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

Date
2018-01-03
Authors
Freeze, John
Erraguntla, Madhav
Verma, Akshans
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
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.
Description
Keywords
Data, Text and Web Mining for Business Analytics, data integration, dengue fever, disease forecasting, machine learning
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 51st Hawaii International Conference on System Sciences
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.