Vector-borne diseases are those caused by the bite of an infected arthropod, such as the Aedes aegypti mosquito, which can infect humans with dengue or Zika. Spatial statistics is an interesting tool that is currently implemented to predict and analyze the behavior of biological systems or natural phenomena. In this chapter, fundamental characteristics of spatial statistics are presented and its application in epidemiology is exemplified by presenting a study on the prediction of the dispersion of dengue disease in Chiapas, Mexico. A total of 573 confirmed dengue cases (CDCs) were studied over the period of January–August 2019. As part of the spatial modeling, the existence of spatial correlation in CDCs was verified with the Moran index (MI) and subsequently the spatial correlation structure was identified with the mean squarer normalized error (MSNE) criterion. A Generalized Linear Spatial Model (GLSM) was used to model the CDCs. CDCs were found to be spatially correlated, and this can be explained by a Matérn covariance function. Finally, the explanatory variables were maximum environmental temperature, altitude, average monthly rainfall, and patient age. The prediction model shows the importance of considering these variables for the prevention of future CDCs in vulnerable areas of Chiapas.

Dengue is one of the major health problems in the state of Chiapas. Consequently, spatial information on the distribution of the disease can optimize directed control strategies. Therefore, this study aimed to develop and validate a simple Bayesian prediction spatial model for the state of Chiapas, Mexico. This is an ecological study that uses data from a range of sources. Dengue cases occurred from January to August 2019. The data analysis used the spatial correlation of dengue cases (DCs), which was calculated with the Moran index statistic, and a generalized linear spatial model (GLSM) within a Bayesian framework, which was considered to model the spatial distribution of DCs in the state of Chiapas. We selected the climatological, geographic, and sociodemographic variables related to the study area. A prediction of the model on Chiapas maps was carried out based on the places where the cases were registered. We find a spatial correlation of 0.115 p  value = 0.001 between neighboring municipalities using the Moran index. The variables that have an effect on the number of confirmed cases of dengue are the maximum temperature ( Coef = 0.110 ; 95 %   CrI : 0.076 − 0.215 ), rainfall ( Coef = 0.013 ; 95 %   CrI : 0.008 − 0.028 ), and altitude ( Coef = 0.00045 ; 95 %   CrI : 0.00002 − 0.00174 ) of each municipality. The predicting power is notably better in regions that have a greater number of municipalities where DCs are registered. The model shows the importance of considering these variables to prevent future DCs in vulnerable areas.