Volume 89, Issue 5
  • ISSN: 0002-9637
  • E-ISSN: 1476-1645



The purpose of this study was to quantify the relationship between climate variation and transmission of hemorrhagic fever with renal syndrome (HFRS) in Heilongjiang Province, a highly endemic area for HFRS in China. Monthly notified HFRS cases and climatic data for 2001–2009 in Heilongjiang Province were collected. Using a seasonal autoregressive integrated moving average model, we found that relative humidity with a one-month lag (β = −0.010, = 0.003) and a three-month lag (β = 0.008, = 0.003), maximum temperature with a two-month lag (β = 0.082, = 0.028), and southern oscillation index with a two-month lag (β = −0.048, = 0.019) were significantly associated with HFRS transmission. Our study also showed that predicted values expected under the seasonal autoregressive integrated moving average model were highly consistent with observed values (Adjusted = 83%, root mean squared error = 108). Thus, findings may help add to the knowledge gap of the role of climate factors in HFRS transmission in China and also assist national local health authorities in the development/refinement of a better strategy to prevent HFRS transmission.


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  1. Ministry of Health, 1998. Handbook of Epidemic Hemorrhagic Fever Prevention and Control. Beijing: China People's Health Publishing House, 6380. [Google Scholar]
  2. Schmaljohn C, Hjelle B, , 1997. Hantaviruses: a global disease problem. Emerg Infect Dis 3: 95104.[Crossref] [Google Scholar]
  3. Bi Z, Formenty PB, Roth CE, , 2008. Hantavirus infection: a review and global update. J Infect Dev Ctries 2: 323.[Crossref] [Google Scholar]
  4. Yan L, Fang LQ, Huang HG, Zhang LQ, Feng D, Zhao WJ, Zhang WY, Li XW, Cao WC, , 2007. Landscape elements and Hantaan virus-related hemorrhagic fever with renal syndrome, People's Republic of China. Emerg Infect Dis 13: 13011306.[Crossref] [Google Scholar]
  5. Jiang JF, Wu XM, Zuo SQ, Wang RM, Chen LQ, Wang BC, Dun Z, Zhang PH, Guo TY, Cao WC, , 2006. Study on the association between Hantavirus infection and Rattus norvegicus . Chin J Epidemiol 27: 196199. [Google Scholar]
  6. Zhang WY, Fang LQ, Jiang JF, Hui FM, Glass GE, Yan L, Xu YF, Zhao WJ, Yang H, Liu W, Cao WC, , 2009. Predicting the risk of Hantavirus infection in Beijing, People's Republic of China. Am J Trop Med Hyg 80: 678683. [Google Scholar]
  7. Bi P, Wu X, Zhang F, Parton K, Tong S, , 1998. Seasonal rainfall variability, the incidence of hemorrhagic fever with renal syndrome, and prediction of the disease in low-lying areas of China. Am J Epidemiol 148: 276281.[Crossref] [Google Scholar]
  8. Bi P, Tong S, Donald K, Parton K, Ni J, , 2002. Climatic, reservoir and occupational variables and the transmission of haemorrhagic fever with renal syndrome in China. Int J Epidemiol 31: 189193.[Crossref] [Google Scholar]
  9. Ernest SKM, Brown JH, Parmenter RR, , 2000. Rodents, plants, and precipitation: spatial and temporal dynamics of consumers and resources. Oikos 88: 470482.[Crossref] [Google Scholar]
  10. Glass GE, Shields T, Cai B, Yates TL, Parmenter R, , 2007. Persistently highest risk areas for hantavirus pulmonary syndrome: potential sites for refugia. Ecol Appl 17: 129139.[Crossref] [Google Scholar]
  11. Langlois JP, Fahrig L, Merriam G, Artsob H, , 2001. Landscape structure infuences continental distribution of Hantavirus in deer mice. Landscape Ecol 16: 255266.[Crossref] [Google Scholar]
  12. Madsen T, Shine R, , 1999. Rainfall and rats: climatically-driven dynamics of a tropical rodent population. Aust J Ecol 24: 8089.[Crossref] [Google Scholar]
