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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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dc.contributor.authorTian, H.
dc.contributor.authorYu, P.
dc.contributor.authorLuis, A.
dc.contributor.authorBi, P.
dc.contributor.authorCazelles, B.
dc.contributor.authorLaine, M.
dc.contributor.authorHuang, S.
dc.contributor.authorMa, C.
dc.contributor.authorZhou, S.
dc.contributor.authorWei, J.
dc.contributor.authorLi, S.
dc.contributor.authorLu, X.
dc.contributor.authorQu, J.
dc.contributor.authorDong, J.
dc.contributor.authorTong, S.
dc.contributor.authorWang, J.
dc.contributor.authorGrenfell, B.
dc.contributor.authorXu, B.
dc.contributor.editorScarpino, S.V.
dc.date.issued2015
dc.description.abstractBACKGROUND: 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.
dc.description.statementofresponsibilityHuai-Yu Tian, Peng-Bo Yu, Angela D. Luis, Peng Bi, Bernard Cazelles, Marko Laine, Shan-Qian Huang, Chao-Feng Ma, Sen Zhou, Jing Wei, Shen Li, Xiao-Ling Lu, Jian-Hui Qu, Jian-Hua Dong, Shi-Lu Tong, Jing-Jun Wang, Bryan Grenfell, Bing Xu
dc.identifier.citationPLoS Neglected Tropical Diseases, 2015; 9(3):e0003530-1-e0003530-13
dc.identifier.doi10.1371/journal.pntd.0003530
dc.identifier.issn1935-2727
dc.identifier.issn1935-2735
dc.identifier.orcidBi, P. [0000-0002-3238-3427]
dc.identifier.urihttp://hdl.handle.net/2440/91355
dc.language.isoen
dc.publisherPublic Library of Science
dc.relation.grantNFC
dc.rightsThis is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication
dc.source.urihttps://doi.org/10.1371/journal.pntd.0003530
dc.subjectAnimals
dc.subjectHumans
dc.subjectRodentia
dc.subjectHantaan virus
dc.subjectHemorrhagic Fever with Renal Syndrome
dc.subjectIncidence
dc.subjectBayes Theorem
dc.subjectPoisson Distribution
dc.subjectTemperature
dc.subjectSeasons
dc.subjectDisease Outbreaks
dc.subjectPopulation Dynamics
dc.subjectModels, Theoretical
dc.subjectHistory, 21st Century
dc.subjectChina
dc.titleChanges in rodent abundance and weather conditions potentially drive hemorrhagic fever with renal syndrome outbreaks in Xi’an, China, 2005–2012
dc.typeJournal article
pubs.publication-statusPublished

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