Suchergebnisse

Dengue fever (DF) is one of the most rapidly spreading diseases in the world, and accurate forecasts of dengue in a timely manner might help local government implement effective control measures. To obtain the accurate forecasting of DF cases, it is crucial to model the long-term dependency in time series data, which is difficult for a typical machine learning method. This study aimed to develop a timely accurate forecasting model of dengue based on long short-term memory (LSTM) recurrent neural networks while only considering monthly dengue cases and climate factors. The performance of LSTM models was compared with the other previously published models when predicting DF cases one month into the future. Our results showed that the LSTM model reduced the average the root mean squared error (RMSE) of the predictions by 12.99% to 24.91% and reduced the average RMSE of the predictions in the outbreak period by 15.09% to 26.82% as compared with other candidate models. The LSTM model achieved superior performance in predicting dengue cases as compared with other previously published forecasting models. Moreover, transfer learning (TL) can improve the generalization ability of the model in areas with fewer dengue incidences. The findings provide a more precise forecasting dengue model and could be used for other dengue-like infectious diseases.

Dengue fever (DF) is the most serious mosquito-borne viral disease in the world and is significantly affected by temperature. Although associations between DF and temperatures have been reported repeatedly, conclusions have been inconsistent. Six databases were searched up to 23 March 2014, without language and geographical restrictions. The articles that studied the correlations between temperatures and dengue were selected, and a random-effects model was used to calculate the pooled odds ratio and 95% confidence intervals. Of 1589 identified articles, 137 were reviewed further, with 33 satisfying inclusion criteria. The closest associations were observed between mean temperature from the included studies (23.2–27.7 °C) and DF (OR 35.0% per 1 °C; 95% CI 18.3%–51.6%) positively. Additionally, minimum (18.1–24.2 °C) (29.5% per 1 °C; 20.9%–38.1%) and maximum temperature (28.0–34.5 °C) (28.9%; 10.3%–47.5%) were also associated with increased dengue transmission. The OR of DF incidence increased steeply from 22 °C to 29 °C, suggesting an inflexion of DF risk between these lower and upper limits of DF risk. This discovery is helpful for government decision-makers focused on preventing and controlling dengue in areas with temperatures within this range.

Sustainable development is best supported by intersectoral policies informed by a range of evidence and knowledge types (e.g. scientific and lay). Given China's rapid urbanisation, scale and global importance in climate mitigation, this study investigates how evidence is perceived and used to inform urban health and sustainability policies at central and local levels. Well-informed senior professionals in government/scientific agencies (12 in Beijing and 11 in Ningbo) were interviewed. A thematic analysis is presented using deductive and inductive coding. Government agency participants described formal remits and processes determining the scope and use of evidence by different tiers of government. Academic evidence was influential when commissioned by government departments. Public opinion and economic priorities were two factors that also influenced the use or weight of evidence in policymaking. This study shows that scientific evidence produced or commissioned by government was routinely used to inform urban health and sustainability policy. Extensive and routine data collection is regularly used to inform cyclical policy processes, which improves adaptive capacity. This study contributes to knowledge on the 'cultures of evidence use'. Environmental governance can be further improved through increased data-sharing and use of diverse knowledge types.

Knowledge, attitude, and practice (KAP) are three key components for reducing the adverse health impacts of heat waves. However, research in eastern China regarding this is scarce. The present study aimed to evaluate the heat wave-related KAP of a population in Licheng in northeast China. This cross-sectional study included 2241 participants. Data regarding demographic characteristics, KAP, and heat illnesses were collected using a structured questionnaire. Univariate analysis and unconditional logistic regression models were used to analyze the data. Most residents had high KAP scores, with a mean score of 12.23 (standard deviation = 2.23) on a 17-point scale. Urban women and participants aged 35–44 years had relatively high total scores, and those with high education levels had the highest total score. There was an increased risk of heat-related illness among those with knowledge scores of 3–5 on an 8-point scale with mean score of 5.40 (standard deviation = 1.45). Having a positive attitude toward sunstroke prevention and engaging in more preventive practices to avoid heat exposure had a protective interaction effect on reducing the prevalence of heat-related illnesses. Although the KAP scores were relatively high, knowledge and practice were lacking to some extent. Therefore, governments should further develop risk-awareness strategies that increase awareness and knowledge regarding the adverse health impact of heat and help in planning response strategies to improve the ability of individuals to cope with heat waves.

