Dengue has long been a public health problem in tropical and subtropical countries. In 2015, a dengue outbreak occurred in Taiwan, where 43,784 cases were reported. This study aims to assess the impact of dengue on Southern Taiwan’s economic growth according to the economic growth model-based regression approach recommended by the World Health Organization (WHO). Herein, annual data from Southern Taiwan on the number of dengue cases, income growth, and demographics from 2010–2015 were analyzed. The percentage of reduction of the average income per capita in 2015 due to the dengue outbreak was estimated. Dengue was determined to have a negative linear economic impact on Southern Taiwan’s economic growth. In particular, a reduction of 0.26% in the average income per capita was estimated in Southern Taiwan due to the 2015 outbreak. If the model is applied alongside other dengue outbreak forecast models, then the forecast for economic reduction due to a future dengue outbreak may also be estimated. Prevention and recovery policies may subsequently be decided upon based on not only the number of dengue cases but also the degree of economic burden resulting from an outbreak.

BackgroundDengue has become an increasing public health threat around the world, and climate conditions have been identified as important factors affecting the transmission of dengue, so this study was aimed to establish a prediction model of dengue epidemic by meteorological methods.MethodsThe dengue case information and meteorological data were collected from Guangdong Provincial Center for Disease Prevention and Control and Guangdong Meteorological Bureau, respectively. We used spatio-temporal analysis to characterize dengue epidemics. Spearman correlation analysis was used to analyze the correlation between lagged meteorological factors and dengue fever cases and determine the maximum lagged correlation coefficient of different meteorological factors. Then, Generalized Additive Models were used to analyze the non-linear influence of lagged meteorological factors on local dengue cases and to predict the number of local dengue cases under different weather conditions.ResultsWe described the temporal and spatial distribution characteristics of dengue fever cases and found that sporadic single or a small number of imported cases had a very slight influence on the dengue epidemic around. We further created a forecast model based on the comprehensive consideration of influence of lagged 42-day meteorological factors on local dengue cases, and the results showed that the forecast model has a forecast effect of 98.8%, which was verified by the actual incidence of dengue from 2005 to 2016 in Guangzhou.ConclusionA forecast model for dengue epidemic was established with good forecast effects and may have a potential application in global dengue endemic areas after modification according to local meteorological conditions. High attention should be paid on sites with concentrated patients for the control of a dengue epidemic.

This study was aimed to determine dengue season, and further establish a prediction model by meteorological methods. The dengue and meteorological data were collected from Guangdong Meteorological Bureau and Guangdong Provincial Center for Disease Prevention and Control, respectively. We created a sliding accumulated temperature method to accurately calculate the beginning and ending day of dengue season. Probabilistic Forecast model was derived under comprehensive consideration of various weather processes including typhoon, rainstorm, and so on. We found: 1) The dengue fever season enters when effective accumulated temperature of a continuing 45 days (T45)≥ 0 degrees Celsius, and it finishes when effective accumulated temperature of a continuing 6 days (T6) <0 degrees Celsius. 2) A Probabilistic Forecast Model for dengue epidemic was established with good forecast effects, which were verified by the actual incidence of dengue in Guangzhou. The Probabilistic Forecast Model provides markedly improved forecasting techniques for dengue prediction.