**7. Conclusions**

The main characteristic of spatial data is that observations close in space tend to be correlated, and in spatial modeling this correlation is used to understand the behavior of the phenomenon under study in a region of interest.

Omitting the spatial dependence of the data can generate a bias of the information and, consequently, lead to an incorrect inference. Therefore, adequately describing the spatial pattern of an event can provide sufficient elements to elaborate possible hypotheses of its cause. As we have seen, the spatial variability of georeferenced data can be studied with the spatial models developed in geostatistics. The usefulness of these models has been demonstrated in several applications related to the identification of social structures, disease patterns, occupational patterns, as well as in the identification of populations (or subgroups) that are at greater or lesser risk of an event. As we have seen, in statistics, all correctly processed information helps in correct decision making. In this sense, this paper aims to introduce the reader to the use of spatial models in geostatistics.

If the response or variable of interest is the cases (counts) of sick people in a given region, or the new cases of a disease in a given period of time (incidence), then Poisson GLSMs can be useful to know the spread of the disease in the population of interest, predict new cases, and identify the variables that influence the occurrence of the disease. On the other hand, when the response variable is a binary or ratio variable, such as mortality rates or infection rates, then binomial GLSMs can be helpful. These models have been used to study the prevalence of dengue and to identify the variables associated with the event.
