The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Hualiang Lin - One of the best experts on this subject based on the ideXlab platform.
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characterizing a large outbreak of Dengue Fever in guangdong province china
Infectious Diseases of Poverty, 2016Co-Authors: Jianpeng Xiao, Hualiang Lin, Aiping Deng, Tie Song, Zhiqiang Peng, Tao Liu, Shannon Rutherford, Weilin Zeng, Yonghui ZhangAbstract:Dengue cases have been reported each year for the past 25 years in Guangdong Province, China with a recorded historical peak in 2014. This study aims to describe the epidemiological characteristics of this large outbreak in order to better understand its epidemic factors and to inform control strategies. Data for clinically diagnosed and laboratory-confirmed Dengue Fever cases in 2014 were extracted from the China Notifiable Infectious Disease Reporting System. We analyzed the incidence and characteristics of imported and indigenous cases in terms of population, temporal and spatial distributions. A total of 45 224 Dengue Fever cases and 6 deaths were notified in Guangdong Province in 2014, with an incidence of 47.3 per 100 000 people. The elderly (65+ years) represented 11.7 % of total indigenous cases with the highest incidence (72.3 per 100 000). Household workers and the unemployed accounted for 23.1 % of indigenous cases. The majority of indigenous cases occurred in the 37th to 44th week of 2014 (September and October) and almost all (20 of 21) prefecture-level cities in Guangdong were affected. Compared to the non-Pearl River Delta Region, the Pearl River Delta Region accounted for the majority of Dengue cases and reported cases earlier in 2014. Dengue virus serotypes 1 (DENV-1), 2 (DENV-2) and 3 (DENV-3) were detected and DENV-1 was predominant (88.4 %). Dengue Fever is a serious public health problem and is emerging as a continuous threat in Guangdong Province. There is an urgent need to enhance Dengue surveillance and control, especially for the high-risk populations in high-risk areas.
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spatial analysis of Dengue Fever in guangdong province china 2001 2006
Asia-Pacific Journal of Public Health, 2014Co-Authors: Chunxiao Liu, Qiyong Liu, Hualiang Lin, Benqiang Xin, Jun NieAbstract:Guangdong Province is the area most seriously affected by Dengue Fever in China. In this study, we describe the spatial distribution of Dengue Fever in Guangdong Province from 2001 to 2006 with the objective of informing priority areas for public health planning and resource allocation. Annualized incidence at a county level was calculated and mapped to show crude incidence, excess hazard, and spatial smoothed incidence. Geographic information system–based spatial scan statistics was conducted to detect the spatial distribution pattern of Dengue Fever incidence at the county level. Spatial scan cluster analyses suggested that counties around Guangzhou City and Chaoshan Region were at increased risk for Dengue Fever (P < .01). Some spatial clusters of Dengue Fever were found in Guangdong Province, which allowed intervention measures to be targeted for maximum effect.
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time series analysis of Dengue Fever and weather in guangzhou china
BMC Public Health, 2009Co-Authors: Weizhong Yang, Hualiang Lin, Linwei Tian, Jimin Sun, Qiyong LiuAbstract:Background Monitoring and predicting Dengue incidence facilitates early public health responses to minimize morbidity and mortality. Weather variables are potential predictors of Dengue incidence. This study explored the impact of weather variability on the transmission of Dengue Fever in the subtropical city of Guangzhou, China.
Mauricio Santillana - One of the best experts on this subject based on the ideXlab platform.
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a dynamic ensemble learning approach to forecast Dengue Fever epidemic years in brazil using weather and population susceptibility cycles
Journal of the Royal Society Interface, 2021Co-Authors: Mauricio Santillana, Sarah F Mcgough, Leonardo Clemente, Nathan J KutzAbstract:Transmission of Dengue Fever depends on a complex interplay of human, climate and mosquito dynamics, which often change in time and space. It is well known that its disease dynamics are highly infl...
