The dataset contained 29,246 laboratory-confirmed cases: influenza A(H1N1)pdm09 accounted for 28.7%, A(H3N2) for 39.6%, and influenza B for 31.7%. To assess the relationships between weather and each subtype, the team combined a two-stage random-forest analysis with univariate assessments of optimal lag structures and multivariate logistic regression models. The random forest identified potentially important meteorological variables, while the regression models estimated their independent associations after accounting for other included factors.

A final joint-effect analysis examined whether variables acted synergistically or antagonistically. The subtype-specific findings nevertheless diverged. Influenza A(H1N1)pdm09 was sensitive to low temperature, low humidity, and high wind speed.

The risk association with high wind speed was reversed by high temperature, showing that the relationship between wind and the subtype depended on the surrounding conditions. Influenza A(H3N2), by contrast, was primarily associated with higher solar radiation and higher temperature; the two factors displayed a synergistic protective association, meaning their combined modeled relationship with lower A(H3N2) risk exceeded the pattern expected from their separate associations. Influenza B was most closely associated with a humidity–pressure system, in which high humidity offset the risk effect associated with high pressure.

These interactions make individual meteorological variables difficult to interpret outside the combinations in which they occurred. Together, the results indicate that influenza subtypes can respond differently to the same local weather environment and that relationships among multiple variables may modify one another. The authors concluded that meteorological early-warning models should account for subtype specificity and multifactor interactions rather than rely on one universal indicator.

The study’s scope nevertheless limits generalization: all analyzed cases came from one province, the surveillance data represented laboratory-confirmed infections rather than all respiratory illness, and the observational analysis could not fully separate meteorological associations from other influences on transmission. The findings therefore provide a differentiated account of influenza–weather relationships in Shanxi during 2009–2024, excluding the COVID-19 non-pharmaceutical-intervention period, rather than a universal rule for influenza seasonality or prevention.

Subject of Research: Subtype-specific meteorological associations with laboratory-confirmed influenza in Shanxi Province

Article Title: Differential meteorological drivers of influenza subtypes in Shanxi Province, China

Article References: Zhang, R., Zhang, R., Ning, Y., Guo, J., & Yu, H. (2026). Differential meteorological drivers of influenza subtypes in Shanxi Province, China. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14229-y

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14229-y

Keywords: influenza, A(H1N1)pdm09, A(H3N2), influenza B, absolute humidity, meteorology, surveillance, Shanxi Province, epidemiology, Differential, meteorological, drivers