Ambient Temperature and Major Infectious Diseases in China

  • Xiaobo Liu
  • Jimin Sun
  • Xiaodong Liu
  • Jingchun Fan
  • Yanlin Niu
  • Lingling Lang
  • Qiyong LiuEmail author


Infectious diseases are a group of diseases which have complex transmission ways and various influencing factors. Clarifying the correlation between ambient temperature and major infectious diseases in China is a crucial step toward the successful control of infectious diseases including vector-borne diseases, water-borne diseases, food-borne diseases, respiratory infectious diseases, etc. and the implementations of climate change adaption strategy and measures in China. However, no study has systematically reviewed the available evidences on the impact of ambient temperature on the incidence of major infectious diseases, and such information is essential for policymakers and stakeholders to take specific actions to control infectious diseases and protect the vulnerable population in the future. In order to fill this gap, we systematically review the current evidence for the effect of ambient temperature on major infectious diseases in China. The findings could provide explicit information for the scientific prevention and control of infectious diseases in China.


Ambient temperature Climate change Infectious diseases China 



This study were supported by the National Key Research and Development Project “Biological Security Key Technology Research and Development” Special Funds (No. 2016YFC1200802), National Natural Science Foundation of China (No. 81703280), and National Science and Technology Major Project (No. 2017ZX10303404-005).


  1. 1.
    Liu Q, Xu W, et al. Landscape of emerging and re-emerging infectious diseases in China: impact of ecology, climate, and behavior. Front Med. 2018a;12(1):3–22.PubMedCrossRefPubMedCentralGoogle Scholar
  2. 2.
    Li C, Lu Y, et al. Climate change and dengue fever transmission in China: evidences and challenges. Sci Total Environ. 2018;622-623:493–501.PubMedCrossRefPubMedCentralGoogle Scholar
  3. 3.
    Li T, Yang Z, et al. Temperature, relative humidity and sunshine may be the effective predictors for occurrence of malaria in Guangzhou, southern China, 2006-2012. Parasit Vectors. 2013b;6:155.PubMedPubMedCentralCrossRefGoogle Scholar
  4. 4.
    Sun Leping ZX, Qingbiao H, Yang G, Yixin H, Weiping X, Yuji J. Impact of global warming on the transmission of schistosomiasis in China V. Effectively growing degree days of schistosoma japonicum developing in different snail populations. Chin J Schisto Control. 2003;15(5):342–5.Google Scholar
  5. 5.
    Yi H, Xia C, et al. Assessing environmental factors associated with regional schistosomiasis prevalence in Anhui Province, Peoples’ Republic of China using a geographical detector method. Infect Dis Poverty. 2017;6(1):87.CrossRefGoogle Scholar
  6. 6.
    Li J, Rao Y, et al. Identification of climate factors related to human infection with avian influenza A H7N9 and H5N1 viruses in China. Sci Rep. 2015a;5:18094.PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Chen B, Liu Q. Dengue fever in China. Lancet. 2015;385(9978):1621–2.PubMedCrossRefPubMedCentralGoogle Scholar
  8. 8.
    Wu F, Liu Q, et al. Distribution of Aedes albopictus (Diptera: Culicidae) in northwestern China. Vector Borne Zoonotic Dis. 2011;11(8):1181–6.PubMedCrossRefPubMedCentralGoogle Scholar
  9. 9.
    Liu Z, Zhang Z, et al. Temperature increase enhances Aedes albopictus competence to transmit dengue virus. Front Microbiol. 2017;8:2337.PubMedPubMedCentralCrossRefGoogle Scholar
  10. 10.
    Jing QL, Cheng Q, et al. Imported cases and minimum temperature drive dengue transmission in Guangzhou, China: evidence from ARIMAX model. Epidemiol Infect. 2018;146:1226–35.PubMedCrossRefPubMedCentralGoogle Scholar
  11. 11.
    Xu L, Stige LC, et al. Climate variation drives dengue dynamics. Proc Natl Acad Sci U S A. 2017;114(1):113–8.PubMedCrossRefPubMedCentralGoogle Scholar
  12. 12.
    Wu X, Lang L, et al. Non-linear effects of mean temperature and relative humidity on dengue incidence in Guangzhou, China. Sci Total Environ. 2018;628-629:766–71.PubMedCrossRefPubMedCentralGoogle Scholar
  13. 13.
    Sang S, Yin W, et al. Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability. PLoS One. 2014;9(7):e102755.PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Lu L, Lin H, et al. Time series analysis of dengue fever and weather in Guangzhou, China. BMC Public Health. 2009;9:395.PubMedPubMedCentralCrossRefGoogle Scholar
  15. 15.
    Fan J, Lin H, et al. Identifying the high-risk areas and associated meteorological factors of dengue transmission in Guangdong Province, China from 2005 to 2011. Epidemiol Infect. 2014;142(3):634–43.PubMedCrossRefPubMedCentralGoogle Scholar
  16. 16.
