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Abstract Background China experienced an unprecedented outbreak of dengue in 2014, and the number of dengue cases reached the highest level over the past 25 years. There is a significant delay in the release of official case count data, and our ability to timely track the timing and magnitude of local outbreaks of dengue remains limited. Material and methods We developed an ensemble penalized regression algorithm (EPRA) for initializing near-real time forecasts of the dengue epidemic trajectory by integrating different penalties (LASSO, Ridge, Elastic Net, SCAD and MCP) with the techniques of iteratively sampling and model averaging. Multiple streams of near-real time data including dengue-related Baidu searches, Sina Weibo posts, and climatic conditions with historical dengue incidence were used. We compared the predictive power of the EPRA with the alternates, penalized regression models using single penalties, to retrospectively forecast weekly dengue incidence and detect outbreak occurrence defined using different cutoffs, during the periods of 2011–2016 in Guangzhou, south China. Results The EPRA showed the best or at least comparable performance for 1-, 2-week ahead out-of-sample and leave-one-out cross validation forecasts. The findings indicate that skillful near-real time forecasts of dengue and confidence in those predictions can be made. For detecting dengue outbreaks, the EPRA predicted periods of high incidence of dengue more accurately than the alternates. Conclusion This study developed a statistically rigorous approach for near-real time forecast of dengue in China. The EPRA provides skillful forecasts and can be used as timely and complementary ways to assess dengue dynamics, which will help to design interventions to mitigate dengue transmission. Graphical abstract Unlabelled Image Highlights • A novel ensemble model has been developed for timely tracking the timing and magnitude of dengue epidemics. • The proposed algorithm had the best performance in comparison with regression models using single penalties. • The ensemble forecast model provides skillful forecasts of the dynamics of dengue in China. [ABSTRACT FROM AUTHOR]

Background Dengue fever is an important infectious disease in Guangzhou, China; previous studies on the effects of weather factors on the incidence of dengue fever did not consider the linearity of the associations. Methods This study evaluated the effects of daily mean temperature, relative humidity and rainfall on the incidence of dengue fever. A generalized additive model with splines smoothing function was performed to examine the effects of daily mean, minimum and maximum temperatures, relative humidity and rainfall on incidence of dengue fever during 2006–2014. Results Our analysis detected a non-linear effect of mean, minimum and maximum temperatures and relative humidity on dengue fever with the thresholds at 28 °C, 23 °C and 32 °C for daily mean, minimum and maximum temperatures, 76% for relative humidity, respectively. Below the thresholds, there was a significant positive effect, the excess risk in dengue fever for each 1 °C in the mean temperature at lag7–14 days was 10.21%, (95% CI: 6.62% to 13.92%), 7.10% (95% CI: 4.99%, 9.26%) for 1 °C increase in daily minimum temperature in lag 11 days, and 2.27% (95% CI: 0.84%, 3.72%) for 1 °C increase in daily maximum temperature in lag 10 days; and each 1% increase in relative humidity of lag7–14 days was associated with 1.95% (95% CI: 1.21% to 2.69%) in risk of dengue fever. Conclusions Future prevention and control measures and epidemiology studies on dengue fever should consider these weather factors based on their exposure-response relationship. [ABSTRACT FROM AUTHOR]

The emergence of avian influenza A(H7N9) virus poses a pandemic threat to human beings. It was proposed that meteorological factors might be important environmental factors favoring the occurrence of H7N9 infection, but evidence is still inadequate. In this study, we aimed to investigate the independent and interactive effects of ambient temperature (TM) and absolute humidity (AH) on H7N9 infection risks in China. The individual information of all reported H7N9 cases and daily meteorological data in five provinces/municipality (Zhejiang, Jiangsu, Shanghai, Fujian, and Guangdong) in China during 2013–2016 were collected. We employed a case-crossover study design, in which the 7–10 days before the onset date of each H7N9 case was defined as the hazard period, and 4 weeks before the hazard period was taken as the control period. The average levels of meteorological factors were calculated during the hazard and control periods. A Cox regression model was used to estimate the independent and interactive effects of TM and vapor pressure (VP), an indicator of AH, on H7N9 infection risks. A total of 738 H7N9 cases were included in the present study. Significantly nonlinear negative associations of TM and VP with H7N9 infection risks were observed in all cases, and in cases from northern and southern regions. There were significant interactive effects between TM and VP on H7N9 infection risks, and the risks of H7N9 infection were higher in cold-dry days than other days. We further observed different risky windows of H7N9 infection in the northern (TM: 0–18 °C, VP: 3 13 mb) and southern areas (TM: 7–21 °C, VP: 3–17 mb). We concluded that ambient temperature and absolute humidity had significant independent and interactive effects on H7N9 infection risks in China, and the risks of H7N9 infection were higher in cold-dry days. The risky windows of H7N9 infection were different in the northern and southern areas. [ABSTRACT FROM AUTHOR]