Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB ...Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering.Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive(ST-CAR)model.Results Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000in 2019.Spatial hotspots were found in northeastern Guangdong,particularly in Heyuan,Shanwei,and Shantou,while Shenzhen,Dongguan,and Foshan had the lowest rates in the Pearl River Delta.The STCAR model showed that the TB risk was lower with higher per capita Gross Domestic Product(GDP)[Relative Risk(RR),0.91;95%Confidence Interval(CI):0.86–0.98],more the ratio of licensed physicians and physician(RR,0.94;95%CI:0.90-0.98),and higher per capita public expenditure(RR,0.94;95%CI:0.90–0.97),with a marginal effect of population density(RR,0.86;95%CI:0.86–1.00).Conclusion The incidence of TB in Guangdong varies spatially and temporally.Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection.Strategies focusing on equitable health resource distribution and economic development are the key to TB control.展开更多
This paper presents an investigation into the spatio-temporal dynamics of Severe Acute Respiratory Syndrome(SARS)across the diverse health regions of Brazil from 2016 to 2024.Leveraging extensive datasets that include...This paper presents an investigation into the spatio-temporal dynamics of Severe Acute Respiratory Syndrome(SARS)across the diverse health regions of Brazil from 2016 to 2024.Leveraging extensive datasets that include SARS cases,climate data,hospitalization records,and COVID-19 vaccination information,our study employs a Bayesian spatio-temporal generalized linear model to capture the intricate dependencies inherent in the dataset.The analysis reveals significant variations in the incidence of SARS cases over time,particularly during and between the distinct eras of pre-COVID-19,during,and post-COVID-19.Our modeling approach accommodates explanatory variables such as humidity,temperature,and COVID-19 vaccine doses,providing a comprehensive understanding of the factors influencing SARS dynamics.Our modeling revealed unique temporal trends in SARS cases for each region,resembling neighborhood patterns.Low temperature and high humidity were linked to decreased cases,while in the COVID-19 era,temperature and vaccination coverage played significant roles.The findings contribute valuable insights into the spatial and temporal patterns of SARS in Brazil,offering a foundation for targeted public health interventions and preparedness strategies.展开更多
As the speed of optical access networks soars with ever increasing multiple services, the service-supporting ability of optical access networks suffers greatly from the shortage of service awareness. Aiming to solve t...As the speed of optical access networks soars with ever increasing multiple services, the service-supporting ability of optical access networks suffers greatly from the shortage of service awareness. Aiming to solve this problem, a hierarchy Bayesian model based services awareness mechanism is proposed for high-speed optical access networks. This approach builds a so-called hierarchy Bayesian model, according to the structure of typical optical access networks. Moreover, the proposed scheme is able to conduct simple services awareness operation in each optical network unit(ONU) and to perform complex services awareness from the whole view of system in optical line terminal(OLT). Simulation results show that the proposed scheme is able to achieve better quality of services(Qo S), in terms of packet loss rate and time delay.展开更多
基金supported by the Guangdong Provincial Clinical Research Center for Tuberculosis(No.2020B1111170014)。
文摘Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering.Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive(ST-CAR)model.Results Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000in 2019.Spatial hotspots were found in northeastern Guangdong,particularly in Heyuan,Shanwei,and Shantou,while Shenzhen,Dongguan,and Foshan had the lowest rates in the Pearl River Delta.The STCAR model showed that the TB risk was lower with higher per capita Gross Domestic Product(GDP)[Relative Risk(RR),0.91;95%Confidence Interval(CI):0.86–0.98],more the ratio of licensed physicians and physician(RR,0.94;95%CI:0.90-0.98),and higher per capita public expenditure(RR,0.94;95%CI:0.90–0.97),with a marginal effect of population density(RR,0.86;95%CI:0.86–1.00).Conclusion The incidence of TB in Guangdong varies spatially and temporally.Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection.Strategies focusing on equitable health resource distribution and economic development are the key to TB control.
文摘This paper presents an investigation into the spatio-temporal dynamics of Severe Acute Respiratory Syndrome(SARS)across the diverse health regions of Brazil from 2016 to 2024.Leveraging extensive datasets that include SARS cases,climate data,hospitalization records,and COVID-19 vaccination information,our study employs a Bayesian spatio-temporal generalized linear model to capture the intricate dependencies inherent in the dataset.The analysis reveals significant variations in the incidence of SARS cases over time,particularly during and between the distinct eras of pre-COVID-19,during,and post-COVID-19.Our modeling approach accommodates explanatory variables such as humidity,temperature,and COVID-19 vaccine doses,providing a comprehensive understanding of the factors influencing SARS dynamics.Our modeling revealed unique temporal trends in SARS cases for each region,resembling neighborhood patterns.Low temperature and high humidity were linked to decreased cases,while in the COVID-19 era,temperature and vaccination coverage played significant roles.The findings contribute valuable insights into the spatial and temporal patterns of SARS in Brazil,offering a foundation for targeted public health interventions and preparedness strategies.
基金supported by the Science and Technology Project of State Grid Corporation of China:"Research on the Power-Grid Services Oriented"IP+Optics"Coordination Choreography Technology"
文摘As the speed of optical access networks soars with ever increasing multiple services, the service-supporting ability of optical access networks suffers greatly from the shortage of service awareness. Aiming to solve this problem, a hierarchy Bayesian model based services awareness mechanism is proposed for high-speed optical access networks. This approach builds a so-called hierarchy Bayesian model, according to the structure of typical optical access networks. Moreover, the proposed scheme is able to conduct simple services awareness operation in each optical network unit(ONU) and to perform complex services awareness from the whole view of system in optical line terminal(OLT). Simulation results show that the proposed scheme is able to achieve better quality of services(Qo S), in terms of packet loss rate and time delay.
