Study On Spatiotemporal Characteristics And Prediction Of Incidence Trend Of Foodborne Diseases In Jiangxi Province Based On Monitoring Hospital-reported Cases

Objective:Drawing upon the foodborne disease surveillance system,this study aimed to analyze and investigate reported cases of foodborne diseases from over 2091 sentinel hospitals in Jiangxi Province,with the intention of gaining a comprehensive insight into the pathogenesis and epidemiological characteristics of these diseases within the province.Additionally,the research explored spatial aggregation as well as high incidence rates of foodborne diseases.A prediction model for foodborne diseases was then developed by leveraging both ARIMA and BP neural network models to serve as a valuable reference for designing appropriate prevention and control measures tailored to the specific needs of Jiangxi Province.Methods:This study employed a variety of methodologies to investigate foodborne diseases in Jiangxi Province between 2016 and 2021,with the aim of acquiring demographic,temporal,and geographic distribution characteristics of foodborne diseases in this region using descriptive statistical analysis.The researchers then leveraged Geo Da software for spatial aggregation analysis,Sa TScan spatio-temporal scanning software to explore spatio-temporal distribution characteristics,and combined Arc GIS software for incidence map visualization of foodborne diseases.Finally,through the use of Python programming,an ARIMA model and a BPNN model were created and compared to identify the optimal prediction model for the future epidemic trends of foodborne diseases.Results:From 2016 to 2021,a total of 73,705 cases and 20 deaths due to foodborne diseases were reported in Jiangxi Province,corresponding to an average annual incidence rate of 27.24 per 100,000 people.The lowest incidence rate was recorded at24.65 per 100,000 people in 2016,while the highest incidence rate of 28.94 was observed in 2018.Out of the total reported cases,36,239（49.17%）were male,while37,466（50.83%）were female.The age group with the highest cumulative number of reported cases was children aged 0 to 5 years old,accounting for 19.35%of the overall cases.The age group over 65 years old accounted for 14.95%of the total cases,with farmers,scattered children,and students identified as high-risk groups,contributing to35.24%,15.54%,and 12.36%of The overall number of cases,separately.Additionally,foodborne diseases exhibited a seasonal pattern,with the highest incidence rates occurring during the summer and autumn months,especially in August.The spatial distribution of foodborne diseases in Jiangxi Province between 2017and 2020 exhibited significant global spatial autocorrelation（P<0.05）.Local spatial autocorrelation analysis identified 88 aggregation regions,comprising 31 high-high aggregation regions,35 low-low aggregation regions,14 low-high aggregation regions,and 8 high-low aggregation regions.High-high clustering was predominantly concentrated in northwest Jiangxi Province,while low-low clustering regions were mainly observed in central Jiangxi Province and central Ganzhou City.Hot spot analysis revealed 41 primary hot spots,mainly concentrated in Jiujiang City in northwest Jiangxi Province and Ganzhou City in southwest Jiangxi Province,with an additional 10 cold spots predominantly located in the middle of Jiangxi Province.Furthermore,spatio-temporal scanning statistical analysis identified 32 counties in six cluster areas,with the first-class cluster area（RR=1.77,P<0.001）encompassing 15counties,including Ruichang City,Chaisang District,Lianxi District,Xunyang District,De’an County,Lushan City,Gongqing City,Hukou County,Duchang County,Wuning County,Yongxiu County,Pengze County,Anyi County,Jing’an County,and Xinjian District.Based on the analysis,the optimal ARIMA model was found to be ARIMA（2,1,1）（0,1,1）₁₂,while the 12-8-1 BP neural network model was identified as the optimal neural network model.The ARIMA model exhibited superior predictive performance compared to the BP neural network model.Notably,the combined model,particularly the error-corrected combined model,demonstrated superior predictive accuracy,with an MSE of 0.097,RMSE of 0.311,and MAPE of 10.97%.The comparison of predictive efficacy ranked the error correction combination model highest,followed by the MAPE weight combination model,the dominance matrix weight combination model,the ARIMA model,and finally the BPNN model.Moreover,the predicted results for the next three years suggest that the monthly incidence of reported foodborne diseases in Jiangxi Province will remain stable.Conclusion:Foodborne diseases in Jiangxi Province display a seasonal pattern,with August of every year being identified as the peak period of occurrence.To address this seasonality,effective measures must be taken.Vulnerable populations to foodborne diseases are children aged 0-5 years and older adults aged 65 years or above.Greater promotion and education efforts should be implemented in communities,schools,and other channels to heighten public awareness and prevention.Moreover,special attention should be paid to farmers and scattered children,who exhibit high incidence of foodborne diseases.It is important to reinforce science popularization and education of this group in order to improve their food safety awareness and self-protection capabilities.Additionally,the northwestern region of Jiangxi Province and the southwestern region of Ganzhou City are key areas for preventing and controlling foodborne diseases.Scientific allocation and utilization of health resources,stronger supervision,implementation of monitoring and reporting systems,and timely detection and management of foodborne disease outbreaks are necessary.Combination models can enhance foodborne disease prediction accuracy,wherein the error correction-based combination model shows the best results.While the predicted trends suggest that the future incidence of foodborne diseases in Jiangxi Province will remain stable,it is vital to maintain high levels of attention and vigilance,boost scientific research and exploration,and continue to improve the effectiveness of preventative and control measures to further reduce the risks of occurrence and transmission of foodborne diseases.