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A national surveillance system on hand, foot, and mouth disease (HFMD) was launched in 2008 in China. Since then, millions of HFMD cases have been reported each year, with enterovirus A71 (EV-A71), coxsackievirus A16 (CV-A16), and coxsackievirus A6 (CV-A6) as the major causative pathogens. Long-term surveillance of viral infection rates and genetic changes is essential for understanding the disease epidemiology pattern. Here, we analyzed molecular surveillance data on CV-A16 covering a period of 12 years (2008-2019) in Guangdong, China, one of the regions reporting the largest number of HFMD cases. Full VP1 sequences of 456 strains were determined to examine the genetic diversity and changes in the distribution of CV-A16 variants. Our study revealed an irregular pattern of CV-A16 infections in Guangdong. Different from the cyclic epidemics observed in some Asia-Pacific regions, there was a continuously high CV-A16 infection rate from 2008 to 2014, and after a period of lower epidemic activity in 2015-2017, an upsurge of CV-A16 infection was observed in 2018-2019. Cocirculation of subgenotypes B1a and B1b was observed, but while subgenotype B1a was predominant from 2008 to 2012, it appears to have been replaced by B1b, which has circulated as the predominant subgenotype since 2013. Phylogenetic analysis showed that most of the circulating CV-A16 strains are endemic, with occasional transmission between neighboring regions. The re-emergence of B1a in 2016-2019 in Guangdong was likely the result of introduction(s) from Southeast Asia. These results highlight the importance of continuous molecular surveillance from different areas, which will improve our understanding of the origin of the epidemic and facilitate the development of strategies for HFMD disease control. [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]

Background: In China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue. Methodology/Principal findings: Weekly dengue cases, Baidu search queries and climate factors (mean temperature, relative humidity and rainfall) during 2011–2014 in Guangdong were gathered. A dengue search index was constructed for developing the predictive models in combination with climate factors. The observed year and week were also included in the models to control for the long-term trend and seasonality. Several machine learning algorithms, including the support vector regression (SVR) algorithm, step-down linear regression model, gradient boosted regression tree algorithm (GBM), negative binomial regression model (NBM), least absolute shrinkage and selection operator (LASSO) linear regression model and generalized additive model (GAM), were used as candidate models to predict dengue incidence. Performance and goodness of fit of the models were assessed using the root-mean-square error (RMSE) and R-squared measures. The residuals of the models were examined using the autocorrelation and partial autocorrelation function analyses to check the validity of the models. The models were further validated using dengue surveillance data from five other provinces. The epidemics during the last 12 weeks and the peak of the 2014 large outbreak were accurately forecasted by the SVR model selected by a cross-validation technique. Moreover, the SVR model had the consistently smallest prediction error rates for tracking the dynamics of dengue and forecasting the outbreaks in other areas in China. Conclusion and significance: The proposed SVR model achieved a superior performance in comparison with other forecasting techniques assessed in this study. The findings can help the government and community respond early to dengue epidemics. [ABSTRACT FROM AUTHOR]

Background: The avian influenza A H7N9 virus has caused infections in human beings in China since 2013. A large epidemic in 2016-17 prompted concerns that the epidemiology of the virus might have changed, increasing the threat of a pandemic. We aimed to describe the epidemiological characteristics, clinical severity, and time-to-event distributions of patients infected with A H7N9 in the 2016-17 epidemic compared with previous epidemics.Methods: In this epidemiological study, we obtained information about all laboratory-confirmed human cases of A H7N9 virus infection reported in mainland China as of Feb 23, 2017, from an integrated electronic database managed by the China Center for Disease Control and Prevention (CDC) and provincial CDCs. Every identified human case of A H7N9 virus infection was required to be reported to China CDC within 24 h via a national surveillance system for notifiable infectious diseases. We described the epidemiological characteristics across epidemics, and estimated the risk of death, mechanical ventilation, and admission to the intensive care unit for patients admitted to hospital for routine clinical practice rather than for isolation purpose. We estimated the incubation periods, and time delays from illness onset to hospital admission, illness onset to initiation of antiviral treatment, and hospital admission to death or discharge using survival analysis techniques.Findings: Between Feb 19, 2013, and Feb 23, 2017, 1220 laboratory-confirmed human infections with A H7N9 virus were reported in mainland China, with 134 cases reported in the spring of 2013, 306 in 2013-14, 219 in 2014-15, 114 in 2015-16, and 447 in 2016-17. The 2016-17 A H7N9 epidemic began earlier, spread to more districts and counties in affected provinces, and had more confirmed cases than previous epidemics. The proportion of cases in middle-aged adults increased steadily from 41% (55 of 134) to 57% (254 of 447) from the first epidemic to the 2016-17 epidemic. Proportions of cases in semi-urban and rural residents in the 2015-16 and 2016-17 epidemics (63% [72 of 114] and 61% [274 of 447], respectively) were higher than those in the first three epidemics (39% [52 of 134], 55% [169 of 306], and 56% [122 of 219], respectively). The clinical severity of individuals admitted to hospital in the 2016-17 epidemic was similar to that in the previous epidemics.Interpretation: Age distribution and case sources have changed gradually across epidemics since 2013, while clinical severity has not changed substantially. Continued vigilance and sustained intensive control efforts are needed to minimise the risk of human infection with A H7N9 virus.Funding: The National Science Fund for Distinguished Young Scholars. [ABSTRACT FROM AUTHOR]

Background: Dengue fever (DF) in Guangzhou, Guangdong province in China is an important public health issue. The problem was highlighted in 2014 by a large, unprecedented outbreak. In order to respond in a more timely manner and hence better control such potential outbreaks in the future, this study develops an early warning model that integrates internet-based query data into traditional surveillance data. Methodology and principal findings: A Dengue Baidu Search Index (DBSI) was collected from the Baidu website for developing a predictive model of dengue fever in combination with meteorological and demographic factors. Generalized additive models (GAM) with or without DBSI were established. The generalized cross validation (GCV) score and deviance explained indexes, intraclass correlation coefficient (ICC) and root mean squared error (RMSE), were respectively applied to measure the fitness and the prediction capability of the models. Our results show that the DBSI with one-week lag has a positive linear relationship with the local DF occurrence, and the model with DBSI (ICC:0.94 and RMSE:59.86) has a better prediction capability than the model without DBSI (ICC:0.72 and RMSE:203.29). Conclusions: Our study suggests that a DSBI combined with traditional disease surveillance and meteorological data can improve the dengue early warning system in Guangzhou. [ABSTRACT FROM AUTHOR]

Background: An explosive outbreak of dengue fever occurred in Guangdong Province, China in 2014. A community-based integrated intervention was applied to control this outbreak in the capital city Guangzhou, where dengue epidemic was mainly caused by imported cases. Methodology/Principal Findings: We used a time series generalized additive model based on meteorological factors to assess the effectiveness of this intervention. The results showed that there was significant reduction in mosquito density following the intervention, and there was a 70.47% (95% confidence interval: 66.07%, 74.88%) reduction in the reported dengue cases compared with the predicted cases after 12 days since the beginning of the intervention, we estimated that a total of 23,302 dengue cases were prevented. Conclusions: This study suggests that an integrated dengue intervention program has significant effects to control a dengue outbreak in areas where dengue epidemic was mainly caused by imported dengue cases. [ABSTRACT FROM AUTHOR]