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Introduction: Dengue 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 Findings: In 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. Conclusions: Together 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. Author Summary: Dengue has emerged as the most important mosquito-borne viral disease globally. With increasing global trade and population movement, the disease is transferred to regions which were previously dengue free. When dengue vector exists and weather factors are suitable, there is the possibility for dengue transmission and even outbreaks happening. Dengue is still believed to be a non-endemic disease in China, with imported cases playing a vital role in local dengue transmission. The situation of dengue is becoming more and more severe with two successive large outbreaks hitting southern China in 2013 and 2014, and the dengue outbreak in 2014 was unprecedented. In this study, we aim to develop a dengue forecasting model that would provide an early warning of dengue outbreak to allow local health authorities and communities to implement timely effective control measures. Our model showed that imported cases in the previous month, monthly minimum temperature in the previous month and monthly accumulative precipitation with three month lags could predict dengue outbreak ahead by one month. We concluded that these variables could be used to develop an early dengue warning model to provide evidence-based decisions for disease control and prevention and including the utilization of limited resources. [ABSTRACT FROM AUTHOR]

Introduction: Each 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 Findings: Three 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. Conclusions: Imported 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. [ABSTRACT FROM AUTHOR]

Background The adverse effects of heat on workers' health and work productivity are well documented. However, the resultant economic consequences and productivity loss are less understood. This review aims to summarize the retrospective and potential future economic burden of workplace heat exposure in the context of climate change. Methods Literature was searched from database inception to October 2020 using Embase, PubMed, and Scopus. Articles were limited to original human studies investigating costs from occupational heat stress in English. Results Twenty studies met the criteria. Eighteen studies estimated costs from heat-induced labor productivity loss. Predicted global costs from lost worktime, in US$, were 280 billion in 1995, 311 billion in 2010 (≈0.5% of GDP), 2.5 trillion in 2030 (>1% of GDP) and up to 4.0% of GDP by 2100, with additional expenses after considering reduced work efficiency. Three studies estimated heat-related healthcare expenses from occupational injuries with averaged annual costs (US$) exceeding 1 million in Spain, 1 million in Guangzhou, China and 250,000 in Adelaide, Australia. Low- and middle-income countries and countries with warmer climates had greater losses as a proportion of GDP. Greater costs per worker were observed in outdoor industries, amongst males, and workers aged 25 to 44 years. Conclusions The estimated global economic burden of occupational heat stress is substantial. Climate change adaptation and mitigation strategies should be implemented to likely minimize future costs. Further research exploring the relationship between occupational heat stress and related expenses from lost productivity, decreased work efficiency and healthcare, and costs stratified by demographic factors, is warranted. Key messages The estimated retrospective and future economic burden from occupational heat stress is large. Responding to climate change is crucial to minimize this burden. Analyzing heat-attributable occupational costs may guide the development of workplace heat management policies and practices as part of global warming strategies. [ABSTRACT FROM AUTHOR]

Dengue transmission in urban areas is strongly influenced by a range of biological and environmental factors, yet the key drivers still need further exploration. To better understand mechanisms of environment–mosquito–urban dengue transmission, we propose an empirical model parameterized and cross-validated from a unique dataset including viral gene sequences, vector dynamics and human dengue cases in Guangzhou, China, together with a 36-year urban environmental change maps investigated by spatiotemporal satellite image fusion. The dengue epidemics in Guangzhou are highly episodic and were not associated with annual rainfall over time. Our results indicate that urban environmental changes, especially variations in surface area covered by water in urban areas, can substantially alter the virus population and dengue transmission. The recent severe dengue outbreaks in Guangzhou may be due to the surge in an artificial lake construction, which could increase infection force between vector (mainly Aedes albopictus ) and host when urban water area significantly increased. Impacts of urban environmental change on dengue dynamics may not have been thoroughly investigated in the past studies and more work needs to be done to better understand the consequences of urbanization processes in our changing world. [ABSTRACT FROM AUTHOR]

The adverse effects of heat on workers' health and work productivity are well documented. However, the resultant economic consequences and productivity loss are less understood. This review aims to summarize the retrospective and potential future economic burden of workplace heat exposure in the context of climate change. Literature was searched from database inception to October 2020 using Embase, PubMed, and Scopus. Articles were limited to original human studies investigating costs from occupational heat stress in English. Twenty studies met criteria for inclusion. Eighteen studies estimated costs secondary to heat-induced labor productivity loss. Predicted global costs from lost worktime, in US$, were 280 billion in 1995, 311 billion in 2010 (≈0.5% of GDP), 2.4–2.5 trillion in 2030 (>1% of GDP) and up to 4.0% of GDP by 2100. Three studies estimated heat-related healthcare expenses from occupational injuries with averaged annual costs (US$) exceeding 1 million in Spain, 1 million in Guangzhou, China and 250,000 in Adelaide, Australia. Low- and middle-income countries and countries with warmer climates had greater losses as a proportion of GDP. Greater costs per worker were observed in outdoor industries, medium-sized businesses, amongst males, and workers aged 25–44 years. The estimated global economic burden of occupational heat stress is substantial. Climate change adaptation and mitigation strategies should be implemented to likely minimize future costs. Further research exploring the relationship between occupational heat stress and related expenses from lost productivity, decreased work efficiency and healthcare, and costs stratified by demographic factors, is warranted. Key messages. The estimated retrospective and future economic burden from occupational heat stress is large. Responding to climate change is crucial to minimize this burden. Analyzing heat-attributable occupational costs may guide the development of workplace heat management policies and practices as part of global warming strategies. [ABSTRACT FROM AUTHOR]