Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico.

Abdiel Laureano-Rosario,Dragan Savic,Julian Garcia-Rejon,Salvador Gomez-Carro,Pablo Mendez-Lazaro,Frank Muller-Karger,Jose Farfan-Ale,Andrew Duncan

doi:10.3390/tropicalmed3010005

Abdiel Laureano-Rosario, Dragan Savic + Show 6 more

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https://doi.org/10.3390/tropicalmed3010005

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Abstract

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.

Highlights

Dengue fever is considered a global burden, with more than 500,000 cases reported annually [1,2,3,4]
The power of the model to predict these outcomes was based on an F-measure (FM; Equation (1)), which provided the importance of false positives (FP) over false negatives (FN) using the weighting factor (a) discussed above; these values ranged from 0 to 1
For a single output artificial neural networks (ANNs), the result is a vector that specifies the combined pathway strength of each input on the output. These weights were combined through the combined neural pathway strength analysis (CNPSA) by looking at the spread of the pathway strengths for each input, where we identified if the majority of these treated the given input in the same sense, excitatory or inhibitory [54,55]

Summary

Introduction

Dengue fever is considered a global burden, with more than 500,000 cases reported annually [1,2,3,4]. This vector-borne disease is mostly transmitted by the Aedes aegypti mosquitoes, but can be transmitted by Aedes albopictus [1,3]. Aedes aegypti are found in tropical/sub-tropical areas, where they have adapted to urbanized environments. This complicates management and mitigation of the organism and the disease [2,5,6,7]. Each location reports around 10,000 cases annually [13,14,15,16]

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