›› 2019, Vol. 39 ›› Issue (2): 187-.doi: 10.3969/j.issn.1674-8115.2019.02.015

• Original article (Public health) • Previous Articles     Next Articles

Spatial epidemiological characteristics and prediction models of bacterial dysentery in Chongqing 2009 to 2016 based on meteorological elements

LIU Xun1, MENG Qiu-yu1, XIE Jia-jia1, XIAO DA-yong2, WANG Yi1, DENG Dan1   

  1. 1. School of Public Health and Management, Research Center for Medicine and Social Development, Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing 400016, China; 2. Institute for Prevention and Control of Endemic and Parasitic Diseases, Chongqing Center for Disease Control and Prevention, Chongqing 400042, China
  • Online:2019-02-28 Published:2019-03-19
  • Supported by:
    Basic Research and Frontier Exploration Project in Chongqing, cstc2018jcyjA0135; Medical Research Project of Chongqing Health and Family Planning Commission in 2015, 2015MSXM094

Abstract: Objective · To analyze the spatial epidemiological characteristics of bacillary dysentery and its correlation with meteorological elements in Chongqing, and to construct its incidence prediction model, thus providing scientific basis for the prevention and control of bacterial dysentery. Methods · The data of bacterial dysentery cases and meteorological factors 2009 to 2016 in Chongqing was collected in this study. Descriptive methods were employed to investigate the epidemiological distribution of bacillary dysentery. Spatiotemporal scanning statistics was used to analyze spatiotemporal characteristics of bacillary dysentery. DCCA coefficient method was used to quantify the correlation between the incidence of bacillary dysentery and meteorological elements. Both Boruta algorithm and particle swarm optimization algorithm (PSO) combined with support vector machine for regression model (SVR) were used to establish the prediction model for the incidence of bacterial dysentery. Results · ① The mean annual reported incidence of bacillary dysentery in Chongqing 2009 to 2016 was 29.394/100 000. Children <5 years old had the highest incidence (295.892/100 000) among all age categories and scattered children had the highest proportion (50.335%) among all occupation categories. The seasonal incidence peak was May to October. Bacterial dysentery showed a significant spatial-temporal aggregation that the most likely clusters for disease was found mainly in the main urban areas and main gathering time was June to October. ② The most important meteorological elements associated with the incidence of bacterial dysentery were monthly mean atmospheric pressure (ρDCCA -0.918), monthly mean maximum temperature (ρDCCA0.875) and monthly mean temperature (ρDCCA0.870). ③ The mean squared error (MSE), mean absolute percentage error (MAPE) and square correlation coefficient (R2) of PSO_SVR model constructed based on meteorological elements were 0.055, 0.101 and 0.909, respectively. Conclusion · The main urban areas of Chongqing and the northeast of Chongqing should be regarded as the key areas for the prevention and control of bacillary dysentery. At the same time, according to the characteristics of bacillary dysentery, relevant health departments should take targeted measures to control the spread and prevalence of bacillary dysentery among children <5 years old, scattered children and farmers. The PSO_SVR model constructed based on meteorological elements has good predictive performance and can provide scientific theoretical support for the prevention and control of bacterial dysentery.

Key words: bacterial dysentery, spatial epidemiology, DCCA coefficient method, Boruta algorithm, particle swarm optimization algorithm, support vector machine regression

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