ArticleFrontiers in public health2025
Improving influenza prediction in Quanzhou, China: an ARIMAX model integrated with meteorological drivers.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Spatiotemporal dynamics, clustering, and ARIMA-based short-term prediction of human brucellosis in Xinjiang, China.BMC infectious diseases · 2026Article
- Spatiotemporal lag effects of surface water quality on liver cirrhosis mortality risk in China: a nine-year time-series study of environmental driving mechanism.BMC public health · 2026Article
- Incorporating meteorological factors into a SARIMA model for predicting pediatric influenza epidemics.Frontiers in public health · 2026Article
- A stacked ensemble model with NNLS-based weighting for influenza forecasting: a case study of Anhui Province, China.Frontiers in public health · 2026Article
- Generalized additive model integrating multi-source data for short-term influenza forecasting in Shenzhen, China (2023-2025).Frontiers in public health · 2026Article
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8 authors.
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Abstract
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, 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.
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