Prediction of Dengue Fever Outbreak Based on Case Household Locations in Southern Taiwan

• 作者: Pui-Jen Tsai
• 来源期刊: 预防医学期刊(英文)
• 学科: 医学
• 关键词: Spatial Analysis Matthews Correlation Coefficient Dengue Outbreak FORECAST
• 年，卷（期）: 2020,（7）
• 页码范围: 175-193
• 页数: 19页
• 分类号: R51

摘要

Dengue fever is a serious vector-borne infectious viral disease found worldwide. Dengue fever forecasting is in demand in the front line of epidemic prevention and control work. The goal of this study was to evaluate the feasibility of using only notified case home locations to predict new cases and village locations. We took the Tainan City dengue fever outbreak in 2015 as the research subject and divided it into 5 periods according to epidemic temporal change. In each period, the predicted variable was the location of the reported cases in the previous week, the previous 2 weeks, and the previous 3 weeks. In addition, we used 21 preset distances with a radius of 0 to 2000 m at intervals of 100 m to predict the villages where new cases would appear. Accounting for 4 predictors of a confusion matrix at each preset distance, these predictors were used in calculations using the Matthews correlation coefficient (MCC) as the basis for model evaluation. In the lag phase, the optimal predictor was within 1700 m in the 3-week forecast. In the exponential phase, the optimal predictor was within 300 m in the 1-week forecast. In the stationary phase, the optimal predictor was within 100 m in the 3-week forecast and within 200 m in the 2-week forecast. In the early decline phase, the optimal predictor was 0 m in the 1-week forecast. In the late decline phase, the optimal predictor was within 200 m in the 2-week forecast. According to MCC calculations and comparisons among the 5 studied periods, the best MCC score was in the exponential phase, a stage of rapid increase of new cases. The results of this study suggest that the epidemic forecasting model based on the location of notified cases has a high reference value for epidemic control and prevention.