    Xiang J, Hansen A, et al. Association between dengue fever incidence and meteorological factors in Guangzhou, China, 2005-2014. Environ Res. 2017;153:17–26.PubMedCrossRefPubMedCentralGoogle Scholar
  17. 17.
    Shen JC, Luo L, et al. The impacts of mosquito density and meteorological factors on dengue fever epidemics in Guangzhou, China, 2006-2014: a time-series analysis. Biomed Environ Sci. 2015;28(5):321–9.PubMedPubMedCentralGoogle Scholar
  18. 18.
    Zhou SS, Huang F, et al. Geographical, meteorological and vectorial factors related to malaria re-emergence in Huang-Huai River of Central China. Malar J. 2010;9:337.PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Bi P, Tong S, et al. Climatic variables and transmission of malaria: a 12-year data analysis in Shuchen County, China. Public Health Rep. 2003;118(1):65–71.PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Zhao X, Chen F, et al. Characterizing the effect of temperature fluctuation on the incidence of malaria: an epidemiological study in south-west China using the varying coefficient distributed lag non-linear model. Malar J. 2014;13:192.PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    Zhang Y, Bi P, et al. Meteorological variables and malaria in a Chinese temperate city: a twenty-year time-series data analysis. Environ Int. 2010b;36(5):439–45.PubMedCrossRefPubMedCentralGoogle Scholar
  22. 22.
    Tian L, Bi Y, et al. One-year delayed effect of fog on malaria transmission: a time-series analysis in the rain forest area of Mengla County, south-west China. Malar J. 2008;7:110.PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Huang F, Zhang S, Wang H, Tang L. Temporal correlation analysis between malaria and meteorological factors in Motuo County, Tibet. Malar J. 2011;10:54.PubMedPubMedCentralCrossRefGoogle Scholar
  24. 24.
    Wardrop NA, Barnett AG, et al. Plasmodium vivax malaria incidence over time and its association with temperature and rainfall in four counties of Yunnan Province, China. Malar J. 2013;12:452.PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Bramanti B, Stenseth NC, et al. In: Yang R, Anisimov A, editors. Plague: a disease which changed the path of human civilization. Yersinia pestis: retrospective and perspective. Berlin: Springer; 2016. p. 1–26.Google Scholar
  26. 26.
    Perry RD, Fetherston JD. Yersinia pestis—etiologic agent of plague. Clin Microbiol Rev. 1997;10(1):35–66.PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Ari TB, Gershunov A, et al. Human plague in the USA: the importance of regional and local climate. Biol Lett. 2008;4(6):737–40.PubMedPubMedCentralCrossRefGoogle Scholar
  28. 28.
    Ari TB, Neerinckx S, et al. Plague and climate: scales matter. PLoS Pathog. 2011;7(9):e1002160.PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Holt AC, Salkeld DJ, et al. Spatial analysis of plague in California: niche modeling predictions of the current distribution and potential response to climate change. Int J Health Geogr. 2009;8(1):38.PubMedPubMedCentralCrossRefGoogle Scholar
  30. 30.
    Parmenter RR, Yadav EP, et al. Incidence of plague associated with increased winter-spring precipitation in New Mexico. Am J Trop Med Hyg. 1999;61(5):814–21.PubMedCrossRefPubMedCentralGoogle Scholar
  31. 31.
    Xu L, Fang X, et al. The evolutionary dynamics and the ecological niche of natural plague foci in China. Chin J Vector Biol Control. 2015a;3:228–32.Google Scholar
  32. 32.
    Davis S, Begon M, et al. Predictive thresholds for plague in Kazakhstan. Science. 2004;304(5671):736–8.PubMedCrossRefPubMedCentralGoogle Scholar
  33. 33.
    Kausrud KL, Begon M, et al. Modeling the epidemiological history of plague in Central Asia: palaeoclimatic forcing on a disease system over the past millennium. BMC Biol. 2010;8(1):112.PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Reijniers J, Davis S, et al. A curve of thresholds governs plague epizootics in Central Asia. Ecol Lett. 2012;15(6):554–60.PubMedCrossRefPubMedCentralGoogle Scholar
  35. 35.
    Davis S, Calvet E, et al. Fluctuating rodent populations and risk to humans from rodent-borne zoonoses. Vector Borne Zoonotic Dis. 2005;5(4):305–14.PubMedCrossRefPubMedCentralGoogle Scholar
  36. 36.
    Gage KL, Burkot TR, et al. Climate and vectorborne diseases. Am J Prev Med. 2008;35(5):436–50.PubMedCrossRefPubMedCentralGoogle Scholar
  37. 37.
