Changes in Rodent Abundance and Weather Conditions Potentially Drive Hemorrhagic Fever with Renal Syndrome Outbreaks in Xi'an, China, 2005-2012

作者:Tian, HY (Tian, Huai-Yu)[ 1 ] ; Yu, PB (Yu, Peng-Bo)[ 2 ] ; Luis, AD (Luis, Angela D.)[ 3,4,5 ] ; Bi, P (Bi, Peng)[ 6 ] ; Cazelles, B (Cazelles, Bernard)[ 7,8 ] ; Laine, M (Laine, Marko)[ 9 ] ; Huang, SQ (Huang, Shan-Qian)[ 1 ] ; Ma, CF (Ma, Chao-Feng)[ 10 ] ; Zhou, S (Zhou, Sen)[ 11 ] ; Wei, J (Wei, Jing)[ 2 ] ; Li, S (Li, Shen)[ 2 ] ; Lu, XL (Lu, Xiao-Ling)[ 12 ] ; Qu, JH (Qu, Jian-Hui)[ 12 ] ; Dong, JH (Dong, Jian-Hua)[ 2 ] ; Tong, SL (Tong, Shi-Lu)[ 13,14 ] ; Wang, JJ (Wang, Jing-Jun)[ 2 ] ; Grenfell, B (Grenfell, Bryan)[ 4,5 ] ; Xu, B (Xu, Bing)[ 1,11 ] 

PLOS NEGLECTED TROPICAL DISEASES

卷: 9  期: 3

文献号: e0003530

DOI: 10.1371/journal.pntd.0003530

出版年: MAR 2015

摘要

Background 

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. 

Methods and Findings 

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. 

Conclusions 

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.

通讯作者地址: Tian, HY (通讯作者)

Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Shaanxi Prov Ctr Dis Control & Prevent, Xian, Shaanxi, Peoples R China

[ 3 ] Univ Montana, Dept Ecosyst & Conservat Sci, Missoula, MT 59812 USA

[ 4 ] Princeton Univ, Dept Ecol & Evolut Biol, Princeton, NJ 08544 USA

[ 5 ] NIH, Fogarty Int Ctr, Bethesda, MD 20892 USA

[ 6 ] Univ Adelaide, Discipline Publ Hlth, Adelaide, SA, Australia

[ 7 ] UPMC, IRD, UMI 209, UMMISCO, F-93142 Bondy, France

[ 8 ] ENS, IBENS,UMR 8197, Ecoevolutionary Math, F-75230 Paris 05, France

[ 9 ] Finnish Meteorol Inst, FIN-00101 Helsinki, Finland

[ 10 ] Xian Ctr Dis Control & Prevent, Xian, Shaanxi, Peoples R China

[ 11 ] Tsinghua Univ, Ctr Earth Syst Sci, Minist Educ, Key Lab Earth Syst Modelling, Beijing 100084, Peoples R China

[ 12 ] Hu Cty Ctr Dis Control & Prevent Shaanxi Prov, Xian, Shaanxi, Peoples R China

[ 13 ] Queensland Univ Technol, Sch Publ Hlth, Brisbane, Qld 4001, Australia

[ 14 ] Queensland Univ Technol, Inst Hlth & Biomed Innovat, Brisbane, Qld 4001, Australia