A Bayesian Prediction Spatial Model for Confirmed Dengue Cases in the State of Chiapas, Mexico. (25th May 2022)

• Record Type: Journal Article
• Authors: Solís-Navarro, Manuel; Vargas-De-León, Cruz; Gúzman-Martínez, María; Corzo-Gómez, Josselin
• Other Names: Wang Maoshui Academic Editor.
• Is Part Of: Journal of tropical medicine. Volume 2022(2022)
• Publication Date: 2022-05-25
• Subjects: Tropical medicine -- Periodicals; 616.9883
• DOI: 10.1155/2022/1971786
• Languages: English
• ISSNs: 1687-9686

Abstract

Abstract : 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.