    Schmid BV, Büntgen U, et al. Climate-driven introduction of the black death and successive plague reintroductions into Europe. Proc Natl Acad Sci U S A. 2015;112(10):3020–5.PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Enscore RE, Biggerstaff BJ, et al. Modeling relationships between climate and the frequency of human plague cases in the southwestern United States, 1960-1997. Am J Trop Med Hyg. 2002;66(2):186–96.PubMedCrossRefPubMedCentralGoogle Scholar
  39. 39.
    Xu L, Liu Q, et al. Nonlinear effect of climate on plague during the third pandemic in China. Proc Natl Acad Sci U S A. 2011;108(25):10214–9.PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Brown HE, Ettestad P, et al. Climatic predictors of the intra- and inter-annual distributions of plague cases in New Mexico based on 29 years of animal-based surveillance data. Am J Trop Med Hyg. 2010;82(1):95–102.PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Cavanaugh DC, Marshall JD Jr. The influence of climate on the seasonal prevalence of plague in the Republic of Vietnam. J Wildl Dis. 1972;8(1):85–94.PubMedCrossRefPubMedCentralGoogle Scholar
  42. 42.
    Stenseth NC, Samia NI, et al. Plague dynamics are driven by climate variation. Proc Natl Acad Sci U S A. 2006;103(35):13110–5.PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Xu L, Schmid BV, et al. The trophic responses of two different rodent–vector–plague systems to climate change. Proc R Soc Lond B Biol Sci. 2015b;282(1800):20141846.CrossRefGoogle Scholar
  44. 44.
    Yue RPH, Lee HF. Pre-industrial plague transmission is mediated by the synergistic effect of temperature and aridity index. BMC Infect Dis. 2018a;18(1):134.PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    Kreppel KS, Caminade C, et al. A non-stationary relationship between global climate phenomena and human plague incidence in Madagascar. PLoS Negl Trop Dis. 2014;8(10):e3155.PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    Zhang Z, Li Z, et al. Relationship between increase rate of human plague in China and global climate index as revealed by cross-spectral and cross-wavelet analyses. Integr Zool. 2007;2(3):144–53.PubMedCrossRefPubMedCentralGoogle Scholar
  47. 47.
    Yue RPH, Lee HF. The relationship between climate change and plague in European history. Sci Sin Terrae. 2018b;48(2):165–80.CrossRefGoogle Scholar
  48. 48.
    Liu J, Xue FZ, et al. Association of haemorrhagic fever with renal syndrome and weather factors in Junan County, China: a case-crossover study. Epidemiol Infect. 2013;141(4):697–705.PubMedCrossRefPubMedCentralGoogle Scholar
  49. 49.
    Hansen A, Cameron S, et al. Transmission of haemorrhagic fever with renal syndrome in China and the role of climate factors: a review. Int J Infect Dis. 2015;33:212–8.PubMedCrossRefPubMedCentralGoogle Scholar
  50. 50.
    Lin H, Zhang Z, et al. Meteorological factors are associated with hemorrhagic fever with renal syndrome in Jiaonan County, China, 2006-2011. Int J Biometeorol. 2014;58(6):1031–7.PubMedCrossRefPubMedCentralGoogle Scholar
  51. 51.
    Xiao H, Lin X, et al. Ecology and geography of hemorrhagic fever with renal syndrome in Changsha, China. BMC Infect Dis. 2013b;13:305.PubMedPubMedCentralCrossRefGoogle Scholar
  52. 52.
    Jiang F, Wang L, et al. Meteorological factors affect the epidemiology of hemorrhagic fever with renal syndrome via altering the breeding and hantavirus-carrying states of rodents and mites: a 9 years’ longitudinal study. Emerg Microbes Infect. 2017;6(11):e104.PubMedPubMedCentralGoogle Scholar
  53. 53.
    Bi P, Tong S, et al. Climatic, reservoir and occupational variables and the transmission of haemorrhagic fever with renal syndrome in China. Int J Epidemiol. 2002;31(1):189–93.PubMedCrossRefPubMedCentralGoogle Scholar
  54. 54.
    Guan P, Huang D, et al. Investigating the effects of climatic variables and reservoir on the incidence of hemorrhagic fever with renal syndrome in Huludao City, China: a 17-year data analysis based on structure equation model. BMC Infect Dis. 2009;9:109.PubMedPubMedCentralCrossRefGoogle Scholar
  55. 55.
    Joshi YP, Kim EH, et al. The influence of climatic factors on the development of hemorrhagic fever with renal syndrome and leptospirosis during the peak season in Korea: an ecologic study. BMC Infect Dis. 2017;17(1):406.PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    Li CP, Cui Z, Li SL, Magalhaes RJ, Wang BL, Zhang C, Sun HL, Li CY, Huang LY, Ma J, Zhang WY. Association between hemorrhagic fever with renal syndrome epidemic and climate factors in Heilongjiang Province, China. Am J Trop Med Hyg. 2013a;89(5):1006–12.PubMedPubMedCentralCrossRefGoogle Scholar
  57. 57.
