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<p>Historically, Yichun City has borne the highest burden of Hemorrhagic Fever with Renal Syndrome (HFRS) in China, with incidence exhibiting marked fluctuations between 2005 and 2023. Despite this, HFRS-weather associations in Yichun remain poorly characterized. We analyzed weekly time-series data on HFRS cases and meteorological factors (mainly temperature, humidity, and precipitation) from 2005 to 2023 using random forest models to identify optimal lags, distributed lag non-linear models, and generalized additive models. We identified a symmetric unimodal relationship for temperature (peak risk at 13.66&#xa0;°C; RR = 1.77, 95% CI: 0.92–3.39), a right-skewed pattern for relative humidity, and a distinct inverse J-shaped pattern for precipitation. Lag-response analysis indicated that the strongest associations occurred at 26 weeks for temperature, 12 weeks for relative humidity, and 16 weeks for precipitation. Temperature showed complex delayed responses with both protective and risk-increasing phases (~ 4–6 months), while precipitation and humidity primarily exhibited protective effects. Formal interaction testing revealed no statistically significant two-way or three-way interactions among meteorological variables (all <i>P</i> &gt; 0.05), indicating that each factor operates independently rather than synergistically. These findings offer actionable evidence for designing targeted, climate-informed HFRS prevention strategies in Yichun and similar high-burden regions.</p>

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The effects of meteorological factors on hemorrhagic fever with renal syndrome in Yichun, China: a major Apodemus-type endemic city

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月20日

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  • Shu Yang,
  • Xiaobo Liu,
  • Xin Qi,
  • Hui Li,
  • Guihua Zhuang

摘要

Historically, Yichun City has borne the highest burden of Hemorrhagic Fever with Renal Syndrome (HFRS) in China, with incidence exhibiting marked fluctuations between 2005 and 2023. Despite this, HFRS-weather associations in Yichun remain poorly characterized. We analyzed weekly time-series data on HFRS cases and meteorological factors (mainly temperature, humidity, and precipitation) from 2005 to 2023 using random forest models to identify optimal lags, distributed lag non-linear models, and generalized additive models. We identified a symmetric unimodal relationship for temperature (peak risk at 13.66 °C; RR = 1.77, 95% CI: 0.92–3.39), a right-skewed pattern for relative humidity, and a distinct inverse J-shaped pattern for precipitation. Lag-response analysis indicated that the strongest associations occurred at 26 weeks for temperature, 12 weeks for relative humidity, and 16 weeks for precipitation. Temperature showed complex delayed responses with both protective and risk-increasing phases (~ 4–6 months), while precipitation and humidity primarily exhibited protective effects. Formal interaction testing revealed no statistically significant two-way or three-way interactions among meteorological variables (all P > 0.05), indicating that each factor operates independently rather than synergistically. These findings offer actionable evidence for designing targeted, climate-informed HFRS prevention strategies in Yichun and similar high-burden regions.