  13. Engelthaler DM, Mosley DG, Cheek JE, Levy CE, Komatsu KK, Ettestad P, Davis T, Tanda DT, Miller L, Frampton JW, Porter R, Bryan RT, , 1999. Climatic and environmental patterns associated with hantavirus pulmonary syndrome, Four Corners region, United States. Emerg Infect Dis 5: 8794.[Crossref] [Google Scholar]
  14. Luis AD, Douglass RJ, Mills JN, Bjørnstad ON, , 2010. The effect of seasonality, density and climate on the population dynamics of Montana deer mice, important reservoir hosts for Sin Nombre Hantavirus. J Anim Ecol 79: 462470.[Crossref] [Google Scholar]
  15. Glass GE, Cheek JE, Patz JA, Shields TM, Doyle TJ, Thoroughman DA, Hunt DK, Enscore RE, Gage KL, Irland C, Peters CJ, Bryan R, , 2000. Using remotely sensed data to identify areas at risk for Hantavirus pulmonary syndrome. Emerg Infect Dis 6: 238247.[Crossref] [Google Scholar]
  16. Hjelle B, Glass GE, , 2000. Outbreak of Hantavirus infection in the Four Corners region of the United States in the wake of the 1997–1998 El Niño-Southern Oscillation. J Infect Dis 181: 15691573.[Crossref] [Google Scholar]
  17. Tamerius JD, Wise EK, Uejio CK, McCoy AL, Comrie AC, , 2007. Climate and human health: synthesizing environmental complexity and uncertainty. Stochastic Environ Res Risk Assess 21: 601613.[Crossref] [Google Scholar]
  18. Tersago K, Verhagen R, Servais A, Heyman P, Ducoffre G, Leirs H, , 2009. Hantavirus disease (nephropathia epidemica) in Belgium: effects of tree seed production and climate. Epidemiol Infect 137: 250256.[Crossref] [Google Scholar]
  19. Klempa B, , 2009. Hantaviruses and climate change. Clin Microbiol Infect 15: 518523.[Crossref] [Google Scholar]
  20. Bi P, Parton K, , 2003. El Niño and incidence of hemorrhagic fever with renal syndrome in China. JAMA 289: 176177.[Crossref] [Google Scholar]
  21. Fang LQ, Wang XJ, Liang S, Yan LL, Song SX, Zhang WY, Qian Q, Li YP, Wei L, Wang ZQ, Yang H, Cao WC, , 2010. Spatiotemporal trends and climatic factors of hemorrhagic fever with renal syndrome epidemic in Shandong Province, China. PLoS Negl Trop Dis 4: 110.[Crossref] [Google Scholar]
  22. Pettersson L, Boman J, Juto P, Evander M, Ahlm C, , 2008. Outbreak of Puumala virus infection, Sweden. Emerg Infect Dis 14: 808810.[Crossref] [Google Scholar]
  23. Schwarz AC, Ranft U, Piechotowski I, Childs JE, Brockmann SO, , 2009. Risk factors for human infection with Puumala virus, southwestern Germany. Emerg Infect Dis 15: 10321039.[Crossref] [Google Scholar]
  24. Huang RH, Wu YF, , 1989. The infuence of ENSO on the summer climate change in China and its mechanism. Adv Atmos Sci 6: 2132.[Crossref] [Google Scholar]
  25. Zhang WY, Guo WD, Fang LQ, Li CP, Bi P, Glass GE, Jiang JF, Sun SH, Qian Q, Liu W, Yan L, Yang H, Tong SL, Cao WC, , 2010. Climate variability and hemorrhagic fever with renal syndrome transmission in northeastern China. Environ Health Perspect 118: 915920.[Crossref] [Google Scholar]
  26. National Centers for Environmental Prediction, 2010. Climatic Forecast System. Available at: http://www.cpc.ncep.noaa.gov/data/indices/soi. Accessed June 6, 2010. [Google Scholar]
  27. Hu W, Tong S, Mengersen K, Connell D, , 2007. Weather variability and the incidence of cryptosporidiosis: comparison of time series poisson regression and SARIMA models. Ann Epidemiol 17: 679688.[Crossref] [Google Scholar]
  28. Allard R, , 1998. Use of time-series analysis in infectious disease surveillance. Bull World Health Organ 76: 327333. [Google Scholar]
  29. Box G, Jenkins G, , 1970. Time-Series Analysis: Forecasting and Control. San Francisco, CA: Holden-Day, 376382. [Google Scholar]
  30. Hu W, Mengersen K, Bi P, Tong S, , 2007. Time series analysis of the risk factors for hemorrhagic fever with renal syndrome: comparison of statistical models. Epidemiol Infect 135: 245252.[Crossref] [Google Scholar]
