Analysis of Dengue Incidence in Piauí: A Study Using Spatial Statistics

Abstract

Introduction

Disease mapping has become a fundamental tool in the field of public health. According to, since the 1990s, there have been advances in data analysis techniques to generate maps identifying areas of risk [1]. The use of Geographic Information Systems (GIS) in spatializing regions with higher incidence has been helpful in preventing endemic diseases and guiding public health authorities on environmental and health issues in different locations.

Geoprocessing techniques contribute to creating health scenarios by aggregating environmental and structural data to the geographic component, considering the neighborhood relationship between areas [2]. Although disease mapping provides a spatial view of cases, it is not sufficient to statistically confirm the existence of clusters or spatial autocorrelations. For this, spatial statistics are widely used to relate case values to their geographic locations (Croner; SPERLING; BROOME, 1996). The Global Moran’s Index and Local Moran’s Index are spatial analysis techniques based on the concept of spatial autocorrelation and are applicable to delimited spatial objects and their associated numerical attributes, such as endemic cases in a particular locality [3].

Dengue is one of the medically important diseases that has concerned public health authorities due to the widespread distribution of the vector and the possibility of causing severe and fatal cases, such as hemorrhagic fever. In this sense, highlight the lack of research seeking to understand the health situation and the occurrence of disease-causing agents resulting from contact with environments that present vulnerabilities and harbor disease reservoirs, especially those transmitted by vectors, such as dengue [4].

The objective of this study is to perform an analysis of the spatial distribution of confirmed cases of dengue in the State of Piauí in 2022, using geoprocessing techniques and exploratory analysis of spatial data, including spatial statistics.