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Spatiotemporal trends of hemorrhagic fever with renal syndrome (HFRS) in China under climate variation.

Yuchen WangChutian ZhangJing GaoZiqi ChenZhao LiuJianbin HuangYidan ChenZhichao LiNan ChangYuxin TaoHui TangXuejie GaoYing XuCan WangDong LiXiaobo LiuJingxiang PanWenjia CaiPeng GongYong LuoWannian LiangQiyong LiuNils Christian StensethRuifu YangLei Xu
Published in: Proceedings of the National Academy of Sciences of the United States of America (2024)
Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease caused by the rodent-transmitted orthohantaviruses (HVs), with China possessing the most cases globally. The virus hosts in China are Apodemus agrarius and Rattus norvegicus , and the disease spread is strongly influenced by global climate dynamics. To assess and predict the spatiotemporal trends of HFRS from 2005 to 2098, we collected historical HFRS data in mainland China (2005-2020), historical and projected climate and population data (2005-2098), and spatial variables including biotic, environmental, topographical, and socioeconomic. Spatiotemporal predictions and mapping were conducted under 27 scenarios incorporating multiple integrated representative concentration pathway models and population scenarios. We identify the type of magistral HVs host species as the best spatial division, including four region categories. Seven extreme climate indices associated with temperature and precipitation have been pinpointed as key factors affecting the trends of HFRS. Our predictions indicate that annual HFRS cases will increase significantly in 62 of 356 cities in mainland China. Rattus regions are predicted to be the most active, surpassing Apodemus and Mixed regions. Eighty cities are identified as at severe risk level for HFRS, each with over 50 reported cases annually, including 22 new cities primarily located in East China and Rattus regions after 2020, while 6 others develop new risk. Our results suggest that the risk of HFRS will remain high through the end of this century, with Rattus norvegicus being the most active host, and that extreme climate indices are significant risk factors. Our findings can inform evidence-based policymaking regarding future risk of HFRS.
Keyphrases
  • climate change
  • risk factors
  • machine learning
  • case report
  • risk assessment
  • electronic health record
  • big data
  • human health
  • deep learning
  • early onset