Effect of climate change and economic development on hemorrhagic fever with renal syndrome

doi:10.17520/biods.2020264

Abstract

Abstract:

Hemorrhagic fever with renal syndrome (HFRS) is a natural focus disease transmitted via contact with infected rodents and is a global public health threat. Using statistical models and wavelet analysis, we analyzed the effects that rodent density, climate and economic variations on HFRS cases in the Huyi District of Xi’an City, Shaanxi Province from 1984-2016. We found that the outbreak history could be divided into two periods, each of which with a different major reservoir. We found that Rattus norvegicus may have replaced Apodemus sagrarius as the major reservoir of HFRS around 2002, using wavelet analysis. Generalized additive models show that incidence of HFRS was positively associated with Apodemus sagrarius densities from 1984 to 2001 (F_2.06,9.02= 102.415, P < 0.01) and with Rattus norvegicus densities from 2002 and 2016 (F_1.67,9.02= 73.929, P < 0.01). We also found that the shift in major reservoir for HFRS was associated with local climate variation (quantified by annual average temperature), and economic activity (quantified by gross domestic product, GDP). We found negative correlations between temperature and incidence of HFRS (F_2.93,9.02= 12.164, P < 0.01) and between GDP and incidence of HFRS (F_1.70,9.02= 2.917, P < 0.05). We used a structural equation model to demonstrate this shift in reservoir through direct and indirect pathways, and found that temperature had a direct negative effect on HFRS incidence and an indirect positive effect via Rattus norvegicus. GDP has a direct negative effect on HFRS incidence. This study has demonstrated how changes in climate and economic factors have affected outbreak of HFRS. Knowledge of these effects can contribute in helping develop better strategies for controlling the spread of HFRS.

Key words: climate change, economic development, hemorrhagic fever with renal syndrome (HFRS), rodent density, generalized additive model, structural equation model, wavelet analysis

Xiang Hou, Tuo Feng, Ning Han, Jing Wang, Xiaoning Chen, Xiaolei An, Lei Xu, Qiyong Liu, Gang Chang. Effect of climate change and economic development on hemorrhagic fever with renal syndrome[J]. Biodiv Sci, 2020, 28(10): 1229-1237.

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URL: https://www.biodiversity-science.net/EN/10.17520/biods.2020264

https://www.biodiversity-science.net/EN/Y2020/V28/I10/1229

Figures/Tables 4

Fig. 1 Yearly time series of cases of hemorrhagic fever with renal syndrome (HFRS), temperature, gross domestic product (GDP) and rodent density in the Huyi District of Xi’an City, Shaanxi Province from 1984 to 2016

Fig. 2 Wavelet coherence between the density of Apodemus agrarius and Rattus norvegicus and hemorrhagic fever with renal syndrome (HFRS) cases. Arrows’ direction indicates periodic synchronicity between the former and the latter, arrows pointing to the right mean that the former and the latter are in phase; arrows pointing to the left mean that the former and the latter are in anti-phase; arrows pointing up mean that the latter leads the former by π/2; arrows pointing down mean that the former leads the latter by π/2; π/2 indicates half of the period; arrows’ region indicates significance levels P < 0.05, color indicates the coefficient of coherence.

Fig. 3 The effects of rodent density, climate and economic factors on the cases of hemorrhagic fever with renal syndrome (HFRS) by generalized additive models (Shaded areas are 95% confidence bands). (A) The effects of Apodemus agrarius density on the cases of HFRS from 1984-2001; (B) The effects of Rattus norvegicus density on the cases of HFRS from 2002-2016; (C)The effects of annual average temperature on the cases of HFRS; (D) The effects of local gross domestic product (GDP) in previous year on the cases of HFRS.

Fig. 4 Structural equation model analysis revealed direct and indirect climatic and economic effects on hemorrhagic fever with renal syndrome (HFRS) cases. Numbers indicate ecological effects and standardized coefficients, black solid line indicates statistically significant levels P < 0.05 pathways, and black dash line indicates statistically no significant levels P > 0.05 pathways.

