理条件都与疾病的发病率有关。
