A Bayesian Prediction Spatial Model for Confirmed Dengue Cases in the State of Chiapas, Mexico
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,...
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ftdoajarticles:oai:doaj.org/article:13f340549b144773ad8c028a2ba705f3 2024-09-09T19:26:45+00:00 A Bayesian Prediction Spatial Model for Confirmed Dengue Cases in the State of Chiapas, Mexico Manuel Solís-Navarro Cruz Vargas-De-León María Gúzman-Martínez Josselin Corzo-Gómez 2022-01-01T00:00:00Z https://doi.org/10.1155/2022/1971786 https://doaj.org/article/13f340549b144773ad8c028a2ba705f3 EN eng Wiley http://dx.doi.org/10.1155/2022/1971786 https://doaj.org/toc/1687-9694 1687-9694 doi:10.1155/2022/1971786 https://doaj.org/article/13f340549b144773ad8c028a2ba705f3 Journal of Tropical Medicine, Vol 2022 (2022) Arctic medicine. Tropical medicine RC955-962 article 2022 ftdoajarticles https://doi.org/10.1155/2022/1971786 2024-08-05T17:48:32Z 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=0.001between 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. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Journal of Tropical Medicine 2022 1 13 |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
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language |
English |
topic |
Arctic medicine. Tropical medicine RC955-962 |
spellingShingle |
Arctic medicine. Tropical medicine RC955-962 Manuel Solís-Navarro Cruz Vargas-De-León María Gúzman-Martínez Josselin Corzo-Gómez A Bayesian Prediction Spatial Model for Confirmed Dengue Cases in the State of Chiapas, Mexico |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 |
description |
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=0.001between 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. |
format |
Article in Journal/Newspaper |
author |
Manuel Solís-Navarro Cruz Vargas-De-León María Gúzman-Martínez Josselin Corzo-Gómez |
author_facet |
Manuel Solís-Navarro Cruz Vargas-De-León María Gúzman-Martínez Josselin Corzo-Gómez |
author_sort |
Manuel Solís-Navarro |
title |
A Bayesian Prediction Spatial Model for Confirmed Dengue Cases in the State of Chiapas, Mexico |
title_short |
A Bayesian Prediction Spatial Model for Confirmed Dengue Cases in the State of Chiapas, Mexico |
title_full |
A Bayesian Prediction Spatial Model for Confirmed Dengue Cases in the State of Chiapas, Mexico |
title_fullStr |
A Bayesian Prediction Spatial Model for Confirmed Dengue Cases in the State of Chiapas, Mexico |
title_full_unstemmed |
A Bayesian Prediction Spatial Model for Confirmed Dengue Cases in the State of Chiapas, Mexico |
title_sort |
bayesian prediction spatial model for confirmed dengue cases in the state of chiapas, mexico |
publisher |
Wiley |
publishDate |
2022 |
url |
https://doi.org/10.1155/2022/1971786 https://doaj.org/article/13f340549b144773ad8c028a2ba705f3 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Journal of Tropical Medicine, Vol 2022 (2022) |
op_relation |
http://dx.doi.org/10.1155/2022/1971786 https://doaj.org/toc/1687-9694 1687-9694 doi:10.1155/2022/1971786 https://doaj.org/article/13f340549b144773ad8c028a2ba705f3 |
op_doi |
https://doi.org/10.1155/2022/1971786 |
container_title |
Journal of Tropical Medicine |
container_volume |
2022 |
container_start_page |
1 |
op_container_end_page |
13 |
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1809896312516116480 |