Abstract

HURTADO, Leonardo; AGUILAR, Manuel  and  AVILA, Miguel. Analysis of spatial distribution of malaria in the department of Chocó for the year 2016. Ing. compet. [online]. 2018, vol.20, n.2, pp.57-68. ISSN 0123-3033.  https://doi.org/10.25100/iyc.v20i1.5810.

The following investigation aims at the estimate of a generalized linear spatial regression model of Poisson statistical distribution that allows to explain the spatial-geographic behavior of a disease transmitted by vectors such as malaria for the specific case of the municipalities of the department of Chocó in the year 2016.

In order to obtain the probability maps, spatial statistics techniques are used based on the analysis of Lattice type areas or data such as figure criteria, distance criteria and the most commonly used criteria of physical contiguity, in order to determine the existence of spatial autocorrelation To analyze the existence of malarial spatial autocorrelation among nearby municipalities, it was carried out through the statistical dynamics of sanitary variables (unsatisfied basic needs and aqueduct coverage), environmental variables (forest cover, precipitation, humidity, height and temperature) and demographic variables such as population of each municipality.

Nevertheless, it is necessary to perform a Gaussian anamorphosis process to guarantee the standardization of the data in standardized morbidity rates (SMR) and in this way identify the statistical structure of the data that for this year came from a Poisson distribution. The study allowed finding positive spatial autocorrelation confirming that in some municipalities of Chocó as Paimado, San Pablo canton, Istmina, Tado and La union panamerica malaria is more likely to occur because of its municipal closeness according to the physical relationship of certain study variables.

The result was that the variables that best explain malaria in the municipalities of Chocó are the coverage of forests and unsatisfied basic needs that give as a product the map of spatial auto correlation and the map of probability of occurrence with the highest ratio among municipalities of the region. South of the department.

Keywords : generalized linear spatial model regression; malaria; SMR; autocorrelation.

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