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Investigating the Effects of Food Available and Climatic Variables on the Animal Host Density of Hemorrhagic Fever with Renal Syndrome in Changsha, China

Author

Listed:
  • Hong Xiao
  • Hai-Ning Liu
  • Li-Dong Gao
  • Cun-Rui Huang
  • Zhou Li
  • Xiao-Ling Lin
  • Bi-Yun Chen
  • Huai-Yu Tian

Abstract

Background: The transmission of hemorrhagic fever with renal syndrome (HFRS) is influenced by population dynamics of its main host, rodents. It is therefore important to better understand rodents’ characteristic in epidemic areas. Methodology/Principal Findings: We examined the potential impact of food available and climatic variability on HFRS rodent host and developed forecasting models. Monthly rodent density of HFRS host and climate data in Changsha from January 2004 to December 2011 were obtained. Monthly normalized difference vegetation index (NDVI) and temperature vegetation dryness index (TVDI) for rice paddies were extracted from MODIS data. Cross-correlation analysis were carried out to explore correlation between climatic variables and food available with monthly rodent data. We used auto-regressive integrated moving average model with explanatory variables to examine the independent contribution of climatic variables and food supply to rodent density. The results indicated that relative rodent density of HFRS host was significantly correlated with monthly mean temperatures, monthly accumulative precipitation, TVDI and NDVI with lags of 1–6 months. Conclusions/Significance: Food available plays a significant role in population fluctuations of HFRS host in Changsha. The model developed in this study has implications for HFRS control and prevention.

Suggested Citation

  • Hong Xiao & Hai-Ning Liu & Li-Dong Gao & Cun-Rui Huang & Zhou Li & Xiao-Ling Lin & Bi-Yun Chen & Huai-Yu Tian, 2013. "Investigating the Effects of Food Available and Climatic Variables on the Animal Host Density of Hemorrhagic Fever with Renal Syndrome in Changsha, China," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-9, April.
    • Handle: RePEc:plo:pone00:0061536
    DOI: 10.1371/journal.pone.0061536
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    Cited by:

    1. Junyu He & George Christakos & Jiaping Wu & Piotr Jankowski & Andreas Langousis & Yong Wang & Wenwu Yin & Wenyi Zhang, 2019. "Probabilistic logic analysis of the highly heterogeneous spatiotemporal HFRS incidence distribution in Heilongjiang province (China) during 2005-2013," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 13(1), pages 1-28, January.
    2. Guo-hua Ye & Mirxat Alim & Peng Guan & De-sheng Huang & Bao-sen Zhou & Wei Wu, 2021. "Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-13, March.
    3. Shujuan Li & Lingli Zhu & Lidan Zhang & Guoyan Zhang & Hongyan Ren & Liang Lu, 2023. "Urbanization-Related Environmental Factors and Hemorrhagic Fever with Renal Syndrome: A Review Based on Studies Taken in China," IJERPH, MDPI, vol. 20(4), pages 1-20, February.
    4. Shujuan Li & Hongyan Ren & Wensheng Hu & Liang Lu & Xinliang Xu & Dafang Zhuang & Qiyong Liu, 2014. "Spatiotemporal Heterogeneity Analysis of Hemorrhagic Fever with Renal Syndrome in China Using Geographically Weighted Regression Models," IJERPH, MDPI, vol. 11(12), pages 1-19, November.
    5. Qinghui An & Jianghua Zheng & Jingyun Guan & Jianguo Wu & Jun Lin & Xifeng Ju & Rui Wu, 2023. "Predicting the Effects of Future Climate Change on the Potential Distribution of Eolagurus luteus in Xinjiang," Sustainability, MDPI, vol. 15(10), pages 1-15, May.

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