The effect of particulate matter 2.5 on seasonal influenza transmission in 1,330 counties, China: A Bayesian spatial analysis based on Köppen Geiger climate zones classifications

Abstract

Previous research has linked seasonal influenza transmission with particulate matters (PM_2.5). However, the effect of PM_2.5 on seasonal influenza transmission varied by region. This study aims explore how PM_2.5 influenced seasonal influenza transmission in the elderly across 1330 counties in two Köppen Geiger climate zones in China, incorporating the socio-economic factors to enhance climate-driven early warning systems (EWS) for influenza. Data included weekly 2015–2019 influenza cases in those aged >65 from China's national influenza surveillance system for 1330 counties in two Köppen Geiger climate zones: Temperate, Hot Summer with Dry Winter (Cwa) and No Dry Season (Cfa). PM_2.5 data from 2015 to 2019 were sourced from Copernicus Atmosphere Monitoring Services. Additional data on floating population, population density and Gross Domestic Product (GDP) per capita were collected from pertinent departments. A Bayesian spatial autoregressive model assessed the association of PM_2.5 and influenza transmission after adjustment of socio-economic factors. Our research results showed PM_2.5 (per 1 μg/m³ increase) was linked to increased influenza transmission in the Cwa zone during winter season (Relative Risk (RR) = 1.023, 95% Credible Interval (CI):1.008–1.040) but not in the Cfa winter (RR = 1.003, 95% CI: 0.992–1.015). Floating population significantly enhanced transmission in both zones (highest RR = 1.362, 95% CI:1.181–1.583), while GDP per capita growth was associated with reduced transmission risk (highest RR = 0.619, 95% CI: 0.445–0.861). The study identifies PM_2.5 as a significant factor influencing influenza transmission in the elderly, with effects varying by climate zone, suggesting the need to incorporate PM_2.5 and socio-economic factors into seasonal influenza EWS.