    Liu X, Jiang B, et al. Temporal trend and climate factors of hemorrhagic fever with renal syndrome epidemic in Shenyang City, China. BMC Infect Dis. 2011;11:331.PubMedPubMedCentralCrossRefGoogle Scholar
  58. 58.
    Xiang J, Hansen A, et al. Impact of meteorological factors on hemorrhagic fever with renal syndrome in 19 cities in China, 2005-2014. Sci Total Environ. 2018;636:1249–56.PubMedCrossRefPubMedCentralGoogle Scholar
  59. 59.
    Xiao H, Gao LD, et al. Environmental variability and the transmission of haemorrhagic fever with renal syndrome in Changsha, People’s Republic of China. Epidemiol Infect. 2013a;141(9):1867–75.PubMedCrossRefPubMedCentralGoogle Scholar
  60. 60.
    Fang LQ, Wang XJ, et al. Spatiotemporal trends and climatic factors of hemorrhagic fever with renal syndrome epidemic in Shandong Province, China. PLoS Negl Trop Dis. 2010;4(8):e789.PubMedPubMedCentralCrossRefGoogle Scholar
  61. 61.
    Wei Y, Wang Y, et al. Meteorological factors and risk of hemorrhagic fever with renal syndrome in Guangzhou, southern China, 2006-2015. PLoS Negl Trop Dis. 2018;12(6):e0006604.PubMedPubMedCentralCrossRefGoogle Scholar
  62. 62.
    Zhang WY, Guo WD, et al. Climate variability and hemorrhagic fever with renal syndrome transmission in northeastern China. Environ Health Perspect. 2010a;118(7):915–20.PubMedPubMedCentralCrossRefGoogle Scholar
  63. 63.
    Jimin Sun LL, Yang J, Liu K, Wu H, Liu Q. Association between severe fever with thrombocytopenia syndrome incidence and ambient temperature. Am J Trop Med Hyg. 2018;98(5):1478–83.PubMedPubMedCentralCrossRefGoogle Scholar
  64. 64.
    Sun JM, Lu L, et al. Forecast of severe fever with thrombocytopenia syndrome incidence with meteorological factors. Sci Total Environ. 2018;626:1188–92.PubMedCrossRefPubMedCentralGoogle Scholar
  65. 65.
    Du Z, Wang Z, et al. Ecological niche modeling for predicting the potential risk areas of severe fever with thrombocytopenia syndrome. Int J Infect Dis. 2014;26:1–8.PubMedCrossRefPubMedCentralGoogle Scholar
  66. 66.
    Wang T, Li XL, et al. Epidemiological characteristics and environmental risk factors of severe fever with thrombocytopenia syndrome in Hubei Province, China, from 2011 to 2016. Front Microbiol. 2017;8:387.PubMedPubMedCentralGoogle Scholar
  67. 67.
    Zhai Yujia LF, Xiaopeng S, He F, Lin J. A study on the association between meteorological factors and severe fever with thrombocytopenia syndrome. Zhejiang Prev Med. 2016;28(2):117–20. (in Chinese).Google Scholar
  68. 68.
    Utzinger J, Keiser J. Schistosomiasis and soil-transmitted helminthiasis: common drugs for treatment and control. Expert Opin Pharmacother. 2004;5(2):263–85.PubMedCrossRefPubMedCentralGoogle Scholar
  69. 69.
    King CH, Dickman K, et al. Reassessment of the cost of chronic helmintic infection: a meta-analysis of disability-related outcomes in endemic schistosomiasis. Lancet. 2015;365(9470):1561–9.CrossRefGoogle Scholar
  70. 70.
    Wang W, Liang YS, et al. African schistosomiasis in mainland China: risk of transmission and countermeasures to tackle the risk. Parasit Vectors. 2013b;6(1):249.PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Zhou XN, Wang LY, et al. The public health significance and control of schistosomiasis in China—then and now. Acta Trop. 2005;96(2):97–105.PubMedCrossRefPubMedCentralGoogle Scholar
  72. 72.
    Guo D, Zhang Y, et al. Functional properties of hemocyanin from Oncomelania hupensis, the intermediate host of Schistosoma japonicum. Exp Parasitol. 2009;123(3):277–81.PubMedCrossRefPubMedCentralGoogle Scholar
  73. 73.
    Wei MA, Liao WG, et al. Study on response of suitable environment for Oncomelania breeding grounds to variation of flow regime. J Yangtze River Sci Res Inst. 2010;27(10):65–9.Google Scholar
  74. 74.
    Mao CP. Biology of schistosome and control of schistosomiasis. Beijing: People’s Health Press; 1990.Google Scholar
  75. 75.
    Ross AGP, Sleigh AC, et al. Schistosomiasis in the People’s Republic of China: prospects and challenges for the 21st century. Clin Microbiol Rev. 2001;14(2):270.PubMedPubMedCentralCrossRefGoogle Scholar
  76. 76.