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incorporating human mobility data improves forecasts of Dengue Fever in thailand
Scientific Reports, 2021Co-Authors: Mathew V Kiang, Mauricio Santillana, Jarvis T Chen, Jukkapekka Onnela, Nancy Krieger, Kenth Engomonsen, Nattwut Ekapirat, Darin Areechokchai, Preecha PrempreeAbstract:Over 390 million people worldwide are infected with Dengue Fever each year. In the absence of an effective vaccine for general use, national control programs must rely on hospital readiness and targeted vector control to prepare for epidemics, so accurate forecasting remains an important goal. Many Dengue forecasting approaches have used environmental data linked to mosquito ecology to predict when epidemics will occur, but these have had mixed results. Conversely, human mobility, an important driver in the spatial spread of infection, is often ignored. Here we compare time-series forecasts of Dengue Fever in Thailand, integrating epidemiological data with mobility models generated from mobile phone data. We show that geographically-distant provinces strongly connected by human travel have more highly correlated Dengue incidence than weakly connected provinces of the same distance, and that incorporating mobility data improves traditional time-series forecasting approaches. Notably, no single model or class of model always outperformed others. We propose an adaptive, mosaic forecasting approach for early warning systems.
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incorporating human mobility data improves forecasts of Dengue Fever in thailand
medRxiv, 2020Co-Authors: Mathew V Kiang, Mauricio Santillana, Jarvis T Chen, Jukkapekka Onnela, Nancy Krieger, Kenth Engomonsen, Nattwut Ekapirat, Darin Areechokchai, Richard J Maude, Caroline O BuckeeAbstract:Over 390 million people worldwide are infected with Dengue Fever each year. In the absence of an effective vaccine for general use, national control programs must rely on hospital readiness and targeted vector control to prepare for epidemics, so accurate forecasting remains an important goal. Many Dengue forecasting approaches have used environmental data linked to mosquito ecology to predict when epidemics will occur, but these have had mixed results. Conversely, human mobility, an important driver in the spatial spread of infection, is often ignored. Here we compare time-series forecasts of Dengue Fever in Thailand, integrating epidemiological data with mobility models generated from mobile phone data. We show that long-distance connectivity is correlated with Dengue incidence at forecasting horizons of up to three months, and that incorporating mobility data improves traditional time-series forecasting approaches. Notably, no single model or class of model always outperformed others. We propose an adaptive, mosaic forecasting approach for early warning systems.
Archie C A Clements - One of the best experts on this subject based on the ideXlab platform.
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spatiotemporal analysis of indigenous and imported Dengue Fever cases in guangdong province china
BMC Infectious Diseases, 2012Co-Authors: Wenwu Yin, Yonghui Zhang, Archie C A Clements, Gail M Williams, Shengjie Lai, Hang Zhou, Dan Zhao, Yansha Guo, Jinfeng Wang, Weizhong YangAbstract:Dengue Fever has been a major public health concern in China since it re-emerged in Guangdong province in 1978. This study aimed to explore spatiotemporal characteristics of Dengue Fever cases for both indigenous and imported cases during recent years in Guangdong province, so as to identify high-risk areas of the province and thereby help plan resource allocation for Dengue interventions. Notifiable cases of Dengue Fever were collected from all 123 counties of Guangdong province from 2005 to 2010. Descriptive temporal and spatial analysis were conducted, including plotting of seasonal distribution of cases, and creating choropleth maps of cumulative incidence by county. The space-time scan statistic was used to determine space-time clusters of Dengue Fever cases at the county level, and a geographical information system was used to visualize the location of the clusters. Analysis were stratified by imported and indigenous origin. 1658 Dengue Fever cases were recorded in Guangdong province during the study period, including 94 imported cases and 1564 indigenous cases. Both imported and indigenous cases occurred more frequently in autumn. The areas affected by the indigenous and imported cases presented a geographically expanding trend over the study period. The results showed that the most likely cluster of imported cases (relative risk = 7.52, p < 0.001) and indigenous cases (relative risk = 153.56, p < 0.001) occurred in the Pearl River Delta Area; while a secondary cluster of indigenous cases occurred in one district of the Chao Shan Area (relative risk = 471.25, p < 0.001). This study demonstrated that the geographic range of imported and indigenous Dengue Fever cases has expanded over recent years, and cases were significantly clustered in two heavily urbanised areas of Guangdong province. This provides the foundation for further investigation of risk factors and interventions in these high-risk areas.
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spatial patterns and socioecological drivers of Dengue Fever transmission in queensland australia
Environmental Health Perspectives, 2012Co-Authors: Archie C A Clements, Gail M Williams, Shilu Tong, Kerrie MengersenAbstract:Background: Understanding how socioecological factors affect the transmission of Dengue Fever (DF) may help to develop an early warning system of DF.Objectives: We examined the impact of socioecolo...
