Abstract
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.
Highlights
Of all the currently existing virus-borne diseases, dengue fever (DF) has the greatest clinical impact, with approximately 96 million infections per year and nearly 4 million life-threatening cases [1] [2]
While studying the cases of DF in 37 administrative districts of Tainan City in 2015, we observed that the places with a high incidence density had a clustering distribution, which indicated that the incidence was highly correlated with population density
According to the outcomes of 5 studied phases that were based on a large wave of an indigenous dengue epidemic, the distance of the optimal predictor varies by phase
Summary
Of all the currently existing virus-borne diseases, dengue fever (DF) has the greatest clinical impact, with approximately 96 million infections per year and nearly 4 million life-threatening cases [1] [2]. The latitude, topography, ocean currents, and prevailing East Asian summer monsoon over Taiwan contribute to the island’s high temperature, humidity, and rainfall, as well as tropical cyclones during summer [4]. Both A. aegypti and A. albopictus are prevalent in Taiwan, they have different distributions [5] [6]. DF is a travel-related disease in Taiwan because travelers can carry the dengue virus from endemic areas to the island After this virus is transported to the island, it is passed to Aedes mosquitoes, which can cause an outbreak of indigenous DF [7] [8] [9] [10]. These outbreaks occurred in the south of Taiwan, where A. aegypti is prevalent and coexists with A. albopictus
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