Application of a Time Series Model and Climate Factors to Develop a Dengue Early Warning System: A Case Study in Tainan Taiwan

Abstract

Dengue fever (DF) is a climate-sensitive disease that has been emerging in southern regions of Taiwan over the past few decades causing a significant health burden to affected areas This study aims to propose a predictive model to implement an early warning system so as to enhance dengue surveillance and control in Tainan Taiwan The Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used herein to forecast dengue cases Temporal correlation between dengue cases and climate variables were examined by Pearson correlation analysis and Cross-correlation tests in order to identify key determinants to be included as predictors The dengue surveillance data between 2000 and 2009 as well as their respective climate variables were then used as inputs for the model We validated the model by forecasting the number of dengue cases expected to occur each week between January 1 2010 and December 31 2015 In addition we analyzed historical dengue trends and found that 25 cases occurring in one week was a trigger point that often led to a dengue outbreak This threshold point was combined with the season-based framework put forth by the World Health Organization to create a more accurate epidemic threshold for a Tainan-specific warning system A Seasonal ARIMA model with the general form: (1 0 5)(1 1 1)52 is identified as the most appropriate model based on lowest AIC and was proven significant in the prediction of observed dengue cases Based on the correlation coefficient Lag-11 maximum 1-hr rainfall (r=0 319 P

Date of Award	2017 Jul 27
Original language	English
Supervisor	Chyan-Deng Jan (Supervisor)