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spatiotemporal analysis of indigenous and imported Dengue Fever cases in guangdong province china
Faculty of Health; Institute of Health and Biomedical Innovation, 2012Co-Authors: Wenwu Yin, Yonghui Zhang, Archie C A Clements, Gail M Williams, Shengjie Lai, Hang Zhou, Dan Zhao, Yansha Guo, Jinfeng Wang, Weizhong YangAbstract:Background Dengue Fever has been a major public health concern in China since it re-emerged in Guangdong province in 1978. This study aimed to explore spatiotemporal characteristics of Dengue Fever cases for both indigenous and imported cases during recent years in Guangdong province, so as to identify high-risk areas of the province and thereby help plan resource allocation for Dengue interventions. Methods Notifiable cases of Dengue Fever were collected from all 123 counties of Guangdong province from 2005 to 2010. Descriptive temporal and spatial analysis were conducted, including plotting of seasonal distribution of cases, and creating choropleth maps of cumulative incidence by county. The space-time scan statistic was used to determine space-time clusters of Dengue Fever cases at the county level, and a geographical information system was used to visualize the location of the clusters. Analysis were stratified by imported and indigenous origin. Results 1658 Dengue Fever cases were recorded in Guangdong province during the study period, including 94 imported cases and 1564 indigenous cases. Both imported and indigenous cases occurred more frequently in autumn. The areas affected by the indigenous and imported cases presented a geographically expanding trend over the study period. The results showed that the most likely cluster of imported cases (relative risk = 7.52, p < 0.001) and indigenous cases (relative risk = 153.56, p < 0.001) occurred in the Pearl River Delta Area; while a secondary cluster of indigenous cases occurred in one district of the Chao Shan Area (relative risk = 471.25, p < 0.001). Conclusions This study demonstrated that the geographic range of imported and indigenous Dengue Fever cases has expanded over recent years, and cases were significantly clustered in two heavily urbanised areas of Guangdong province. This provides the foundation for further investigation of risk factors and interventions in these high-risk areas.
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Dengue Fever and el nino southern oscillation in queensland australia a time series predictive model
Faculty of Health; Institute of Health and Biomedical Innovation, 2009Co-Authors: Archie C A Clements, Gail M Williams, Shilu TongAbstract:Background It remains unclear over whether it is possible to develop an epidemic forecasting model for transmission of Dengue Fever in Queensland, Australia. Objectives To examine the potential impact of El Nino/Southern Oscillation on the transmission of Dengue Fever in Queensland, Australia and explore the possibility of developing a forecast model of Dengue Fever. Methods Data on the Southern Oscillation Index (SOI), an indicator of El Nino/Southern Oscillation activity, were obtained from the Australian Bureau of Meteorology. Numbers of Dengue Fever cases notified and the numbers of postcode areas with Dengue Fever cases between January 1993 and December 2005 were obtained from the Queensland Health and relevant population data were obtained from the Australia Bureau of Statistics. A multivariate Seasonal Auto-regressive Integrated Moving Average model was developed and validated by dividing the data file into two datasets: the data from January 1993 to December 2003 were used to construct a model and those from January 2004 to December 2005 were used to validate it. Results A decrease in the average SOI (ie, warmer conditions) during the preceding 3–12 months was significantly associated with an increase in the monthly numbers of postcode areas with Dengue Fever cases (β=−0.038; p = 0.019). Predicted values from the Seasonal Auto-regressive Integrated Moving Average model were consistent with the observed values in the validation dataset (root-mean-square percentage error: 1.93%). Conclusions Climate variability is directly and/or indirectly associated with Dengue transmission and the development of an SOI-based epidemic forecasting system is possible for Dengue Fever in Queensland, Australia.
Shihchun Lung - One of the best experts on this subject based on the ideXlab platform.