China is one of the largest countries in the world with nearly 20% of the world's population. There have been significant improvements in economy, education and technology over the last three decades. Due to substantial investments from all levels of government, the public health system in China has been improved since the 2003 severe acute respiratory syndrome (SARS) outbreak. However, infectious diseases still remain a major population health issue and this may be exacerbated by rapid urbanization and unprecedented impacts of climate change. This commentary aims to explore China's current capacity to manage infectious diseases which impair population health. It discusses the existing disease surveillance system and underscores the critical importance of strengthening the system. It also explores how the growing migrant population, dramatic changes in the natural landscape following rapid urbanization, and changing climatic conditions can contribute to the emergence and re-emergence of infectious disease. Continuing research on infectious diseases, urbanization and climate change may inform the country's capacity to deal with emerging and re-emerging infectious diseases in the future.

China is one of the largest countries in the world with nearly 20 of the world s population. There have been significant improvements in economy, education and technology over the last three decades. Due to substantial investments from all levels of government, the public health system in China has been improved since the 2003 severe acute respiratory syndrome (SARS) outbreak. However, infectious diseases still remain a major population health issue and this may be exacerbated by rapid urbanization and unprecedented impacts of climate change. This commentary aims to explore China s current capacity to manage infectious diseases which impair population health. It discusses the existing disease surveillance system and underscores the critical importance of strengthening the system. It also explores how the growing migrant population, dramatic changes in the natural landscape following rapid urbanization, and changing climatic conditions can contribute to the emergence and re-emergence of infectious disease. Continuing research on infectious diseases, urbanization and climate change may inform the country s capacity to deal with emerging and re-emerging infectious diseases in the future.

Limited information is available on the perceptions of stakeholders concerning the health co-benefits of greenhouse gas (GHG) emission reductions. The purpose of this study was to investigate the perceptions of urban residents on the health co-benefits involving GHG abatement and related influencing factors in three cities in China. Beijing, Ningbo and Guangzhou were selected for this survey. Participants were recruited from randomly chosen committees, following quotas for gender and age in proportion to the respective population shares. Chi-square or Fisher's exact tests were employed to examine the associations between socio-demographic variables and individuals' perceptions of the health co-benefits related to GHG mitigation. Unconditional logistic regression analysis was performed to investigate the influencing factors of respondents' awareness about the health co-benefits. A total of 1159 participants were included in the final analysis, of which 15.9% reported that they were familiar with the health co-benefits of GHG emission reductions. Those who were younger, more educated, with higher family income, and with registered urban residence, were more likely to be aware of health co-benefits. Age, attitudes toward air pollution and governmental efforts to improve air quality, suffering from respiratory diseases, and following low carbon lifestyles are significant predictors of respondents' perceptions on the health co-benefits. These findings may not only provide information to policy-makers to develop and implement public welcome policies of GHG mitigation, but also help to bridge the gap between GHG mitigation measures and public engagement as well as willingness to change health-related behaviors.

Limited information is available on the perceptions of stakeholders concerning the health co-benefits of greenhouse gas (GHG) emission reductions. The purpose of this study was to investigate the perceptions of urban residents on the health co-benefits involving GHG abatement and related influencing factors in three cities in China. Beijing, Ningbo and Guangzhou were selected for this survey. Participants were recruited from randomly chosen committees, following quotas for gender and age in proportion to the respective population shares. Chi-square or Fisher's exact tests were employed to examine the associations between socio-demographic variables and individuals' perceptions of the health co-benefits related to GHG mitigation. Unconditional logistic regression analysis was performed to investigate the influencing factors of respondents' awareness about the health co-benefits. A total of 1159 participants were included in the final analysis, of which 15.9% reported that they were familiar with the health co-benefits of GHG emission reductions. Those who were younger, more educated, with higher family income, and with registered urban residence, were more likely to be aware of health co-benefits. Age, attitudes toward air pollution and governmental efforts to improve air quality, suffering from respiratory diseases, and following low carbon lifestyles are significant predictors of respondents' perceptions on the health co-benefits. These findings may not only provide information to policy-makers to develop and implement public welcome policies of GHG mitigation, but also help to bridge the gap between GHG mitigation measures and public engagement as well as willingness to change health-related behaviors.