Scrub typhus is a significant public health issue with a wide distribution and is influenced by various determinants.However,in order to effectively eradicate scrub typhus,it is crucial to identify the specific factor...Scrub typhus is a significant public health issue with a wide distribution and is influenced by various determinants.However,in order to effectively eradicate scrub typhus,it is crucial to identify the specific factors that contribute to its incidence at a detailed level.Therefore,the objective of our study is to identify these influencing factors,examine the spatial variations in incidence,and analyze the interplay of two factors on scrub typhus incidence,so as to provide valuable experience for the prevention and treatment of scrub typhus in Gannan and to alleviate the economic burden of the local population.This study employed spatial autocorrelation analyses to examine the dependent variable and ordinary least squares model residuals.Additionally,spatial regression modelling and geographical detector were used to analyze the factors influencing the annual mean 14-year incidence of scrub typhus in the streets/townships of Gannan region from 2008 to 2021.The results of spatial1 autocorrelation analyses indicated the presence of spatial correlation.Among the global spatial regression models,the spatial lag model was found to be the best fitting model(log likelihood ratio?319.3029,AIC?666.6059).The results from the SLM analysis indicated that DEM,mean temperature,and mean wind speed were the primary factors influencing the occurrence of scrub typhus.For the local spatial regression models,the multiscale geographically weighted regression was determined to be the best fitting model(adjusted R2?0.443,AICc?726.489).Further analysis using the MGWR model revealed that DEM had a greater impact in Xinfeng and Longnan,while the southern region was found to be more susceptible to scrub typhus due to mean wind speed.The geographical detector results revealed that the incidence of scrub typhus was primarily influenced by annual average normalized difference vegetation index.Additionally,the interaction between GDP and the percentage of grassland area had a significant impact on the incidence of scrub typhus(q?0.357).This study illustrated the individual and interactive effects of natural environmental factors and socio-economic factors on the incidence of scrub typhus;and elucidated the specific factors affecting the incidence of scrub typhus in various streets/townships.The findings of this study can be used to develop effective interventions for the prevention and control of scrub typhus.展开更多
Scrub typhus poses a serious public health risk globally.Forecasting the occurrence of the disease is essential for policymakers to develop prevention and control strategies.This study investigated the application of ...Scrub typhus poses a serious public health risk globally.Forecasting the occurrence of the disease is essential for policymakers to develop prevention and control strategies.This study investigated the application of modelling techniques to predict the occurrence of scrub typhus and establishes an early warning system aimed at providing a foundational reference for its effective prevention and control.In this study,the monthly occurrence of scrub typhus in Ganzhou City from January 2008 to December 2022 was utilized as the training set for the first part of the analysis,while the data from January 2008 to December 2019 served as the training set for the second part.Based1 on these data,the SARIMA model,the BPNN model,and the combined SARIMA-BPNN model were developed and validated using data from January to December 2023.The most effective model was then selected to predict the number of occurrences of scrub typhus for the years 2024 and 2025,respectively.The root mean square error(RMSE)and mean absolute error(MAE)of the BPNN(3-9-1)model,developed using data from January 2008 to December 2022,were 8.472 and 6.4,respectively.In contrast,the RMSE and MAE of the combined SARIMA-BPNN(1-9-1)model,constructed using data from January 2008 to December 2019,were 19.361 and 16.178,respectively.In addition,the BPNN(3-9-1)model predicted 284 cases of scrub typhus in Ganzhou City for 2024,and 163 cases for 2025.The BPNN(3-9-1)model demonstrated strong applicability in predicting the monthly occurrence of scrub typhus.Furthermore,incorporating three years of data on the occurrence of new crown outbreaks when developing a predictive model for infectious diseases can substantially enhance prediction accuracy.展开更多
基金provided by the Science and Technology Program of Jiangxi Provincial Health andWellness Commission(Grant No.SKJP220226866).
文摘Scrub typhus is a significant public health issue with a wide distribution and is influenced by various determinants.However,in order to effectively eradicate scrub typhus,it is crucial to identify the specific factors that contribute to its incidence at a detailed level.Therefore,the objective of our study is to identify these influencing factors,examine the spatial variations in incidence,and analyze the interplay of two factors on scrub typhus incidence,so as to provide valuable experience for the prevention and treatment of scrub typhus in Gannan and to alleviate the economic burden of the local population.This study employed spatial autocorrelation analyses to examine the dependent variable and ordinary least squares model residuals.Additionally,spatial regression modelling and geographical detector were used to analyze the factors influencing the annual mean 14-year incidence of scrub typhus in the streets/townships of Gannan region from 2008 to 2021.The results of spatial1 autocorrelation analyses indicated the presence of spatial correlation.Among the global spatial regression models,the spatial lag model was found to be the best fitting model(log likelihood ratio?319.3029,AIC?666.6059).The results from the SLM analysis indicated that DEM,mean temperature,and mean wind speed were the primary factors influencing the occurrence of scrub typhus.For the local spatial regression models,the multiscale geographically weighted regression was determined to be the best fitting model(adjusted R2?0.443,AICc?726.489).Further analysis using the MGWR model revealed that DEM had a greater impact in Xinfeng and Longnan,while the southern region was found to be more susceptible to scrub typhus due to mean wind speed.The geographical detector results revealed that the incidence of scrub typhus was primarily influenced by annual average normalized difference vegetation index.Additionally,the interaction between GDP and the percentage of grassland area had a significant impact on the incidence of scrub typhus(q?0.357).This study illustrated the individual and interactive effects of natural environmental factors and socio-economic factors on the incidence of scrub typhus;and elucidated the specific factors affecting the incidence of scrub typhus in various streets/townships.The findings of this study can be used to develop effective interventions for the prevention and control of scrub typhus.
基金Funding for this research was provided by the Science and Technology Program of Jiangxi Provincial Health and Wellness Commission(202312001).
文摘Scrub typhus poses a serious public health risk globally.Forecasting the occurrence of the disease is essential for policymakers to develop prevention and control strategies.This study investigated the application of modelling techniques to predict the occurrence of scrub typhus and establishes an early warning system aimed at providing a foundational reference for its effective prevention and control.In this study,the monthly occurrence of scrub typhus in Ganzhou City from January 2008 to December 2022 was utilized as the training set for the first part of the analysis,while the data from January 2008 to December 2019 served as the training set for the second part.Based1 on these data,the SARIMA model,the BPNN model,and the combined SARIMA-BPNN model were developed and validated using data from January to December 2023.The most effective model was then selected to predict the number of occurrences of scrub typhus for the years 2024 and 2025,respectively.The root mean square error(RMSE)and mean absolute error(MAE)of the BPNN(3-9-1)model,developed using data from January 2008 to December 2022,were 8.472 and 6.4,respectively.In contrast,the RMSE and MAE of the combined SARIMA-BPNN(1-9-1)model,constructed using data from January 2008 to December 2019,were 19.361 and 16.178,respectively.In addition,the BPNN(3-9-1)model predicted 284 cases of scrub typhus in Ganzhou City for 2024,and 163 cases for 2025.The BPNN(3-9-1)model demonstrated strong applicability in predicting the monthly occurrence of scrub typhus.Furthermore,incorporating three years of data on the occurrence of new crown outbreaks when developing a predictive model for infectious diseases can substantially enhance prediction accuracy.
