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Changes in Rodent Abundance and Weather Conditions Potentially Drive Hemorrhagic Fever with Renal Syndrome Outbreaks in Xi’an,China,2005–2012
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نویسنده
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tian h.-y. ,yu p.-b. ,luis a.d. ,bi p. ,cazelles b. ,laine m. ,huang s.-q. ,ma c.-f. ,zhou s. ,wei j. ,li s. ,lu x.-l. ,qu j.-h. ,dong j.-h. ,tong s.-l. ,wang j.-j. ,grenfell b. ,xu b.
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منبع
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plos neglected tropical diseases - 2015 - دوره : 9 - شماره : 3
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چکیده
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Increased risks for hemorrhagic fever with renal syndrome (hfrs) caused by hantaan virus have been observed since 2005,in xi’an,china. despite increased vigilance and preparedness,hfrs outbreaks in 2010,2011,and 2012 were larger than ever,with a total of 3,938 confirmed hfrs cases and 88 deaths in 2010 and 2011. data on hfrs cases and weather were collected monthly from 2005 to 2012,along with active rodent monitoring. wavelet analyses were performed to assess the temporal relationship between hfrs incidence,rodent density and climatic factors over the study period. results showed that hfrs cases correlated to rodent density,rainfall,and temperature with 2,3 and 4-month lags,respectively. using a bayesian time-series poisson adjusted model,we fitted the hfrs outbreaks among humans for risk assessment in xi’an. the best models included seasonality,autocorrelation,rodent density 2 months previously,and rainfall 2 to 3 months previously. our models well reflected the epidemic characteristics by one step ahead prediction,out-of-sample. in addition to a strong seasonal pattern,hfrs incidence was correlated with rodent density and rainfall,indicating that they potentially drive the hfrs outbreaks. future work should aim to determine the mechanism underlying the seasonal pattern and autocorrelation. however,this model can be useful in risk management to provide early warning of potential outbreaks of this disease.
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آدرس
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state key laboratory of remote sensing science,college of global change and earth system science,beijing normal university,beijing, China, shaanxi provincial centre for disease control and prevention,xi’an,shaanxi, China, department of ecosystem and conservation sciences,university of montana,missoula,montana,united states,department of ecology and evolutionary biology,princeton university,princeton,nj,united states,fogarty international center,national institutes of health,bethesda,md, United States, discipline of public health,university of adelaide,adelaide, Australia, ummisco,umi 209 ird—upmc,bondy,93142,france,eco-evolutionary mathematic,ibens umr 8197,ens,paris, France, finnish meteorological institute,helsinki, Finland, state key laboratory of remote sensing science,college of global change and earth system science,beijing normal university,beijing, China, xi’an centre for disease control and prevention,xi’an,shaanxi, China, ministry of education key laboratory for earth system modelling,center for earth system science,tsinghua university,beijing, China, shaanxi provincial centre for disease control and prevention,xi’an,shaanxi, China, shaanxi provincial centre for disease control and prevention,xi’an,shaanxi, China, hu county centre for disease control and prevention of shaanxi province,xi’an,shaanxi, China, hu county centre for disease control and prevention of shaanxi province,xi’an,shaanxi, China, shaanxi provincial centre for disease control and prevention,xi’an,shaanxi, China, school of public health and institute of health and biomedical innovation,queensland university of technology,brisbane,qld, Australia, shaanxi provincial centre for disease control and prevention,xi’an,shaanxi, China, department of ecology and evolutionary biology,princeton university,princeton,nj,united states,fogarty international center,national institutes of health,bethesda,md, United States, state key laboratory of remote sensing science,college of global change and earth system science,beijing normal university,beijing,china,ministry of education key laboratory for earth system modelling,center for earth system science,tsinghua university,beijing, China
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Authors
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