Abstract
INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Highlights
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
The model that utilized the combination of the matrix of 100 meters, kernel density estimation and GWR method showed the highest efficiency in estimating the spatial distribution of the diseases
We aim to compare spatial statistic models to estimate the spatial distribution of Zika and chikungunya infections in the city of Recife (Pernambuco, Brazil)
Summary
We aim to compare spatial statistic models to estimate the spatial distribution of Zika and Chikungunya infections in the city of Recife, Brazil. After selecting the model that provided best results, the relationship between the geographical conditions and the incidence of the diseases was analyzed. The best results were observed in the models that utilized the kernel density estimation (as the calculation of the dependent variable). 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. Conclusions: 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. Public educational campaigns encourage communities at risk to engage in preventive behaviors, as people who are aware of the risk in their neighborhood are more likely to eliminate potential breeding sites in their homes, apply insect repellent, dress appropriately to avoid bites, and avoid the outdoors during mosquito feeding hours[4]
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