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Risk Prediction Model for Dengue Transmission using Artificial Neural Networks

by Leslie Chandrakantha
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 17
Year of Publication: 2020
Authors: Leslie Chandrakantha
10.5120/ijca2020920685

Leslie Chandrakantha . Risk Prediction Model for Dengue Transmission using Artificial Neural Networks. International Journal of Computer Applications. 175, 17 ( Sep 2020), 37-41. DOI=10.5120/ijca2020920685

@article{ 10.5120/ijca2020920685,
author = { Leslie Chandrakantha },
title = { Risk Prediction Model for Dengue Transmission using Artificial Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2020 },
volume = { 175 },
number = { 17 },
month = { Sep },
year = { 2020 },
issn = { 0975-8887 },
pages = { 37-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number17/31547-2020920685/ },
doi = { 10.5120/ijca2020920685 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:25:19.468190+05:30
%A Leslie Chandrakantha
%T Risk Prediction Model for Dengue Transmission using Artificial Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 17
%P 37-41
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Dengue fever is a mosquito-borne viral disease that has grown dramatically around the world in recent years. It is more prevalent in tropical and subtropical countries. Annually, an estimated 390 million infections occur worldwide. Several studies have shown that climate factors influence this disease. Furthermore, it was shown that the influence of climate factors on dengue incidences was expected to be visible after some lag period. Identifying the climate factors that influence the spread of dengue fever would be helpful in combatting growth of the disease. This study builds an Artificial Neural Network (ANN) model for predicting the risk status of dengue incidences based on climate factors. The climate factors, average temperature, rainfall, and average relative humidity with a time lag are used as input parameters to the ANN. The monthly dengue incidences and the data on climate factors from the city of Colombo in Sri Lanka are used for this study. The accuracy of the ANN model prediction is found to be 90%.

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Index Terms

Computer Science
Information Sciences

Keywords

Dengue incidences Artificial Neural Networks Risk prediction Climate factors

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