Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/50001

Data Integration and Predictive Analysis System for Disease Prophylaxis: Incorporating Dengue Fever Forecasts

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Title:Data Integration and Predictive Analysis System for Disease Prophylaxis: Incorporating Dengue Fever Forecasts
Authors:Freeze, John
Erraguntla, Madhav
Verma, Akshans
Keywords:Data, Text and Web Mining for Business Analytics
data integration, dengue fever, disease forecasting, machine learning
Date Issued:03 Jan 2018
Abstract:The goal of the Data Integration and Predictive Analysis System (IPAS) is to enable prediction, analysis, and response management for incidents of infectious diseases. IPAS collects and integrates comprehensive datasets of previous disease incidents and potential influencing factors to facilitate multivariate, predictive analysis of disease patterns, intensity, and timing. We have used the IPAS technology to generate successful forecasts for Influenza Like Illness (ILI). In this study, IPAS was expanded to forecast Dengue fever in the cities of San Juan, Puerto Rico and Iquitos, Peru. Data provided by the National Oceanic and Atmospheric Administration (NOAA) was processed and used to generate prediction models. Predictions were developed with modern machine learning algorithms, identifying the one-week and four-week forecast of Dengue incidences in each city. Prediction model results are presented along with the features of the IPAS system.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/50001
ISBN:978-0-9981331-1-9
DOI:10.24251/HICSS.2018.114
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Data, Text and Web Mining for Business Analytics


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