Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico
Modelling dengue fever in endemic areas is important to mitigate and improve vector-borne disease control to reduce outbreaks. This study applied artificial neural networks (ANNs) to predict dengue fever outbreak occurrences in San Juan, Puerto Rico (USA), and in several coastal municipalities of th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-01-01
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| Series: | Tropical Medicine and Infectious Disease |
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| Online Access: | http://www.mdpi.com/2414-6366/3/1/5 |
| _version_ | 1828138716771647488 |
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| author | Abdiel E. Laureano-Rosario Andrew P. Duncan Pablo A. Mendez-Lazaro Julian E. Garcia-Rejon Salvador Gomez-Carro Jose Farfan-Ale Dragan A. Savic Frank E. Muller-Karger |
| author_facet | Abdiel E. Laureano-Rosario Andrew P. Duncan Pablo A. Mendez-Lazaro Julian E. Garcia-Rejon Salvador Gomez-Carro Jose Farfan-Ale Dragan A. Savic Frank E. Muller-Karger |
| author_sort | Abdiel E. Laureano-Rosario |
| collection | DOAJ |
| description | Modelling dengue fever in endemic areas is important to mitigate and improve vector-borne disease control to reduce outbreaks. This study applied artificial neural networks (ANNs) to predict dengue fever outbreak occurrences in San Juan, Puerto Rico (USA), and in several coastal municipalities of the state of Yucatan, Mexico, based on specific thresholds. The models were trained with 19 years of dengue fever data for Puerto Rico and six years for Mexico. Environmental and demographic data included in the predictive models were sea surface temperature (SST), precipitation, air temperature (i.e., minimum, maximum, and average), humidity, previous dengue cases, and population size. Two models were applied for each study area. One predicted dengue incidence rates based on population at risk (i.e., numbers of people younger than 24 years), and the other on the size of the vulnerable population (i.e., number of people younger than five years and older than 65 years). The predictive power was above 70% for all four model runs. The ANNs were able to successfully model dengue fever outbreak occurrences in both study areas. The variables with the most influence on predicting dengue fever outbreak occurrences for San Juan, Puerto Rico, included population size, previous dengue cases, maximum air temperature, and date. In Yucatan, Mexico, the most important variables were population size, previous dengue cases, minimum air temperature, and date. These models have predictive skills and should help dengue fever mitigation and management to aid specific population segments in the Caribbean region and around the Gulf of Mexico. |
| first_indexed | 2024-04-11T18:40:45Z |
| format | Article |
| id | doaj.art-8136378d618447ada66494c84336a48c |
| institution | Directory Open Access Journal |
| issn | 2414-6366 |
| language | English |
| last_indexed | 2024-04-11T18:40:45Z |
| publishDate | 2018-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Tropical Medicine and Infectious Disease |
| spelling | doaj.art-8136378d618447ada66494c84336a48c2022-12-22T04:08:59ZengMDPI AGTropical Medicine and Infectious Disease2414-63662018-01-0131510.3390/tropicalmed3010005tropicalmed3010005Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto RicoAbdiel E. Laureano-Rosario0Andrew P. Duncan1Pablo A. Mendez-Lazaro2Julian E. Garcia-Rejon3Salvador Gomez-Carro4Jose Farfan-Ale5Dragan A. Savic6Frank E. Muller-Karger7Institute for Marine Remote Sensing, University of South Florida, College of Marine Science, 140 7th Avenue South, Saint Petersburg, FL 33701, USACentre for Water Systems, University of Exeter, Harrison Building, North Park Road, Exeter EX4 4QF, UKEnvironmental Health Department, Graduate School of Public Health, University of Puerto Rico, Medical Sciences Campus, P.O. Box 365067, San Juan, PR 00936, USACentro de Investigaciones Regionales, Lab de Arbovirologia, Unidad Inalámbrica, Universidad Autonoma de Yucatan, Calle 43 No. 613 x Calle 90, Colonia Inalambrica, Merida C.P. 97069, Yucatan, MexicoServicios de Salud de Yucatan, Hospital General Agustin O’Horan Unidad de Vigilancia Epidemiologica, Avenida Itzaes s/n Av. Jacinto Canek, Centro, Merida C.P. 97000, Yucatan, MexicoCentro de Investigaciones Regionales, Lab de Arbovirologia, Unidad Inalámbrica, Universidad Autonoma de Yucatan, Calle 43 No. 613 x Calle 90, Colonia Inalambrica, Merida C.P. 97069, Yucatan, MexicoCentre for Water Systems, University of Exeter, Harrison Building, North Park Road, Exeter EX4 4QF, UKInstitute for Marine Remote Sensing, University of South Florida, College of Marine Science, 140 7th Avenue South, Saint Petersburg, FL 33701, USAModelling dengue fever in endemic areas is important to mitigate and improve vector-borne disease control to reduce outbreaks. This study applied artificial neural networks (ANNs) to predict dengue fever outbreak occurrences in San Juan, Puerto Rico (USA), and in several coastal municipalities of the state of Yucatan, Mexico, based on specific thresholds. The models were trained with 19 years of dengue fever data for Puerto Rico and six years for Mexico. Environmental and demographic data included in the predictive models were sea surface temperature (SST), precipitation, air temperature (i.e., minimum, maximum, and average), humidity, previous dengue cases, and population size. Two models were applied for each study area. One predicted dengue incidence rates based on population at risk (i.e., numbers of people younger than 24 years), and the other on the size of the vulnerable population (i.e., number of people younger than five years and older than 65 years). The predictive power was above 70% for all four model runs. The ANNs were able to successfully model dengue fever outbreak occurrences in both study areas. The variables with the most influence on predicting dengue fever outbreak occurrences for San Juan, Puerto Rico, included population size, previous dengue cases, maximum air temperature, and date. In Yucatan, Mexico, the most important variables were population size, previous dengue cases, minimum air temperature, and date. These models have predictive skills and should help dengue fever mitigation and management to aid specific population segments in the Caribbean region and around the Gulf of Mexico.http://www.mdpi.com/2414-6366/3/1/5nonlinear modelsAedes aegyptiAedes albopictusremote sensingearly warning systems |
| spellingShingle | Abdiel E. Laureano-Rosario Andrew P. Duncan Pablo A. Mendez-Lazaro Julian E. Garcia-Rejon Salvador Gomez-Carro Jose Farfan-Ale Dragan A. Savic Frank E. Muller-Karger Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico Tropical Medicine and Infectious Disease nonlinear models Aedes aegypti Aedes albopictus remote sensing early warning systems |
| title | Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico |
| title_full | Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico |
| title_fullStr | Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico |
| title_full_unstemmed | Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico |
| title_short | Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico |
| title_sort | application of artificial neural networks for dengue fever outbreak predictions in the northwest coast of yucatan mexico and san juan puerto rico |
| topic | nonlinear models Aedes aegypti Aedes albopictus remote sensing early warning systems |
| url | http://www.mdpi.com/2414-6366/3/1/5 |
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