Background While many studies have explored the correlation between environmental factors and influenza, research on their potential causal associations remains limited. Further, the impact of temperature changes between neighboring days (TCN) has not been thoroughly investigated. Methods Influenza-like illness (ILI) data, meteorological indicators, and air pollutant levels were collected in Jinan, China (2015–2019). Gradient boosting decision trees (GBDT) were used to identify key environmental variables. The distributed lag nonlinear models (DLNM) and empirical dynamic modeling (EDM) framework were then applied to explore their nonlinear associations with and potential causal effects on influenza infection. Subgroup analysis was also performed by different age groups. Results GBDT identified absolute humidity (AH), atmospheric pressure (AP), sulfur dioxide (SO2), and ozone (O3) as key factors, along with TCN as a key variable of interest. DLNM results revealed J-shaped and bimodal exposure–response relationships for TCN and AH, respectively, with increased relative risks (RRs) under low AH. SO2 was positively associated with influenza risk. The highest RRs were 1.190 (95% confidence interval (CI): 1.070–1.322) observed at 1012 hPa for AP and 3.373 (95% CI: 2.650–4.294) at 125 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\upmu\)</EquationSource> </InlineEquation>g/m3 for O3. EDM results indicated that long-lag AP and SO2 may have a potential driving effect on influenza incidence. Positive effects on influenza were observed when TCN > <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(-\)</EquationSource> </InlineEquation>5 °C, AP > 1100 hPa, and across the full SO2 range. Children aged 0–4 were more sensitive to AP and SO2, while the aged 5–59 were more affected by TCN, AH, and O3. Conclusion This study demonstrated that both the DLNM and EDM methods consistently revealed the complex and nonlinear effects of specific environmental factors on influenza infection. Specifically, TCN > 5 ℃, AP > 1100 hPa, and SO2 > 60 <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\upmu\)</EquationSource> </InlineEquation>g/m3 were associated with increased risk of influenza infection. Children aged 0–4 years and individuals aged 5–59 years exhibit different susceptibility patterns to environmental factors. These findings can inform public health strategies for influenza prevention, particularly in the context of increasing air pollution and climate variability.

Nonlinear and potential driving impacts of meteorological and air pollution factors on influenza-like illness in Jinan, China

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2025年12月4日

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Hui Zuo,
Qi Gao,
Michael Tong,
Shujuan Huang,
Chuanlong Cheng,
Shufen Li,
Kemeng Liang,
Caiyun Chang,
Xiujun Li

摘要

Background

While many studies have explored the correlation between environmental factors and influenza, research on their potential causal associations remains limited. Further, the impact of temperature changes between neighboring days (TCN) has not been thoroughly investigated.

Methods

Influenza-like illness (ILI) data, meteorological indicators, and air pollutant levels were collected in Jinan, China (2015–2019). Gradient boosting decision trees (GBDT) were used to identify key environmental variables. The distributed lag nonlinear models (DLNM) and empirical dynamic modeling (EDM) framework were then applied to explore their nonlinear associations with and potential causal effects on influenza infection. Subgroup analysis was also performed by different age groups.

Results

GBDT identified absolute humidity (AH), atmospheric pressure (AP), sulfur dioxide (SO₂), and ozone (O₃) as key factors, along with TCN as a key variable of interest. DLNM results revealed J-shaped and bimodal exposure–response relationships for TCN and AH, respectively, with increased relative risks (RRs) under low AH. SO₂ was positively associated with influenza risk. The highest RRs were 1.190 (95% confidence interval (CI): 1.070–1.322) observed at 1012 hPa for AP and 3.373 (95% CI: 2.650–4.294) at 125 \(\upmu\) g/m³ for O₃. EDM results indicated that long-lag AP and SO₂ may have a potential driving effect on influenza incidence. Positive effects on influenza were observed when TCN > \(-\) 5 °C, AP > 1100 hPa, and across the full SO₂ range. Children aged 0–4 were more sensitive to AP and SO₂, while the aged 5–59 were more affected by TCN, AH, and O₃.

Conclusion

This study demonstrated that both the DLNM and EDM methods consistently revealed the complex and nonlinear effects of specific environmental factors on influenza infection. Specifically, TCN > 5 ℃, AP > 1100 hPa, and SO₂ > 60 \(\upmu\) g/m³ were associated with increased risk of influenza infection. Children aged 0–4 years and individuals aged 5–59 years exhibit different susceptibility patterns to environmental factors. These findings can inform public health strategies for influenza prevention, particularly in the context of increasing air pollution and climate variability.