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higher temperature and urbanization affect the spatial patterns of Dengue Fever transmission in subtropical taiwan
Science of The Total Environment, 2009Co-Authors: Peichih Wu, Shihchun Lung, Hueyjen Jenny SuAbstract:Our study conducted spatial analysis to examine how temperature and other environmental factors might affect Dengue Fever distributions, and to forecast areas with potential risk for Dengue Fever endemics with predicted climatic change in Taiwan. Geographic information system (GIS) was used to demonstrate the spatial patterns of all studied variables across 356 townships. Relationships between cumulative incidence of Dengue Fever, climatic and non-climatic factors were explored. Numbers of months with average temperature higher than 18 °C per year and degree of urbanization were found to be associated with increasing risk of Dengue Fever incidence at township level. With every 1 °C increase of monthly average temperature, the total population at risk for Dengue Fever transmission would increase by 1.95 times (from 3,966,173 to 7,748,267). A highly-suggested warmer trend, with a statistical model, across the Taiwan Island is predicted to result in a sizable increase in population and geographical areas at higher risk for Dengue Fever epidemics.
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higher temperature and urbanization affect the spatial patterns of Dengue Fever transmission in subtropical taiwan
Science of The Total Environment, 2009Co-Authors: Jinnguey Lay, Howran Guo, Chuanyao Lin, Shihchun LungAbstract:Our study conducted spatial analysis to examine how temperature and other environmental factors might affect Dengue Fever distributions, and to forecast areas with potential risk for Dengue Fever endemics with predicted climatic change in Taiwan. Geographic information system (GIS) was used to demonstrate the spatial patterns of all studied variables across 356 townships. Relationships between cumulative incidence of Dengue Fever, climatic and non-climatic factors were explored. Numbers of months with average temperature higher than 18 degrees C per year and degree of urbanization were found to be associated with increasing risk of Dengue Fever incidence at township level. With every 1 degrees C increase of monthly average temperature, the total population at risk for Dengue Fever transmission would increase by 1.95 times (from 3,966,173 to 7,748,267). A highly-suggested warmer trend, with a statistical model, across the Taiwan Island is predicted to result in a sizable increase in population and geographical areas at higher risk for Dengue Fever epidemics.
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weather as an effective predictor for occurrence of Dengue Fever in taiwan
Acta Tropica, 2007Co-Authors: Howran Guo, Shihchun Lung, Chuanyao LinAbstract:We evaluated the impacts of weather variability on the occurrence of Dengue Fever in a major metropolitan city, Kaohsiung, in southern Taiwan using time-series analysis. Autoregressive integrated moving average (ARIMA) models showed that the incidence of Dengue Fever was negatively associated with monthly temperature deviation (beta=-0.126, p=0.044), and a reverse association was also found with relative humidity (beta=-0.025, p=0.048). Both factors were observed to present their most prominent effects at a time lag of 2 months. Meanwhile, vector density record, a conventional approach often applied as a predictor for outbreak, did not appear to be a good one for diseases occurrence. Weather variability was identified as a meaningful and significant indicator for the increasing occurrence of Dengue Fever in this study, and it might be feasible to be adopted for predicting the influences of rising average temperature on the occurrence of infectious diseases of such kind at a city level. Further studies should take into account variations of socio-ecological changes and disease transmission patterns to better propose the increasing risk for infectious disease outbreak by applying the conveniently accumulated information of weather variability.
Yonghui Zhang - One of the best experts on this subject based on the ideXlab platform.
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characterizing a large outbreak of Dengue Fever in guangdong province china
Infectious Diseases of Poverty, 2016Co-Authors: Jianpeng Xiao, Hualiang Lin, Aiping Deng, Tie Song, Zhiqiang Peng, Tao Liu, Shannon Rutherford, Weilin Zeng, Yonghui ZhangAbstract:Dengue cases have been reported each year for the past 25 years in Guangdong Province, China with a recorded historical peak in 2014. This study aims to describe the epidemiological characteristics of this large outbreak in order to better understand its epidemic factors and to inform control strategies. Data for clinically diagnosed and laboratory-confirmed Dengue Fever cases in 2014 were extracted from the China Notifiable Infectious Disease Reporting System. We analyzed the incidence and characteristics of imported and indigenous cases in terms of population, temporal and spatial distributions. A total of 45 224 Dengue Fever cases and 6 deaths were notified in Guangdong Province in 2014, with an incidence of 47.3 per 100 000 people. The elderly (65+ years) represented 11.7 % of total indigenous cases with the highest incidence (72.3 per 100 000). Household workers and the unemployed accounted for 23.1 % of indigenous cases. The majority of indigenous cases occurred in the 37th to 44th week of 2014 (September and October) and almost all (20 of 21) prefecture-level cities in Guangdong were affected. Compared to the non-Pearl River Delta Region, the Pearl River Delta Region accounted for the majority of Dengue cases and reported cases earlier in 2014. Dengue virus serotypes 1 (DENV-1), 2 (DENV-2) and 3 (DENV-3) were detected and DENV-1 was predominant (88.4 %). Dengue Fever is a serious public health problem and is emerging as a continuous threat in Guangdong Province. There is an urgent need to enhance Dengue surveillance and control, especially for the high-risk populations in high-risk areas.
