Changsha was one of the most affected areas during the 2009 A(H1N1)influenza pandemic in China.Here,we analyze the spatial–temporal dynamics of the 2009 pandemic across Changsha municipal districts,evaluate the relat...Changsha was one of the most affected areas during the 2009 A(H1N1)influenza pandemic in China.Here,we analyze the spatial–temporal dynamics of the 2009 pandemic across Changsha municipal districts,evaluate the relationship between case incidence and the local urban spatial structure and predict high-risk areas of influenza A(H1N1).We obtained epidemiological data on all cases of influenza A(H1N1)reported across municipal districts in Changsha during period May 2009–December 2010 and data on population density and basic geographic characteristics for 239 primary schools,97 middle schools,347 universities,96 malls and markets,674 business districts and 121 hospitals.Spatial–temporal K functions,proximity models and logistic regression were used to analyze the spatial distribution pattern of influenza A(H1N1)incidence and the association between influenza A(H1N1)cases and spatial risk factors and predict the infection risks.We found that the 2009 influenza A(H1N1)was driven by a transmission wave from the center of the study area to surrounding areas and reported cases increased significantly after September 2009.We also found that the distribution of influenza A(H1N1)cases was associated with population density and the presence of nearest public places,especially universities(OR=10.166).The final predictive risk map based on the multivariate logistic analysis showed high-risk areas concentrated in the center areas of the study area associated with high population density.Our findings support the identification of spatial risk factors and highrisk areas to guide the prioritization of preventive and mitigation efforts against future influenza pandemics.展开更多
Influenza A (H1N1) was spread widely between cities and towns by road traffic and had a major impact on public health in China in 2009. Understanding regulation of its transmission is of great significance with urbani...Influenza A (H1N1) was spread widely between cities and towns by road traffic and had a major impact on public health in China in 2009. Understanding regulation of its transmission is of great significance with urbanization ongoing and for mitigation of damage by the epidemic. We analyzed influenza A (H1N1) spatiotemporal transmission and risk factors along roads in Changsha, and combined diffusion velocity and floating population size to construct an epidemic diffusion model to simulate its transmission between cities and towns. The results showed that areas along the highways and road intersections had a higher incidence rate than other areas. Expressways and county roads played an important role in the rapid development stage and the epidemic peak, respectively, and intercity bus stations showed a high risk of disease transmission. The model simulates the intensity and center of disease outbreaks in cities and towns, and provides a more complete simulation of the disease spatiotemporal process than other models.展开更多
基金supported by the Key Discipline Construction Project in Hunan Province(2008001)the National Natural Science Foundation of China and the Scientific Research Fund of Hunan Provincial Education Department(13A051)
文摘Changsha was one of the most affected areas during the 2009 A(H1N1)influenza pandemic in China.Here,we analyze the spatial–temporal dynamics of the 2009 pandemic across Changsha municipal districts,evaluate the relationship between case incidence and the local urban spatial structure and predict high-risk areas of influenza A(H1N1).We obtained epidemiological data on all cases of influenza A(H1N1)reported across municipal districts in Changsha during period May 2009–December 2010 and data on population density and basic geographic characteristics for 239 primary schools,97 middle schools,347 universities,96 malls and markets,674 business districts and 121 hospitals.Spatial–temporal K functions,proximity models and logistic regression were used to analyze the spatial distribution pattern of influenza A(H1N1)incidence and the association between influenza A(H1N1)cases and spatial risk factors and predict the infection risks.We found that the 2009 influenza A(H1N1)was driven by a transmission wave from the center of the study area to surrounding areas and reported cases increased significantly after September 2009.We also found that the distribution of influenza A(H1N1)cases was associated with population density and the presence of nearest public places,especially universities(OR=10.166).The final predictive risk map based on the multivariate logistic analysis showed high-risk areas concentrated in the center areas of the study area associated with high population density.Our findings support the identification of spatial risk factors and highrisk areas to guide the prioritization of preventive and mitigation efforts against future influenza pandemics.
基金supported by the Key Discipline Construction Project in Hunan Province (2008001)the Science and Technology Planning Project of Hunan Province, China (2010SK3007)
文摘Influenza A (H1N1) was spread widely between cities and towns by road traffic and had a major impact on public health in China in 2009. Understanding regulation of its transmission is of great significance with urbanization ongoing and for mitigation of damage by the epidemic. We analyzed influenza A (H1N1) spatiotemporal transmission and risk factors along roads in Changsha, and combined diffusion velocity and floating population size to construct an epidemic diffusion model to simulate its transmission between cities and towns. The results showed that areas along the highways and road intersections had a higher incidence rate than other areas. Expressways and county roads played an important role in the rapid development stage and the epidemic peak, respectively, and intercity bus stations showed a high risk of disease transmission. The model simulates the intensity and center of disease outbreaks in cities and towns, and provides a more complete simulation of the disease spatiotemporal process than other models.