    Stensgaard AS, Utzinger J, et al. Large-scale determinants of intestinal schistosomiasis and intermediate host snail distribution across Africa: does climate matter? Acta Trop. 2013;128(2):378–90.PubMedCrossRefPubMedCentralGoogle Scholar
  77. 77.
    Yang GJ, Utzinger J, et al. The regional network for Asian Schistosomiasis and other Helminth Zoonoses (RNAS+): target diseases in face of climate change. Adv Parasitol. 2010a;73:101.PubMedCrossRefPubMedCentralGoogle Scholar
  78. 78.
    Yang GJ, Vounatsou P, et al. A Bayesian-based approach for spatio-temporal modeling of county level prevalence of Schistosoma japonicum infection in Jiangsu province, China. Int J Parasitol. 2005a;35(2):155–62.PubMedCrossRefPubMedCentralGoogle Scholar
  79. 79.
    Zhou XN, Yang GJ, et al. Potential impact of climate change on schistosomiasis transmission in China. Am J Trop Med Hyg. 2008;78(2):188–94.PubMedCrossRefPubMedCentralGoogle Scholar
  80. 80.
    Leping ZX, Qingbiao H, Yang G, Yixing H, Weiping X, Yuji J. Impact of global warming on transmission of schistosomiasis in china III relationship between snail infections rate and environmental temperature. Chin J Schisto Control. 2003;15(3):161–3.Google Scholar
  81. 81.
    Yang GJ, Utzinger J, et al. Effect of temperature on the development of Schistosoma japonicum within Oncomelania hupensis, and hibernation of O. hupensis. Parasitol Res. 2007;100(4):695–700.PubMedCrossRefPubMedCentralGoogle Scholar
  82. 82.
    Zhou Xiaonong YG, Leping S, Qingbiao H, Yang K, Wang R, Zhenghui H. The potential impact of global warming on schistosomiasis transmission. Chin J Epidemiol. 2002;23(2):83–6.Google Scholar
  83. 83.
    Xu Y, Zhang S. The influence of environmental factors on snail growth and distribution. Int J Med Parasit Dis. 2011;38(4):218–22.Google Scholar
  84. 84.
    Liu YY, Zhang WZ, Wang YX. Medical malacology. Beijing: Ocean Press; 1993.Google Scholar
  85. 85.
    Zhu HR, Liu L, et al. Ecological model to predict potential habitats of Oncomelania hupensis, the intermediate host of Schistosoma japonicum in the mountainous regions, China. PLoS Negl Trop Dis. 2015;9(8):e0004028.PubMedPubMedCentralCrossRefGoogle Scholar
  86. 86.
    Wu JY, Zhou YB, et al. Identification of optimum scopes of environmental factors for snails using spatial analysis techniques in Dongting Lake region, China. Parasit Vectors. 2014;7(1):216.PubMedPubMedCentralCrossRefGoogle Scholar
  87. 87.
    Sun LP, Zhou XN, et al. Investigation on effectively growing degree days of cercaria of Schistosoma japonicum developing in snail. Chin J Zoonoses. 2003;19(06):59–61.Google Scholar
  88. 88.
    Gong C, Dan L, et al. The influence of natural factors on the spatio-temporal distribution of Oncomelania hupensis. Acta Trop. 2016;164:194–207.CrossRefGoogle Scholar
  89. 89.
    Wang XH, Zhou XN, et al. Bayesian Spatio-temporal modeling of Schistosoma japonicum prevalence data in the absence of a diagnostic ‘gold’ standard. PLoS Negl Trop Dis. 2008;2(6):e250.PubMedPubMedCentralCrossRefGoogle Scholar
  90. 90.
    Yang GJ, Vounatsou P, et al. A potential impact of climate change and water resource development on the transmission of Schistosoma japonicum in China. Parassitologia. 2005b;47(1):127.PubMedPubMedCentralGoogle Scholar
  91. 91.
    Yang J, Zhao Z, et al. A multi-level analysis of risk factors for Schistosoma japonicum infection in China. Int J Infect Dis. 2009;13(6):e407–12.PubMedCrossRefPubMedCentralGoogle Scholar
  92. 92.
    Yi H, Zhang Z, et al. Spatial pattern of schistosomiasis in Xingzi, Jiangxi Province, China: the effects of environmental factors. Part Fibre Toxicol. 2013;6(1):214.Google Scholar
  93. 93.
    Yong W, Zhuang D. A rapid monitoring and evaluation method of schistosomiasis based on spatial information technology. Int J Environ Res Public Health. 2015;12(12):15843–59.CrossRefGoogle Scholar
  94. 94.
    Hu Y, Li R, et al. Spatio-temporal transmission and environmental determinants of Schistosomiasis japonica in Anhui Province, China. PLoS Negl Trop Dis. 2015b;9(2):e0003470.PubMedPubMedCentralCrossRefGoogle Scholar
  95. 95.
