**Abstract**

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.

**Keywords:** vector-borne diseases, Gaussian process, generalized spatial linear models, georeferenced data, spatial correlation