References 42

[1]	Adda J ( 2016) Economic activity and the spread of viral diseases: Evidence from high frequency data. The Quarterly Journal of Economics, 131, 891-941.
[2]	Al-Shorbaji F, Roche B, Gozlan R, Britton R, Andreou D ( 2016) The consequences of reservoir host eradication on disease epidemiology in animal communities. Emerging Microbes and Infections, 5, e46. DOI URL PMID
[3]	Bai YT, Xu ZG, Lu B, Sun QH, Tang WG, Liu XB, Yang WZ, Xu XY, Liu QY ( 2015) Effects of climate and rodent factors on hemorrhagic fever with renal syndrome in Chongqing, China, 1997-2008. PLoS ONE, 10, e0133218. DOI URL PMID
[4]	Bayram H, Bauer AK, Abdalati W, Carlsten C, Pinkerton KE, Thurston GD, Balmes JR, Takaro TK ( 2017) Environment, global climate change, and cardiopulmonary health. American Journal of Respiratory and Critical Care Medicine, 195, 718-724. DOI URL PMID
[5]	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. International Journal of Epidemiology, 31, 189-193. DOI URL PMID
[6]	Clement J, Vercauteren J, Verstraeten WW, Ducoffre G, Barrios JM, Vandamme AM, Maes P, Ranst MV ( 2009) Relating increasing Hantavirus incidences to the changing climate: The mast connection. International Journal of Health Geographics, 8, 1-11. DOI URL PMID
[7]	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. Emerging Infectious Diseases, 5, 87-94. URL PMID
[8]	Ge L, Zhao YL, Zhou K, Mu XM, Yu HB, Wang YF, Ning W, Hong F, Guo LQ, Huo XX ( 2016) Spatio-temporal pattern and influencing factors of hemorrhagic fever with renal syndrome (HFRS) in Hubei Province (China) between 2005 and 2014. PLoS ONE, 11, e0167836.
[9]	Guan P, Huang DS, He M, Shen TF, Guo JQ, Zhou BS ( 2009) 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 Infectious Diseases, 9, 109.
[10]	Habtemariam LT, Kassa GA, Gandorfer M ( 2017) Impact of climate change on farms in smallholder farming systems: Yield impacts, economic implications and distributional effects. Agricultural Systems, 152, 58-66.
[11]	Hou X, Liu KK, Liu XB, Chang G, Xu L, Liu QY ( 2019) Nonlinear effects of climate factors on dengue epidemic in Guangdong Province, China. Chinese Journal of Vector Biology and Control, 30, 25-30. (in Chinese with English abstract)
	[ 侯祥, 刘可可, 刘小波, 常罡, 许磊, 刘起勇 ( 2019) 气候因素对广东省登革热流行影响的非线性效应. 中国媒介生物学及控制杂志, 30, 25-30.]
[12]	Kallio ER, Begon M, Henttonen H, Koskela E, Mappes T, Vaheri A, Vapalahti O ( 2009) Cyclic Hantavirus epidemics in humans-predicted by rodent host dynamics. Epidemics, 1, 101-107. URL PMID
[13]	Kariwa H, Yoshimatsu K, Arikawa J ( 2007) Hantavirus infection in East Asia. Comparative Immunology Microbiology and Infectious Diseases, 30, 341-356.
[14]	Li Q, Zhao WN, Wei YM, Han X, Han ZY, Zhang YB, Qi SX, Xu YG ( 2014) Analysis of incidence and related factors of hemorrhagic fever with renal syndrome in Hebei Province, China. PLoS ONE, 9, e101348. DOI URL PMID
[15]	Liang WF, Gu X, Li X, Zhang KJ, Wu KJ, Pang MM, Dong JH, Merrill HR, Hu T, Liu K ( 2018) Mapping the epidemic changes and risks of hemorrhagic fever with renal syndrome in Shaanxi Province, China, 2005-2016. Scientific Reports, 8, 749.
[16]	Lin HL, Zhang ZT, Lu L, Liu QY ( 2014) Meteorological factors are associated with hemorrhagic fever with renal syndrome in Jiaonan County, China, 2006-2011. International Journal of Biometeorology, 58, 1031-1037. URL PMID
[17]	Liu XD, Jiang BF, Gu WD, Liu QY ( 2011) Temporal trend and climate factors of hemorrhagic fever with renal syndrome epidemic in Shenyang City, China. BMC Infectious Diseases, 11, 331. DOI URL PMID
[18]	McMichael AJ, Woodruff RE, Hales S ( 2006) Climate change and human health: Present and future risks. Lancet, 367, 859-869.
[19]	Nichol ST, Spiropoulou C, Morzunov S, Rollin PE, Ksiazek TG, Feldmann H, Sanchez A, Childs J, Zaki S, Peters C ( 1993) Genetic identification of a Hantavirus associated with an outbreak of acute respiratory illness. Science, 262, 914-917.
[20]	Pinkerton KE, Rom WN ( 2014) Global Climate Change and Public Health. Springer, London.
[21]	Rosseel Y ( 2012) lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1-36.
[22]	Sargianou M, Watson DC, Chra P, Papa A, Panos G ( 2012) Hantavirus infections for the clinician: From case presentation to diagnosis and treatment. Critical Reviews in Microbiology, 38, 317-329. DOI URL PMID
[23]	Steiger JH ( 1990) Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25, 173-180.
[24]	Stige LC, Ottersen G, Brander K, Chan KS, Stenseth NC ( 2006) Cod and climate: Effect of the North Atlantic Oscillation on recruitment in the North Atlantic. Marine Ecology Progress Series, 325, 227-241.
