登革熱是一種氣候敏感的疾病，近年來台灣南部地區也發生過相當規模的疫情，對於當地民眾健康與生命安全造成一定程度的威脅。本研究旨在建立統計模型，以台南地區為案例發展登革熱預警系統，用以加強台南登革熱疫情的監測。本研究使用季節性差分整合移動平均自迴歸模型（季節性ARIMA模型）來預測登革熱的病例數，並透過皮爾森相關係數和交叉相關函數來檢驗登革熱病例數和氣候因子之間的關係，進而將關係顯著的氣候因子加之於模型。本研究使用2000年至2009年的登革熱病例數進行統計建模，以2010年至2015年間每週預期發生的登革熱病例數來驗證模型。從歷史台南登革熱的資料中發現一周內發生25例登革熱病例往往是觸發登革熱疫情大量爆發的臨界值。本研究以世界衛生組織提出的季節性登革熱警戒值為基礎，並與此25例臨界值相結合，建議一套新的登革熱預警方法。
本研究以赤池信息量準則做為模式選取之依據，分析結果顯示季節性ARIMA模型（1,0,5）（1,1,1）52為模擬台南地區登革熱病例數的最適模型，並且在預測登革熱病例中被證明是達顯著水準的。氣候因子當中的最大時雨量和最低溫度皆在第11周延時與登革熱病例數有最大正相關。比較四個加入氣候因子的多變量模型（即1,4,9和13週提前預測），發現若與僅以過往登革熱病例資料的單變量模型相比，加入氣候因子能改善預測均方根誤差高達3.24％，10.39％，17.96％，21.81％。此外本研究也使用列聯表分析四個多變量模型檢定 2010-2015年預測期間判定登革熱疫情爆發周的能力，結果顯示季節性ARIMA模型可用於登革熱的預警，它具有高命中率和相對低的假警報率，從而提供給公共衛生部門訂定登革熱防疫計畫之參考，幫助醫療機構更有效地分配登革熱防疫資源。

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<0.05) and Lag-11 minimum temperature (r=0.416, P<0.05) are found to be the most positively correlated climate variables. Comparing the four multivariate models(i.e. 1, 4, 9 and 13 weeks ahead), we found that including the climate variables improves the prediction RMSE as high as 3.24%, 10.39%, 17.96%, 21.81% respectively, in contrast to univariate models. Furthermore, the ability of the four multivariate models to determine whether the epidemic threshold would be exceeded in any given week during the forecasting period of 2010-2015 was analyzed using a contingency table. Our findings indicate that SARIMA model is an ideal model for detecting outbreaks as it has high sensitivity and low risk of false alarms. Accurately forecasting future trends will provide valuable time to activate dengue surveillance and control in Tainan, Taiwan. We conclude that this timely dengue early warning system will enable public health services to allocate limited resources more effectively, and public health officials to adjust dengue emergency response plans to their maximum capabilities.

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