  31. Bozdogan H, , 1987. Model-selection and Akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika 52: 345370.[Crossref] [Google Scholar]
  32. Liddle AR, , 2007. Information criteria for astrophysical model selection. Mon Not R Astron Soc 377: 7478.[Crossref] [Google Scholar]
  33. SAS, 2008. SAS/ETS®9.2: User's Guide. Cary, NC: SAS Institute Inc., 177300. [Google Scholar]
  34. Calisher CH, Wagoner KD, Amman BR, Root JJ, Douglass RJ, Kuenzi AJ, Abbott KD, Parmenter C, Yates TL, Ksiazek TG, Beaty BJ, Mills JN, , 2007. Demographic factors associated with prevalence of antibody to Sin Nombre virus in deer mice in the western United States. J Wildl Dis 43: 111.[Crossref] [Google Scholar]
  35. Glass GE, Livingstone W, Mills JN, Hlady WG, Fine JB, Biggler W, Coke T, Frazier D, Atherley S, Rollin PE, Ksiazek TG, Peters CJ, Childs JE, , 1998. Black Creek Canal virus infection in Sigmodon hispidus in southern Florida. Am J Trop Med Hyg 59: 699703. [Google Scholar]
  36. Yahnke CJ, Meserve PL, Ksiazek TG, Mills JN, . Patterns of infection with Laguna Negra virus in wild populations of Calomys laucha in the central Paraguayan Chaco 2001. Am J Trop Med Hyg 65: 768776. [Google Scholar]
  37. Luo CW, Liu QY, Hou JL, , 2009. Correlation analysis and regression model of epidemic factors of hemorrhagic fever with renal syndrome in Heihe City, Heilongjiang Province. Dis Surveill 24: 118120. [Google Scholar]
  38. Thomson MC, Mason SJ, Phindela T, Connor SJ, , 2005. Use of rainfall and sea surface temperature monitoring for malaria early warning in Botswana. Am J Trop Med Hyg 73: 214221. [Google Scholar]
  39. Zhou G, Minakawa N, Githeko AK, Yan G, , 2004. Association between climate variability and malaria epidemics in the east African highlands. Proc Natl Acad Sci USA 101: 23752380.[Crossref] [Google Scholar]
  40. Cazelles B, Chavez M, McMichael AJ, Hales S, , 2005. Nonstationary influence of El Niño on the synchronous dengue epidemics in Thailand. PLoS Med 2: e106.[Crossref] [Google Scholar]
  41. Hales S, de Wet N, Maindonald J, Woodward A, , 2002. Potential effect of population and climate changes on global distribution of dengue fever: an empirical model. Lancet 360: 830834.[Crossref] [Google Scholar]
  42. Tong S, Hu W, , 2001. Climate variation and incidence of Ross river virus in Cairns, Australia: a time-series analysis. Environ Health Perspect 109: 12711273.[Crossref] [Google Scholar]
  43. Tong S, Hu W, , 2002. Different responses of Ross River virus to climate variability between coastline and inland cities in Queensland, Australia. Occup Environ Med 59: 739744.[Crossref] [Google Scholar]
  44. Chaves LF, Pascual M, , 2006. Climate cycles and forecasts of cutaneous leishmaniasis, a nonstationary vector-borne disease. PLoS Med 3: 295.[Crossref] [Google Scholar]
  45. Nakazawa Y, Williams R, Peterson AT, Mead P, Staples E, Gage KL, , 2007. Climate change effects on plague and tularemia in the United States. Vector Borne Zoonotic Dis 7: 529540.[Crossref] [Google Scholar]
  46. Hallett TB, Coulson T, Pilkington JG, Clutton-Brock TH, Pemberton JM, Grenfell BT, , 2004. Why large-scale climate indices seem to predict ecological processes better than local weather. Nature 430: 7175.[Crossref] [Google Scholar]
  47. Zheng ZM, Jiang ZK, Chen AG, , 2008. Rodents Zoology. Shanghai: Shanghai Jiaotong University Press, 400450. [Google Scholar]

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  • Received : 02 Aug 2012
  • Accepted : 12 Jul 2013
  • Published online : 06 Nov 2013

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