Statistical Association of Meteorological and Geographical Factors with Influenza Incidence
Keywords:
Influenza, Climate types (Prathet Samutthan), Seasonal factors (Utu Samutthan), Generalized linear model, Incidence rate ratioAbstract
This ecological time-series study aimed to investigate the statistical association between monthly influenza cases and meteorological factors across four model provinces representing distinct climate types in Thailand: Chiang Mai (hot climate), Nonthaburi (cool climate), Nakhon Ratchasima (temperate climate), and Nakhon Si Thammarat (cold climate). Secondary data from January 2018 to December 2024 (84 months) were analyzed using Spearman's rank correlation coefficient and generalized linear models (GLM) with quasi-Poisson and negative binomial distributions. A backward elimination approach was used for variable selection, and results were reported as incidence rate ratios (IRRs) with 95% confidence intervals (CIs). The results showed that in Chiang Mai (hot climate), a significant increase in mean temperature was associated with a decrease in influenza cases (IRR = 0.667, 95% CI: 0.55–0.81, p < 0.001), while minimum rainfall and minimum temperature were associated with increased case numbers (IRR = 1.007 and 1.172, respectively). In Nonthaburi (cool climate), an increase in minimum rainfall was associated with a marginal rise in cases (IRR ≈ 1.000, p = 0.016). In Nakhon Ratchasima (temperate climate), an increase in maximum temperature showed a decreasing trend in cases (IRR = 0.902, p = 0.061), which was not statistically significant at the 0.05 level. In Nakhon Si Thammarat (cold climate), an increase in maximum temperature was significantly associated with a reduction in influenza cases (IRR = 0.815, 95% CI: 0.70–0.95, p = 0.009). In conclusion, meteorological factors, particularly temperature, are associated with influenza case numbers across all four climate model provinces, especially in hot (Chiang Mai) and cold (Nakhon Si Thammarat) climates. Public health agencies should integrate weather forecasting data into disease surveillance systems and develop area-specific preventive measures tailored to the climatic characteristics of each region.
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