    Zhang Zhijie PW, Jianlin Z, Yibiao Z, Genming Z, Qingwu J. Relationships between distribution of Oncomelania hupensis and extreme air temperature in a year. Chin J Schisto Control. 2005;17(5):341–3.Google Scholar
  96. 96.
    Yang GJ, Vounatsou P, et al. A review of geographic information system and remote sensing with applications to the epidemiology and control of schistosomiasis in China. Acta Trop. 2005c;96(2):117–29.PubMedCrossRefPubMedCentralGoogle Scholar
  97. 97.
    Liu Kequn XX, Yuxia C, Rendong L, Yang T, Fenghua W. Analysis on influence of meteorological factors on Oncomelania density. Chin J Epidemiol. 2015;36(11):1274–8.Google Scholar
  98. 98.
    Zheng Y, Qang Q, et al. The function of the overlaying climate data in analysis of Oncomelania snail distribution. Chin Publ Health. 1998;14:724–5.Google Scholar
  99. 99.
    Yang Y, Zheng SB, et al. The three gorges dam: does the flooding time determine the distribution of Schistosome-transmitting snails in the middle and lower reaches of the Yangtze River, China? Int J Environ Res Public Health. 2018;15(7):1304.PubMedCentralCrossRefGoogle Scholar
  100. 100.
    Schrader M, Hauffe T, et al. Spatially explicit modeling of schistosomiasis risk in eastern China based on a synthesis of epidemiological, environmental and intermediate host genetic data. PLoS Negl Trop Dis. 2013;7(7):e2327.PubMedPubMedCentralCrossRefGoogle Scholar
  101. 101.
    Zhang Z, Ong S, et al. A model for the prediction of Oncomelania hupensis in the lake and marshland regions, China. Parasitol Int. 2008;57(2):121–31.PubMedCrossRefPubMedCentralGoogle Scholar
  102. 102.
    Tian L, Liang F, et al. Spatio-temporal analysis of the relationship between meteorological factors and hand-foot-mouth disease in Beijing, China. BMC Infect Dis. 2018;18(1):158.PubMedPubMedCentralCrossRefGoogle Scholar
  103. 103.
    Shi RX, Wang JF, et al. Spatiotemporal pattern of hand-foot-mouth disease in China: an analysis of empirical orthogonal functions. Public Health. 2014;128(4):367–75.PubMedCrossRefPubMedCentralGoogle Scholar
  104. 104.
    Wang JF, Xu CD, et al. Spatial dynamic patterns of hand-foot-mouth disease in the People’s Republic of China. Geospat Health. 2013a;7(2):381–90.PubMedCrossRefPubMedCentralGoogle Scholar
  105. 105.
    Liu W, Ji H, et al. Spatiotemporal dynamics of hand-foot-mouth disease and its relationship with meteorological factors in Jiangsu Province, China. PLoS One. 2015;10(6):e0131311.PubMedPubMedCentralCrossRefGoogle Scholar
  106. 106.
    Feng H, Duan G, et al. Time series analysis of hand-foot-mouth disease hospitalization in Zhengzhou: establishment of forecasting models using climate variables as predictors. PLoS One. 2014;9(1):e87916.PubMedPubMedCentralCrossRefGoogle Scholar
  107. 107.
    Cheng Q, Bai L, et al. Ambient temperature, humidity and hand, foot, and mouth disease: a systematic review and meta-analysis. Sci Total Environ. 2018;625:828–36.PubMedCrossRefPubMedCentralGoogle Scholar
  108. 108.
    Du Z, Zhang W, et al. The threshold effects of meteorological factors on hand, foot, and mouth disease (HFMD) in China, 2011. Sci Rep. 2016;6:36351.PubMedPubMedCentralCrossRefGoogle Scholar
  109. 109.
    Sumi A, Toyoda S, Kanou K, Fujimoto T, Mise K, Kohei Y, Koyama A, Kobayashi N. Association between meteorological factors and reported cases of hand, foot, and mouth disease from 2000 to 2015 in Japan. Epidemiol Infect. 2017;145(14):2896–911.PubMedCrossRefPubMedCentralGoogle Scholar
  110. 110.
    Kim BI, Ki H, et al. Effect of climatic factors on hand, foot, and mouth disease in South Korea, 2010-2013. PLoS One. 2016;11(6):e0157500.PubMedPubMedCentralCrossRefGoogle Scholar
  111. 111.
    Nguyen HX, Chu C, et al. Temporal and spatial analysis of hand, foot, and mouth disease in relation to climate factors: a study in the Mekong Delta region, Vietnam. Sci Total Environ. 2017;581-582:766–72.PubMedCrossRefPubMedCentralGoogle Scholar
  112. 112.
    Mirsaeidi M, Motahari H, et al. Climate change and respiratory infections. Ann Am Thorac Soc. 2016;13(8):1223–30.PubMedCrossRefPubMedCentralGoogle Scholar
  113. 113.
