tetano
Editor, Senior Moderator
Glob Health Res Policy
. 2026 Apr 19;11(1):143-152.
doi: 10.1016/j.ghrp.2026.04.002. eCollection 2026 Dec.
Danyang Wang 1 , Na Li 1 , Xinxin Li 1 , Xiaoxu Wu 2 , Lu Zhao 1 , Guoying Dong 1 , Jie Yin 3
Affiliations Expand
Background: Influenza remains a recurring threat to global health with pandemic potential, yet the predictive causal associations of climate factors and air pollutants on its transmission remain not fully quantified. Thus, this study aimed to quantify the nonlinear and delayed effects of these factors, as well as their predictive causal signals for global influenza epidemics.
Methods: This study was a longitudinal time-series analysis conducted across 48 countries from 2022 to 2024. Weekly influenza positivity rates were derived from the FluNet database and integrated with climate variables and air pollutants. Spatiotemporal Bayesian models were constructed separately for the Northern and Southern Hemispheres to quantify the nonlinear and lagged effects of climate variables and air pollutants while accounting for socioeconomic conditions. Predictive causal relationships were further assessed using convergent cross mapping (CCM) to identify potential environmental drivers for influenza transmission.
Results: In Northern Hemisphere countries, the highest odds ratio (OR) for influenza was 1.74 (95% CI: 1.34-2.26) at weekly cumulative SO2 concentrations of 3100 μg/m3 with a 1.6-week lag, and 2.36 (95% CI: 1.53-3.64) at a temperature of -27 °C with a 2.2-week lag. In Southern Hemisphere countries, the highest OR was 1.72 (95% CI: 0.65-4.58) at weekly cumulative organic carbon (OC) concentrations of 5800 μg/m3 with a 3.2-week lag, and 1.31 (95% CI: 1.00-1.71) corresponding to a temperature of 14 °C with a lag of 2 weeks. CCM analysis identified PM2.5 in dust and wind speed as the dominant drivers among air pollutants and climate factors, respectively. PM2.5 in dust influenced influenza in 19 countries, while wind speed did so in 15 countries across the study area.
Conclusions: Climate variables and air pollutants exhibited nonlinear and lagged effects on influenza transmission, with key air pollutants varying across hemispheres. Predictive causal signals of selected climate variables were further modulated by specific socioeconomic conditions. These findings highlight the importance of integrating environmental and socioeconomic conditions, providing practical guidance for implementing targeted influenza control strategies.
Keywords: Air pollutant; Climate factor; Influenza; Predictive causal signal.
. 2026 Apr 19;11(1):143-152.
doi: 10.1016/j.ghrp.2026.04.002. eCollection 2026 Dec.
Delayed and nonlinear associations and predictive causal signals between climate factors, air pollutants, and influenza epidemics in 48 countries
Danyang Wang 1 , Na Li 1 , Xinxin Li 1 , Xiaoxu Wu 2 , Lu Zhao 1 , Guoying Dong 1 , Jie Yin 3
Affiliations Expand
- PMID: 42965015
- PMCID: PMC13721013
- DOI: 10.1016/j.ghrp.2026.04.002
Abstract
Background: Influenza remains a recurring threat to global health with pandemic potential, yet the predictive causal associations of climate factors and air pollutants on its transmission remain not fully quantified. Thus, this study aimed to quantify the nonlinear and delayed effects of these factors, as well as their predictive causal signals for global influenza epidemics.
Methods: This study was a longitudinal time-series analysis conducted across 48 countries from 2022 to 2024. Weekly influenza positivity rates were derived from the FluNet database and integrated with climate variables and air pollutants. Spatiotemporal Bayesian models were constructed separately for the Northern and Southern Hemispheres to quantify the nonlinear and lagged effects of climate variables and air pollutants while accounting for socioeconomic conditions. Predictive causal relationships were further assessed using convergent cross mapping (CCM) to identify potential environmental drivers for influenza transmission.
Results: In Northern Hemisphere countries, the highest odds ratio (OR) for influenza was 1.74 (95% CI: 1.34-2.26) at weekly cumulative SO2 concentrations of 3100 μg/m3 with a 1.6-week lag, and 2.36 (95% CI: 1.53-3.64) at a temperature of -27 °C with a 2.2-week lag. In Southern Hemisphere countries, the highest OR was 1.72 (95% CI: 0.65-4.58) at weekly cumulative organic carbon (OC) concentrations of 5800 μg/m3 with a 3.2-week lag, and 1.31 (95% CI: 1.00-1.71) corresponding to a temperature of 14 °C with a lag of 2 weeks. CCM analysis identified PM2.5 in dust and wind speed as the dominant drivers among air pollutants and climate factors, respectively. PM2.5 in dust influenced influenza in 19 countries, while wind speed did so in 15 countries across the study area.
Conclusions: Climate variables and air pollutants exhibited nonlinear and lagged effects on influenza transmission, with key air pollutants varying across hemispheres. Predictive causal signals of selected climate variables were further modulated by specific socioeconomic conditions. These findings highlight the importance of integrating environmental and socioeconomic conditions, providing practical guidance for implementing targeted influenza control strategies.
Keywords: Air pollutant; Climate factor; Influenza; Predictive causal signal.