ArticlePLoS computational biology2015
Forecasting Influenza Epidemics in Hong Kong.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 1 of them a synthesis that pooled it.
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Who cites it
67 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Infectious disease modelling for SARS-CoV-2 in Africa to guide policy: A systematic review.Epidemics · 2022Pooled it
- Multi-source machine learning for short-term influenza forecasting in Hunan Province, China: a spatiotemporal analysis using surveillance, search, meteorological and air-quality data.BMC infectious diseases · 2026Article
- Unified climate factors predict influenza outbreak seasonality across tropical and temperate regions.PNAS nexus · 2026Article
- A Human Mobility-Based Modeling Study of Influenza Vaccination Strategies Across Socioeconomically Disparate Regions in China.Vaccines · 2026Article
- Temperature-based spatiotemporal heterogeneity of hand, foot, and mouth disease transmission in China.BMC infectious diseases · 2025Article
- The seasonality of respiratory syncytial virus and its associations with meteorological factors in Hong Kong, 2014-2023.One health (Amsterdam, Netherlands) · 2025Article
- A Framework for Evaluating the Use of Surveillance Systems for Short-Term Influenza Forecasting.Influenza and other respiratory viruses · 2025Article
- Predicting influenza in China from October 1, 2023, to February 5, 2024: A transmission dynamics model based on population migration.Infectious Disease Modelling · 2025Article
- Forecasting influenza epidemics in China using transmission dynamic model with absolute humidity.Infectious Disease Modelling · 2025Article
- Improving influenza forecast in the tropics and subtropics: a case study of Hong Kong.Journal of the Royal Society, Interface · 2025Article
- Characterizing the interactions between influenza and respiratory syncytial viruses and their implications for epidemic control.Nature communications · 2024Article
- Monitoring the Status of Multi-Wave Omicron Variant Outbreaks - 71 Countries, 2021-2023.China CDC weekly · 2024Article
- An adaptive weight ensemble approach to forecast influenza activity in an irregular seasonality context.Nature communications · 2024Article
- Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019: A modelling and comparative studyInfectious Disease Modelling · 2024Article
- Forecasting of influenza activity and associated hospital admission burden and estimating the impact of COVID-19 pandemic on 2019/20 winter season in Hong Kong.PLoS computational biology · 2024Article
- Predicting seasonal influenza outbreaks with regime shift-informed dynamics for improved public health preparedness.Scientific reports · 2024Article
- Optimizing type 2 diabetes management: AI-enhanced time series analysis of continuous glucose monitoring data for personalized dietary intervention.PeerJ. Computer science · 2024Article
- Nowcasting and Forecasting Seasonal Influenza Epidemics - China, 2022-2023.China CDC weekly · 2023Article
- Quantifying the rebound of influenza epidemics after the adjustment of zero-COVID policy in China.PNAS nexus · 2023Article
- Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study.Infectious diseases of poverty · 2023Observational
7 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
Recent advances in mathematical modeling and inference methodologies have enabled development of systems capable of forecasting seasonal influenza epidemics in temperate regions in real-time. However, in subtropical and tropical regions, influenza epidemics can occur throughout the year, making routine forecast of influenza more challenging. Here we develop and report forecast systems that are able to predict irregular non-seasonal influenza epidemics, using either the ensemble adjustment Kalman filter or a modified particle filter in conjunction with a susceptible-infected-recovered (SIR) model. We applied these model-filter systems to retrospectively forecast influenza epidemics in Hong Kong from January 1998 to December 2013, including the 2009 pandemic. The forecast systems were able to forecast both the peak timing and peak magnitude for 44 epidemics in 16 years caused by individual influenza strains (i.e., seasonal influenza A(H1N1), pandemic A(H1N1), A(H3N2), and B), as well as 19 aggregate epidemics caused by one or more of these influenza strains. Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead. Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads. These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.