Detecting The Association Between Hemorrhagic Fever With Renal Syndrome And Animal Epidemic Situation,Meteorological Factors Based On Panel Data Model

Objective: To investigate the relationship between the incidence of Hemorrhagic fever with renal syndrome and the host’s epidemic and meteorology using a panel data model,and to explore the application value of panel model in infectious disease monitoring data analysis.Methods: From January 2008 to December 2016,the monthly incidence data of hemorrhagic fever with renal syndrome of various provinces were obtained from the China Public Health Science Data Center.From January 2008 to December 2016,the monthly meteorological data of various provinces were obtained from China Meteorological Data Network.We found out the monthly incidence rate of the area and the host epidemic situation data through the website of CNKI,Wanfang Data,and Duxiu.The spatial auto-correlation analysis of the incidence of HFRS was performed using global spatial auto-correlation(GISA)and local spatial auto-correlation(LISA).Three traditional panel models: mixed model,fixed effect model,and random effect model were used to simulate the incidence and meteorological data as well as host epidemic data.The F-test and Hausman test were used to select the model.We use ArcGIS 10.1 to build a geographic information database,and perform spatial auto-correlation analysis of related data in GeoDa 095-i software,and use STATA 14 to implement the panel data model.Results: 1.Spatial auto-correlation analysis: From 2008 to 2016,only the annual incidence rates of the provinces were clustered in 2008,2009,2014,and 2016,and the global indicators of spatial auto-correlation coefficient was statistically significant(P<0.01),and randomly distributed in other years.The local indicators of spatial auto-correlation coefficient shows that Northeast China are still the provinces with high outbreaks,and Shaanxi Province has been in the "high-low" region since 2011.2.Panel data analysis:(1)Panel models of incidence rates in various provinces of mainland China.The results suggest that the monthly average atmospheric pressure month,average temperature,monthly average vapor pressure,monthly average relative humidity,and monthly hours sunshine has a certain effect with Monthly incidence of HFRS byone-month lag(P <0.01).After adding rat density and rat poisoning rate to the explanatory variables,the results suggest that the rate of house rat poisoning is positively correlated with the monthly incidence of HFRS(r = 0.016,P <0.01).This study divides31 provinces in mainland China into four regions according to their climate distribution.In the subtropical monsoon climate area,the model suggests that the monthly average temperature,monthly average vapor pressure,monthly average wind speed,monthly average relative humidity,and monthly sunshine hours in the meteorological factors have certain effects on the monthly incidence of HFRS(P <0.01).In the temperate monsoon area,the simulation results show that the monthly average temperature and monthly vapor pressure in the meteorological factors have a certain effect on the monthly incidence of HFRS(P <0.01).In the temperate continental climate area,the simulation results suggest that the monthly average temperature,monthly average vapor pressure,and monthly average relative humidity in the meteorological factors have a certain effect on the monthly incidence of HFRS(P <0.01).In the plateau climate area,the results suggest that the monthly average atmospheric pressure,monthly average vapor pressure,monthly precipitation,monthly average wind speed,and monthly average relative humidity are related to the monthly incidence of HFRS(P <0.01).(2)Panel data analysis of the incidence of HFRS in Shenyang,Huludao,Dandong,and Yingkou of Liaoning Province,the results suggest that the monthly incidence of HFRS is related to the average monthly vapor pressure of the same period(P <0.01);Panel data analysis of the incidence of HFRS in Qingdao,Weifang,and Zibo of Shandong Province shows that the monthly incidence rates are correlated with monthly average atmospheric pressure,average temperature,and monthly average vapor pressure over the same period(P <0.01);Panel data analysis of Changsha City,Chenzhou City,Hunan Province,the results suggest that the monthly incidence rate is related to the average temperature and monthly average vapor pressure(P <0.01).Conclusion: The panel data model considers time and individual effects more comprehensively,makes full use of information in various regions,and performs data analysis more accurately.The panel model has good application value in the analysis of infectious disease surveillance data.