The Application Of Spatial-temporal Analysis And Modeling Methods On Hemorrhagic Fever With Renal Syndrome

Hemorrhagic fever with renal syndrome(HFRS),is a kind of natural focal diseases,caused by Hantaviruses(HV)and has great damage to human health.The clinical symptoms for HFRS are fever,bleeding and renal dysfunction.HFRS is distributed in serveral countries.China is the most serious affected countries,it accounted for more than 90%of the world's cases.Hubei Province,however,continued to be the most seriously affected area over the past years.Since the first case of HFRS emergenced in 1957 in Hubei Province,HFRS epidemics were expanding and reached its highest point at 1983 with cases number of 23,943.During 1980 to 2009,the number of HFRS cases in Hubei Province reached 104,467 totally.The spreading of HFRS cases have great impact on social stability and human health.Taking proper measures to analyze the spreading and changing tendencies of HFRS in Hubei Province seems necessary.Geographic Information System,shorted for GIS,is a collection of tools with managing,analyzing and modelling for the spatial data.Using GIS tools,it is easily to realize the integrated management for HFRS and the relevant database.On one hand,it is possible to make qualitative and quantitative analysis to make a comprehensive investigation on the spatial and temporal pattern,spreading tendencies of HFRS.On the other hand,GIS spatial and temporal modelling technologies have great contributions to the understanding of epidemiology and ecological mechanism for HFRS.By correlation analysis with the related influencing factors,it is also helpful to make simulations and predictions for the spreading of HFRS.There are two main problems according to the current research for HFRS.First,lacking long term data for related study caused difficulties to reveal the spatial and temporal pattern comprehensively.Secondly,the absence of proper spatial and temporal analyzing method made the spatial and temporal simulation and the interpretation of multiple influencing factors hard to be executed.To solve these problems,this paper established a complete set of solutions for the analysis and prediction of HFRS spatial temporal distribution by combining spatial and temporal data analysis and modeling methods.Solutions provided in this paper will make contributions to avoid incomplete analysis of the characteristics and tendencies for HFRS.Nonetheless,this paper is focused on the above-mentioned problems in essence as the following basic aspects shows:(1)Understanding the direction and magnitude of spatial-temporal clustering situations on HFRS outbreaks in Hubei Province over the past decades is crucial in formulating research priorities and disease characteristic identification,as well as the corresponding changes in this region.(2)The primary characteristic identification on spatial-temporal heterogeneity of HFRS,especially seasonal difference,is also another novel point regarding infectious disease.The heterogeneity of HFRS based on the present study highlights that trend simulation and prediction of spatial-temporal sequences on HFRS cases should be focused on through GIS technology.(3)Finding the reasons and key factors that contributed to the HFRS outbreaks could assist the prediction and control of the HFRS outbreaks,the same as the relationships understanding between influencing factors and outbreaks of infectious diseases.Based on the problem-driven research method,spatial-temporal statistics,spatial-temporal modeling,inductive-deduction methodology were explored to identify the spatial and temporal distribution characteristics and predict the spread trend of HFRS outbreaks.This research mainly completed the following contents:(1)Identification on spatial-temporal characteristics of HFRS casesTo better demonstrate and predict the outbreak of HFRS in Hubei Province,the spatiotemporal pattern and clustering pattern were investigated in this chapter.Spatial global autocorrelation analysis was adopted to identify the overall spatial-temporal pattern of HFRS outbreaks.Clustering analysis and spatial-temporal scan statistical analysis were performed to further identify the changing trends of the clustering patterns of HFRS outbreak.(2)Spatial-temporal sequence trend prediction of HFRS outbreak casesThe main purpose of this chapter is to identify the further outbreak trend of HFRS and explore how those trends associate with the spatio-temporal pattern of the HFRS outbreaks.Considering the seasonal characteristic of HFRS,a new SD-STARMA(Seasonal Differenced-Space-Time Autoregressive Integrated Moving Average)model was developed in this chapter.SD-STARMA model was developed based on STARMA(Space-Time Autoregressive Integrated Moving Average)model.As a result,it inherited the spatial and temporal characteristics of STARMA(STARMA model inherits the spatial functions of Autoregressive Integrated Moving Average Model)model and this can simulate the tendencies of HFRS epidemics with the consideration of both spatial and temporal variations.However,SD-STARMA model integrated the seasonally distributed characteristics of HFRS epidemic and has a capability to make a seasonal difference calculation to eliminate the temporal non-stationary problem of HFRS data.The trends for each counties and cities in Hubei Province seemed much better simulated with SD-STARMA model,as well as scientific results appeared from this model.(3)Identification on influencing factors relationships of HFRS outbreak casesAs we mentioned before,HFRS is a specifically spatial distributed epidemic.In order to conduct a better analysis and acquire an accurate prediction of HFRS in Hubei Province,a new model named Seasonal Difference-Geographically and Temporally Weighted Regression(SD-GTWR)was constructed,which was developed based on Geographically and Temporally Weighted Regression(GTWR)model.The SD-GTWR model,which integrates the analysis and relationship of seasonal difference,spatial and temporal characteristics of HFRS(HFRS was characterized with spatiotemporal heterogeneity and it was seasonally distributed),was designed to illustrate the latent relationships between the spatial-temporal pattern of the HFRS epidemic and its influencing factors.Estimations have been made by different models such as OLS(Ordinary Least Squares)and GWR(Geographically Weighted Regression)GTWR and SD-GTWR.It can be inferred from the model diagnosis results that with the process of seasonal difference,SD-GTWR model is superior to traditional models such as GWR based models in terms of the efficiency and the ability of conducting influencing factor analysis.This model stressed on the correlations of the possible influencing factors to achieve a better prevention and control on these disease outbreaks.(4)Comprehensive analysis on HFRS diseasesThe findings in above research presented some new features regarding the influencing factors and trends for the HFRS outbreaks in Hubei Province.Another comprehensive analyze and summary was investigated on HFRS epidemiological characteristics,temporal-spatial distribution and clustering characteristics based on identification results of temporal-spatial features,construction and analyze of temporal-spatial models.While for an in-depth predict on the outbreak of HFRS in Hubei Province,influencing factors relationships and further trends were discussed with related epidemiological knowledge of HFRS.With a better understanding of disease characteristics and trends on HFRS,this study may provide a basis for the scientific prevention and control on HFRS and further help to improve the reducing cases of HFRS and a reference for the other infectious diseases.