    Chen G, Zhang W, et al. The impact of ambient fine particles on influenza transmission and the modification effects of temperature in China: a multi-city study. Environ Int. 2017;98:82–8.PubMedCrossRefPubMedCentralGoogle Scholar
  114. 114.
    Lau SY, Wang X, et al. Identification of meteorological factors associated with human infection with avian influenza A H7N9 virus in Zhejiang Province, China. Sci Total Environ. 2018;644:696–709.PubMedCrossRefPubMedCentralGoogle Scholar
  115. 115.
    Fang LQ, Wang LP, et al. Distribution and risk factors of 2009 pandemic influenza A (H1N1) in mainland China. Am J Epidemiol. 2012;175(9):890–7.PubMedPubMedCentralCrossRefGoogle Scholar
  116. 116.
    Xiao H, Lin XL, et al. Study on sensitivity of climatic factors on influenza A (H1N1) based on classification and regression tree and wavelet analysis. Zhonghua Yu Fang Yi Xue Za Zhi. 2012;46(5):430–5.PubMedPubMedCentralGoogle Scholar
  117. 117.
    Fang LQ, Li XL, et al. Mapping spread and risk of avian influenza a (H7N9) in China. Sci Rep. 2013;3:2722.PubMedPubMedCentralCrossRefGoogle Scholar
  118. 118.
    Li XL, Yang Y, et al. Risk distribution of human infections with avian influenza H7N9 and H5N1 virus in China. Sci Rep. 2015b;5:18610.PubMedPubMedCentralCrossRefGoogle Scholar
  119. 119.
    Hu W, Zhang W, et al. Weather variability and influenza a (H7N9) transmission in Shanghai, China: a Bayesian spatial analysis. Environ Res. 2015a;136:405–12.PubMedCrossRefPubMedCentralGoogle Scholar
  120. 120.
    Qiu J, Li R, et al. Spatiotemporal pattern and risk factors of the reported novel avian-origin influenza A (H7N9) cases in China. Prev Vet Med. 2014;115(3–4):229–37.PubMedCrossRefPubMedCentralGoogle Scholar
  121. 121.
    Zhang Y, Feng C, et al. The impact of temperature and humidity measures on influenza A (H7N9) outbreaks-evidence from China. Int J Infect Dis. 2015;30:122–4.PubMedCrossRefPubMedCentralGoogle Scholar
  122. 122.
    Liu T, Kang M, et al. Independent and interactive effects of ambient temperature and absolute humidity on the risks of avian influenza A (H7N9) infection in China. Sci Total Environ. 2018b;619-620:1358–65.PubMedCrossRefPubMedCentralGoogle Scholar
  123. 123.
    Lu L, Leigh Brown AJ, et al. Quantifying predictors for the spatial diffusion of avian influenza virus in China. BMC Evol Biol. 2017;17(1):16.PubMedPubMedCentralCrossRefGoogle Scholar
  124. 124.
    Liu CM, Lin SH, et al. Temperature drops and the onset of severe avian influenza A H5N1 virus outbreaks. PLoS One. 2007;2(2):e191.PubMedPubMedCentralCrossRefGoogle Scholar
  125. 125.
    Yu H, Alonso WJ, et al. Characterization of regional influenza seasonality patterns in China and implications for vaccination strategies: spatio-temporal modeling of surveillance data. PLoS Med. 2013;10(11):e1001552.PubMedPubMedCentralCrossRefGoogle Scholar
  126. 126.
    Tang JW, Lai FY, et al. Comparison of the incidence of influenza in relation to climate factors during 2000-2007 in five countries. J Med Virol. 2010;82(11):1958–65.PubMedCrossRefPubMedCentralGoogle Scholar
  127. 127.
    Zhang Y, Bambrick H, et al. Using Google trends and ambient temperature to predict seasonal influenza outbreaks. Environ Int. 2018;117:284–91.PubMedCrossRefPubMedCentralGoogle Scholar
  128. 128.
    Iha Y, Higa F, et al. Effect of climatic conditions on epidemic patterns of influenza in Okinawa, Japan, during the pandemic of 2009: surveillance of rapid antigen test results. Jpn J Infect Dis. 2012;65(4):295–300.PubMedCrossRefPubMedCentralGoogle Scholar
  129. 129.
    Jaakkola K, Saukkoriipi A, et al. Decline in temperature and humidity increases the occurrence of influenza in cold climate. Environ Health. 2014;13(1):22.PubMedPubMedCentralCrossRefGoogle Scholar
  130. 130.
    Lin H, Yang L, et al. Time series analysis of Japanese encephalitis and weather in Linyi City, China. Int J Public Health. 2012;57(2):289–96.PubMedCrossRefPubMedCentralGoogle Scholar
  131. 131.
    Qu B, Guan P, Zhou BS, Huang DS. Study on the impact of meteorological factors on Japanese encephalitis incidence. Chin J Epidemiol Infect. 2006;27:179.Google Scholar
  132. 132.
