ArticlePloS one2022
Exploration of influenza incidence prediction model based on meteorological factors in Lanzhou, China, 2014-2017.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 17 citations in OpenAlex.
- Research on influenza surveillance and a prediction model based on multi-source data.BMC medical informatics and decision making · 2026Article
- Three-stage interpretability analysis of influenza virus and meteorological correlation in Jiuquan City, 2016-2025: SARIMAX + TreeSHAP.Frontiers in cellular and infection microbiology · 2026Article
- Unraveling meteorologically influenced recurrence and sequential infection risks among influenza, mumps, scarlet fever, and hand-foot-and-mouth disease: evidence from Shenzhen, China.Frontiers in public health · 2026Article
- FaXNet: a frequency-adaptive, explainable, and uncertainty-aware network for influenza forecasting.Frontiers in public health · 2026Article
- A study on the climate-driven spatiotemporal dynamics of influenza in Lanzhou spanning the COVID-19 era.Frontiers in cellular and infection microbiology · 2026Article
- Epidemiological trends and climate-driven dynamics of children respiratory infections in Lanzhou, China: a multi-pathogen time-series analysis.Frontiers in cellular and infection microbiology · 2026Article
- Analysis of pediatric respiratory virus epidemiology and its association with atmospheric parameters in Chengdu.Translational pediatrics · 2025Article
- Implications of morphological variation in influenza viruses.Microbiology and molecular biology reviews : MMBR · 2025Review
- Enhancing public health surveillance: SARIMAX model incorporating Baidu search index for HCV prediction in China.BMC medical research methodology · 2025Article
- Impact of meteorological factors on influenza incidence in Wuxi from 2014 to 2019: a time series and comprehensive analysis.Frontiers in public health · 2025Article
- Spatiotemporal patterns of influenza incidence changes in mainland China, 2004-2020.Frontiers in public health · 2025Article
- Study of the driving factors of the abnormal influenza A (H3N2) epidemic in 2022 and early predictions in Xiamen, China.BMC infectious diseases · 2024Article
- A Predictive Model of the Start of Annual Influenza Epidemics.Microorganisms · 2024Article
- Estimating All-Cause Deaths Averted in the First Two Years of the COVID-19 Vaccination Campaign in Italy.Vaccines · 2024Article
- Effects and interaction of temperature and relative humidity on the trend of influenza prevalence: A multi-central study based on 30 provinces in mainland China from 2013 to 2018.Infectious Disease Modelling · 2023Article
- Predicting monthly hospital outpatient visits based on meteorological environmental factors using the ARIMA model.Scientific reports · 2023Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Humans are susceptible to influenza. The influenza virus spreads quickly and behave seasonally. The seasonality and spread of influenza are often associated with meteorological factors and have spatio-temporal differences. Based on the influenza cases and daily average meteorological factors in Lanzhou from 2014 to 2017, this study firstly aimed to analyze the characteristics of influenza incidence in Lanzhou and the impact of meteorological factors on influenza activities. Then, SARIMA(X) models for the prediction were established. The influenza cases in Lanzhou from 2014 to 2017 was more male than female, and the younger the age, the higher the susceptibility; the epidemic characteristics showed that there is a peak in winter, a secondary peak in spring, and a trough in summer and autumn. The influenza cases in Lanzhou increased with increasing daily pressure, decreasing precipitation, average relative humidity, hours of sunshine, average daily temperature and average daily wind speed. Low temperature was a significant driving factor for the increase of transmission intensity of seasonal influenza. The SARIMAX (1,0,0)(1,0,1)[12] multivariable model with average temperature has better prediction performance than the university model. This model is helpful to establish an early warning system, and provide important evidence for the development of influenza control policies and public health interventions.
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