Dengue, a viral infection transmitted between people by mosquitoes, is one of the most rapidly spreading diseases in the world. Here, we report the analyses covering 11 y (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) from the city of Guangzhou in southern China. Using the first 8 y of data to develop an ecologically based model for the dengue system, we reliably predict the following 3 y of dengue dynamics-years with exceptionally extensive dengue outbreaks. We demonstrate that climate conditions, through the effects of rainfall and temperature on mosquito abundance and dengue transmission rate, play key roles in explaining the temporal dynamics of dengue incidence in the human population. Our study thus contributes to a better understanding of dengue dynamics and provides a predictive tool for preventive dengue reduction strategies.D engue is one of the most rapidly spreading diseases in the world (1), including within the Guangdong province of southern China (2). During the last 50 y, the incidence of dengue has increased 30-fold with increasing geographic expansion to new countries (1). In 2010, an estimated 390 million dengue infections occurred, of which around 96 million showed symptoms (3). Dengue outbreaks in China were previously thought to be imported and initiated by people traveling to China from dengue-endemic areas elsewhere (4); however, recent studies suggest that dengue may now be endemic to China as well (2). The epidemiological triangle of both dengue fever and dengue hemorrhagic fever, which is the more serious form of dengue, includes hosts (humans), pathogens (one or more of five dengue virus serotypes) (5), and mosquito vectors (Aedes albopictus and Aedes aegypti) with their ecological interactions (6). The dengue outbreaks are qualitatively known to be strongly influenced by temperature (7), humidity, rainfall, and socioeconomic factors like urbanization (8). However, a full understanding of the quantitative nature of such effects is largely lacking. With this paper, we provide such a quantitative understanding of dengue dynamics.In 2014, an extensive dengue outbreak hit China, with 47,127 dengue cases diagnosed, a new record since 1986 (9). Since the 1990s, dengue epidemics have gradually spread from Guangdong, Hainan, and Guangxi provinces (9). We present here a time series analysis of dengue dynamics, using dengue surveillance data for the years 2005−2015 from Guangzhou, the largest city in Guangdong and the city with the most dengue cases in China. We split the main analysis by using the first 8 y to develop a model, and the three remaining years to test that model, as these latter years encompass exceptionally extensive dengue outbreaks.Monthly human dengue incidence data (number of diagnosed cases) were obtained from the China National Notifiable Disease Surveillance System (10) (Fig. 1). Monthly surveillance data of A. albopictus density, the only dengue vector species in Guangzhou, were obtained from local Centers for Disease Control and Prevention (11) (Metho...

This study emphasises how cold weather is responsible for most part of the temperature-related CVD death burden. Our results may have important implications for the development of policies to reduce CVD mortality from extreme temperatures.

IntroductionDengue is endemic in more than 100 countries, mainly in tropical and subtropical regions, and the incidence has increased 30-fold in the past 50 years. The situation of dengue in China has become more and more severe, with an unprecedented dengue outbreak hitting south China in 2014. Building a dengue early warning system is therefore urgent and necessary for timely and effective response.Methodology and Principal FindingsIn the study we developed a time series Poisson multivariate regression model using imported dengue cases, local minimum temperature and accumulative precipitation to predict the dengue occurrence in four districts of Guangzhou, China. The time series data were decomposed into seasonal, trend and remainder components using a seasonal-trend decomposition procedure based on loess (STL). The time lag of climatic factors included in the model was chosen based on Spearman correlation analysis. Autocorrelation, seasonality and long-term trend were controlled in the model. A best model was selected and validated using Generalized Cross Validation (GCV) score and residual test. The data from March 2006 to December 2012 were used to develop the model while the data from January 2013 to September 2014 were employed to validate the model. Time series Poisson model showed that imported cases in the previous month, minimum temperature in the previous month and accumulative precipitation with three month lags could project the dengue outbreaks occurred in 2013 and 2014 after controlling the autocorrelation, seasonality and long-term trend.ConclusionsTogether with the sole transmission vector Aedes albopictus, imported cases, monthly minimum temperature and monthly accumulative precipitation may be used to develop a low-cost effective early warning system.

IntroductionEach year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF), a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explore the roles of imported cases, mosquito density and climate variability in dengue transmission in China. The study was to identify the relationship between dengue occurrence and possible risk factors and to develop a predicting model for dengue’s control and prevention purpose.Methodology and Principal FindingsThree traditional suburbs and one district with an international airport in Guangzhou city were selected as the study areas. Autocorrelation and cross-correlation analysis were used to perform univariate analysis to identify possible risk factors, with relevant lagged effects, associated with local dengue cases. Principal component analysis (PCA) was applied to extract principal components and PCA score was used to represent the original variables to reduce multi-collinearity. Combining the univariate analysis and prior knowledge, time-series Poisson regression analysis was conducted to quantify the relationship between weather variables, Breteau Index, imported DF cases and the local dengue transmission in Guangzhou, China. The goodness-of-fit of the constructed model was determined by pseudo-R2, Akaike information criterion (AIC) and residual test. There were a total of 707 notified local DF cases from March 2006 to December 2012, with a seasonal distribution from August to November. There were a total of 65 notified imported DF cases from 20 countries, with forty-six cases (70.8%) imported from Southeast Asia. The model showed that local DF cases were positively associated with mosquito density, imported cases, temperature, precipitation, vapour pressure and minimum relative humidity, whilst being negatively associated with air pressure, with different time lags.ConclusionsImported DF cases and mosquito density play a critical role in local DF transmission, together with weather variables. The establishment of an early warning system, using existing surveillance datasets will help to control and prevent dengue in Guangzhou, China.