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Forecasting Influenza Epidemics in Hong Kong

https://doi.org/10.1371/journal.pcbi.1004383

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Comparative community burden and severity of seasonal and pandemic influenza: results of the Flu Watch cohort study

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Incidence of common respiratory viral infections related to climate factors in hospitalized children in Hong Kong

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Aerosol transmission is an important mode of influenza A virus spread

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Global Influenza Seasonality: Reconciling Patterns across Temperate and Tropical Regions

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Comparison of Filtering Methods for the Modeling and Retrospective Forecasting of Influenza Epidemics

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Predicting the Epidemic Sizes of Influenza A/H1N1, A/H3N2, and B: A Statistical Method

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Modeling and Predicting Seasonal Influenza Transmission in Warm Regions Using Climatological Parameters

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Real-Time Epidemic Monitoring and Forecasting of H1N1-2009 Using Influenza-Like Illness from General Practice and Family Doctor Clinics in Singapore

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Forecasting Influenza Epidemics from Multi-Stream Surveillance Data in a Subtropical City of China

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Influenza Forecasting in Human Populations: A Scoping Review

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Environmental Predictors of Seasonal Influenza Epidemics across Temperate and Tropical Climates

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In silico analysis enabling informed design for genome editing in medicinal cannabis; gene families and variant characterisation

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Optimizing Use of Multistream Influenza Sentinel Surveillance Data

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A simple modification for improving inference of non-linear dynamical systems

What points at this work

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Additional file 1 of Increased urbanization reduced the effectiveness of school closures on seasonal influenza epidemics in China

inferred← citeshttps://doi.org/10.6084/m9.figshare.19881550openalex-snapshot

Additional file 1 of Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models

inferred← citeshttps://doi.org/10.6084/m9.figshare.22608079openalex-snapshot

Additional file 1 of Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study

inferred← citeshttps://doi.org/10.6084/m9.figshare.22608082openalex-snapshot

Additional file 2 of Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study

inferred← citeshttps://doi.org/10.6084/m9.figshare.22608085openalex-snapshot

Additional file 3 of Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study

inferred← citeshttps://doi.org/10.6084/m9.figshare.22608088openalex-snapshot

Additional file 4 of Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study

inferred← citeshttps://doi.org/10.6084/m9.figshare.22608091openalex-snapshot

Additional file 5 of Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study

inferred← citeshttps://doi.org/10.6084/m9.figshare.22608094openalex-snapshot

Additional file 6 of Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study

inferred← citeshttps://doi.org/10.6084/m9.figshare.22608097openalex-snapshot

Additional file 7 of Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study

inferred← citeshttps://doi.org/10.7717/peerj-cs.1971openalex-snapshot

Optimizing type 2 diabetes management: AI-enhanced time series analysis of continuous glucose monitoring data for personalized dietary intervention

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