Improving influenza prediction in Quanzhou, China

An Arimax model integrated with meteorological drivers

Bibliographic Data

ID22072719
AuthorsHuanhuan Pan (Fujian Medical University), Jiangyi Liu (0000-0001-6525-4659, Fujian Medical University), Fengping Li (0000-0002-2622-0883, Fujian Medical University), Weiming Wang (0000-0001-6289-0094, Fujian Medical University, corresponding author), Xinlan Huang (Fujian Medical University), Huanrong Li (0000-0002-0120-0616, Fujian Medical University), Xiaoxiong Yang (Fujian Medical University), Xueqi Chen (0000-0002-6648-4584, Xiamen University of Technology)
Year2025
Volume13
Pages1662775-1662775
Publication date2025-10-22
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2025.1662775
PMID41200038
OpenAlexW4415419000
LanguageEN
Citations received4
References cited29

Background Influenza remains a significant public health challenge, characterized by substantial seasonal variation and considerable socioeconomic burden. Although meteorological factors are known to influence influenza transmission, their specific effects within subtropical monsoon climates, such as that of Quanzhou, remain inadequately characterized. Methods We analyzed weekly influenza-like illness (ILI%) data from sentinel hospitals in Quanzhou between 2016 and 2024. Descriptive statistics, distributed lag nonlinear models (DLNM), cross-correlation function (CCF) analysis, and ARIMAX modeling were employed to examine the lagged and nonlinear associations between meteorological variables and ILI%. Results The overall ILI% during the surveillance period was 2.32%, with significant temporal trends: a pronounced decline from 2016 to 2020 (APC = −22.693, p = 0.001) was followed by a significant increase from 2020 to 2024 (APC = 21.555, p = 0.003). Children under 15 years of age were the most affected demographic. A consistent bimodal seasonal pattern was observed, with a primary peak in winter (weeks 50–14) and a secondary peak in summer (weeks 20–29). DLNM analysis indicated that low atmospheric pressure ( 21 mm was protective. Wind speed demonstrated an N-shaped association with ILI%, though this was not statistically significant. The optimal forecasting model incorporated precipitation at a 2-week lag as an exogenous variable (ARIMA(1,1,1)(1,1,1) 52 + WAP(lag2)) and yielded the highest predictive accuracy for 2024 ILI%, improving RMSE by 2.9% and MAE by 1.3% compared to the baseline model. Conclusion Incorporating meteorological factors significantly improves the accuracy of influenza forecasting models. These findings support the development of climate-informed early warning systems and targeted public health interventions in subtropical regions

Psychological intervention · Public health · Public health interventions · Subtropics · Warning system · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Influenza Virus Research Studies

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