[25]	Sun J, Wang JN, Wei YQ, Li YR, Liu M ( 2016) The haze nightmare following the economic boom in China: Dilemma and tradeoffs. International Journal of Environmental Research and Public Health, 13, 1-12.
[26]	Tian HY, Hu SX, Cazelles B, Chowell G, Gao LD, Laine M, Li YP, Yang HS, Li YD, Yang QQ, Tong X, Huang R, Bjornstad ON, Xiao H, Stenseth NC ( 2018) Urbanization prolongs Hantavirus epidemics in cities. Proceedings of the National Academy of Sciences, USA, 115, 4707-4712.
[27]	Tian HY, Yu PB, Bjørnstad ON, Cazelles B, Yang J, Tan H, Huang SQ, Cui YJ, Dong L, Ma CF, Ma CA, Zhou S, Laine M, Wu XX, Zhang YY, Wang JJ, Yang RF, Stenseth NC, Xu B ( 2017a) Anthropogenically driven environmental changes shift the ecological dynamics of hemorrhagic fever with renal syndrome. PLoS Pathogens, 13, e1006198. URL PMID
[28]	Tian HY, Yu PB, Cazelles B, Xu L, Tan H, Yang J, Huang SQ, Xu B, Cai J, Ma CF, Wei J, Li S, Qu JH, Laine M, Wang JJ, Tong SL, Stenseth NC, Xu B ( 2017b) Interannual cycles of Hantaan virus outbreaks at the human-animal interface in Central China are controlled by temperature and rainfall. Proceedings of the National Academy of Sciences, USA, 114, 8041-8046.
[29]	Tian HY, Yu PB, Luis AD, Bi P, Cazelles B, Laine M, Huang SQ, Ma CF, Zhou S, Wei J, Li S, Lu XL, Qu JH, Dong JH, Tong SL, Wang JJ, Grenfell B, Xu B ( 2015) Changes in rodent abundance and weather conditions potentially drive hemorrhagic fever with renal syndrome outbreaks in Xi’an, China, 2005-2012. PLOS Neglected Tropical Diseases, 9, e0003530.
[30]	Wang T, Zhou YP, Wang L, Huang ZS, Cui F, Zhai SY ( 2015) Using autoregressive integrated moving average model to predict the incidence of hemorrhagic fever with renal syndrome in Zibo, China, 2004-2014. Japanese Journal of Infectious Diseases, 69, 279-284.
[31]	Watson DC, Sargianou M, Papa A, Chra P, Starakis I, Panos G ( 2014) Epidemiology of Hantavirus infections in humans: A comprehensive, global overview. Critical Reviews in Microbiology, 40, 261-272.
[32]	Wood SN ( 2006) Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC, Boca Raton.
[33]	Xiao D, Wu KJ, Tan X, Le J, Li HT, Yan YP, Xu ZK ( 2015) Modeling and predicting hemorrhagic fever with renal syndrome trends based on meteorological factors in Hu County, China. PLoS ONE, 10, e0123166. URL PMID
[34]	Xiao D, Wu KJ, Tan X, Yan TC, Li HT, Yan YP ( 2014 a) The impact of the vaccination program for hemorrhagic fever with renal syndrome in Hu County, China. Vaccine, 32, 740-745. DOI URL PMID
[35]	Xiao H, Tian HY, Gao LD, Liu HN, Duan LS, Basta N, Cazelles B, Li XJ, Lin XL, Wu HW, Chen BY, Yang HS, Xu B, Grenfell B ( 2014 b) Animal reservoir, natural and socioeconomic variations and the transmission of hemorrhagic fever with renal syndrome in Chenzhou, China, 2006-2010. PLoS Neglected Tropical Diseases, 8, e2615. URL PMID
[36]	Xiao H, Gao LD, Li XJ, Lin XL, Dai XY, Zhu PJ, Chen BY, Zhang XX, Zhao J, Tian HY ( 2013 a) Environmental variability and the transmission of haemorrhagic fever with renal syndrome in Changsha, People’s Republic of China. Epidemiology and Infection, 141, 1867-1875. DOI URL PMID
[37]	Xiao H, Tian HY, Cazelles B, Li XJ, Tong SL, Gao LD, Qin JX, Lin XL, Liu HN, Zhang XX ( 2013 b) Atmospheric moisture variability and transmission of hemorrhagic fever with renal syndrome in Changsha City, Mainland China, 1991-2010. PLoS Neglected Tropical Diseases, 7, e2260. URL PMID
[38]	Xu L, Liu QY, Stige LC, Ari TB, Fang XY, Chan KS, Wang SC, Stenseth NC, Zhang ZB ( 2011) Nonlinear effect of climate on plague during the third pandemic in China. Proceedings of the National Academy of Sciences, USA, 108, 10214-10219.
[39]	Yan X, Yang YY ( 2018) RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods. Behavior Research Methods, 51, 409-428. DOI URL PMID
[40]	Zhang MW, Wang Y, Li B, Chen AG ( 2003) The rodent pest control of the social-economical-natural complex ecosystem. Acta Theriologica Sinica, 23, 250-258. (in Chinese with English abstract)
	[ 张美文, 王勇, 李波, 陈安国 ( 2003) “社会-经济-自然复合生态系统”中的鼠害治理. 兽类学报, 23, 250-258.]
[41]	Zhang YZ, Zou Y, Fu ZF, Plyusnin A ( 2010) Hantavirus infections in humans and animals, China. Emerging Infectious Diseases, 16, 1195-1203. DOI URL PMID
[42]	Zhang ZB, Cazelles B, Tian HD, Stige LC, Bräuning A, Stenseth NC ( 2009) Periodic temperature-associated drought/flood drives locust plagues in China. Proceedings of the Royal Society B: Biological Ences, 276, 823-831.