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spatiotemporal analysis of indigenous and imported Dengue Fever cases in guangdong province china
BMC Infectious Diseases, 2012Co-Authors: Wenwu Yin, Yonghui Zhang, Archie C A Clements, Gail M Williams, Shengjie Lai, Hang Zhou, Dan Zhao, Yansha Guo, Jinfeng Wang, Weizhong YangAbstract:Dengue Fever has been a major public health concern in China since it re-emerged in Guangdong province in 1978. This study aimed to explore spatiotemporal characteristics of Dengue Fever cases for both indigenous and imported cases during recent years in Guangdong province, so as to identify high-risk areas of the province and thereby help plan resource allocation for Dengue interventions. Notifiable cases of Dengue Fever were collected from all 123 counties of Guangdong province from 2005 to 2010. Descriptive temporal and spatial analysis were conducted, including plotting of seasonal distribution of cases, and creating choropleth maps of cumulative incidence by county. The space-time scan statistic was used to determine space-time clusters of Dengue Fever cases at the county level, and a geographical information system was used to visualize the location of the clusters. Analysis were stratified by imported and indigenous origin. 1658 Dengue Fever cases were recorded in Guangdong province during the study period, including 94 imported cases and 1564 indigenous cases. Both imported and indigenous cases occurred more frequently in autumn. The areas affected by the indigenous and imported cases presented a geographically expanding trend over the study period. The results showed that the most likely cluster of imported cases (relative risk = 7.52, p < 0.001) and indigenous cases (relative risk = 153.56, p < 0.001) occurred in the Pearl River Delta Area; while a secondary cluster of indigenous cases occurred in one district of the Chao Shan Area (relative risk = 471.25, p < 0.001). This study demonstrated that the geographic range of imported and indigenous Dengue Fever cases has expanded over recent years, and cases were significantly clustered in two heavily urbanised areas of Guangdong province. This provides the foundation for further investigation of risk factors and interventions in these high-risk areas.
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spatiotemporal analysis of indigenous and imported Dengue Fever cases in guangdong province china
Faculty of Health; Institute of Health and Biomedical Innovation, 2012Co-Authors: Wenwu Yin, Yonghui Zhang, Archie C A Clements, Gail M Williams, Shengjie Lai, Hang Zhou, Dan Zhao, Yansha Guo, Jinfeng Wang, Weizhong YangAbstract:Background Dengue Fever has been a major public health concern in China since it re-emerged in Guangdong province in 1978. This study aimed to explore spatiotemporal characteristics of Dengue Fever cases for both indigenous and imported cases during recent years in Guangdong province, so as to identify high-risk areas of the province and thereby help plan resource allocation for Dengue interventions. Methods Notifiable cases of Dengue Fever were collected from all 123 counties of Guangdong province from 2005 to 2010. Descriptive temporal and spatial analysis were conducted, including plotting of seasonal distribution of cases, and creating choropleth maps of cumulative incidence by county. The space-time scan statistic was used to determine space-time clusters of Dengue Fever cases at the county level, and a geographical information system was used to visualize the location of the clusters. Analysis were stratified by imported and indigenous origin. Results 1658 Dengue Fever cases were recorded in Guangdong province during the study period, including 94 imported cases and 1564 indigenous cases. Both imported and indigenous cases occurred more frequently in autumn. The areas affected by the indigenous and imported cases presented a geographically expanding trend over the study period. The results showed that the most likely cluster of imported cases (relative risk = 7.52, p < 0.001) and indigenous cases (relative risk = 153.56, p < 0.001) occurred in the Pearl River Delta Area; while a secondary cluster of indigenous cases occurred in one district of the Chao Shan Area (relative risk = 471.25, p < 0.001). Conclusions This study demonstrated that the geographic range of imported and indigenous Dengue Fever cases has expanded over recent years, and cases were significantly clustered in two heavily urbanised areas of Guangdong province. This provides the foundation for further investigation of risk factors and interventions in these high-risk areas.