    Bai Y, Xu Z, et al. Regional impact of climate on Japanese encephalitis in areas located near the three gorges dam. PLoS One. 2014;9(1):e84326.PubMedPubMedCentralCrossRefGoogle Scholar
  133. 133.
    Bi P, Zhang Y, et al. Weather variables and Japanese encephalitis in the metropolitan area of Jinan city, China. J Infect. 2007;55(6):551–6.PubMedCrossRefPubMedCentralGoogle Scholar
  134. 134.
    Moizeis RNC, Fernandes T, et al. Chikungunya fever: a threat to global public health. Pathog Glob Health. 2018:1–13.Google Scholar
  135. 135.
    Ren Z, Wang D, et al. Predicting malaria vector distribution under climate change scenarios in China: challenges for malaria elimination. Sci Rep. 2016;6:20604.PubMedPubMedCentralCrossRefGoogle Scholar
  136. 136.
    Hundessa S, Li S, et al. Projecting environmental suitable areas for malaria transmission in China under climate change scenarios. Environ Res. 2018;162:203–10.PubMedCrossRefPubMedCentralGoogle Scholar
  137. 137.
    Zhou Xiaonong YK, Qingbiao H, Leping S, Yang G, Liang Y, Yixin H. Prediction of the impact of climate warming on transmission of Schistosomiasis in China. Chin J Parasitol Parasit Dis. 2004;22(5):2–265.Google Scholar
  138. 138.
    Liang S, Yang C, et al. Re-emerging schistosomiasis in hilly and mountainous areas of Sichuan, China. Bull World Health Organ. 2006;84(2):139.PubMedPubMedCentralCrossRefGoogle Scholar
  139. 139.
    Utzinger J, Zhou XN, et al. Conquering schistosomiasis in China: the long march. Acta Trop. 2005;96(2–3):69–96.PubMedPubMedCentralGoogle Scholar
  140. 140.
    Yu Xianshan TW, Shen J, Jian C. Assessment on the impact of warming climate in winter on schistosomiasis epidemIcs. Chin J Epidemiol. 2004;25(7):575–7.Google Scholar
  141. 141.
    Peng WX, Zhang ZJ, Zhuang JL, Zhou YB, Jang QW. Potential impact of climate changes on spatial distribution of Schistosomiasis in China. Sci Technol Rev. 2006;24(7):58–60.Google Scholar
  142. 142.
    Zhu G, Fan J, et al. Schistosoma japonicum transmission risk maps at present and under climate change in mainland China. PLoS Negl Trop Dis. 2017;11(10):e0006021.PubMedPubMedCentralCrossRefGoogle Scholar
  143. 143.
    Zhang ZY, Xu DZ, et al. Remote sensing and spatial statistical analysis to predict the distribution of Oncomelania hupensis in the marshlands of China. Acta Trop. 2005;96(2):205–12.PubMedCrossRefPubMedCentralGoogle Scholar
  144. 144.
    Zhao A, Wang TJ. A re-adapted Malone Schistosome transmission index model and its application. Geogr Res. 2008;27(2):250–6.Google Scholar
  145. 145.
    Zhou XN, Hu XS, et al. Application of geographic information systems on schistosomiasis surveillance I. Application possibility of prediction model. Chin J Schistosomiasis Control. 1998.Google Scholar
  146. 146.
    Zhou XN, Hu XS, et al. Application of geographical information systems on schistosomiasis surveillance II. Predicting transmission intensity. Chin J Schi Contl. 1999;2:66–70.Google Scholar
  147. 147.
    Yang K, Pan J, et al. Projection of the transmission scale and intensity of Schistosomiasis in China under A2 and B2 climate change scenarios. Adv Clim Chang Res. 2010b;6(04):248–53.Google Scholar
  148. 148.
    Zhao Q, Li S, et al. Modeling the present and future incidence of pediatric hand, foot, and mouth disease associated with ambient temperature in mainland China. Environ Health Perspect. 2018;126(4):047010.PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Xiaobo Liu
    • 1
  • Jimin Sun
    • 2
  • Xiaodong Liu
    • 3
  • Jingchun Fan
    • 1
    • 4
  • Yanlin Niu
    • 1
    • 5
  • Lingling Lang
    • 6
  • Qiyong Liu
    • 1
    Email author
  1. 1.State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and Prevention, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, WHO Collaborating Centre for Vector Surveillance and ManagementBeijingChina
  2. 2.Zhejiang Provincial Center for Disease Control and PreventionHangzhouChina
  3. 3.Shandong Provincial Center for Disease Control and PreventionJinanChina
  4. 4.School of Public HealthGansu University of Chinese MedicineLanzhouChina
  5. 5.Beijing Centers for Disease Prevention and Control, Beijing Centers for Disease Preventive Medical ResearchBeijingChina
  6. 6.Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and PreventionGuangzhouChina

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