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Near-term forecasts of influenza-like illness
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/sim.7488openalex-snapshot
Infectious disease prediction with kernel conditional density estimation
inferredcites →https://doi.org/10.1007/978-0-387-21606-5openalex-snapshot
The Elements of Statistical Learning
inferredcites →https://doi.org/10.1007/bf02478259openalex-snapshot
A logical calculus of the ideas immanent in nervous activity
inferredcites →https://doi.org/10.1016/0304-4076(92)90104-yopenalex-snapshot
Testing the null hypothesis of stationarity against the alternative of a unit root
inferredcites →https://doi.org/10.1016/j.epidem.2018.02.003openalex-snapshot
Results from the second year of a collaborative effort to forecast influenza seasons in the United States
inferredcites →https://doi.org/10.1016/j.vaccine.2007.03.046openalex-snapshot
The annual impact of seasonal influenza in the US: Measuring disease burden and costs
inferredcites →https://doi.org/10.1038/nature07634openalex-snapshot
Detecting influenza epidemics using search engine query data
inferredcites →https://doi.org/10.1038/srep12760openalex-snapshot
Advances in nowcasting influenza-like illness rates using search query logs
inferredcites →https://doi.org/10.1038/srep25732openalex-snapshot
Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance
inferredcites →https://doi.org/10.1073/pnas.0906910106openalex-snapshot
Multiscale mobility networks and the spatial spreading of infectious diseases
inferredcites →https://doi.org/10.1073/pnas.1208772109openalex-snapshot
Forecasting seasonal outbreaks of influenza
inferredcites →https://doi.org/10.1073/pnas.1515373112openalex-snapshot
Accurate estimation of influenza epidemics using Google search data via ARGO
inferredcites →https://doi.org/10.1080/01621459.1937.10503522openalex-snapshot
The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance
inferredcites →https://doi.org/10.1093/aje/kwg239openalex-snapshot
Prediction of the Spread of Influenza Epidemics by the Method of Analogues
inferredcites →https://doi.org/10.1109/tac.1974.1100705openalex-snapshot
A new look at the statistical model identification
inferredcites →https://doi.org/10.1111/irv.12226openalex-snapshot
A systematic review of studies on forecasting the dynamics of influenza outbreaks
inferredcites →https://doi.org/10.1145/2783258.2783291openalex-snapshot
Dynamic Poisson Autoregression for Influenza-Like-Illness Case Count Prediction
inferredcites →https://doi.org/10.1186/s12879-016-1669-xopenalex-snapshot
Results from the centers for disease control and prevention’s predict the 2013–2014 Influenza Season Challenge
inferredcites →https://doi.org/10.1198/016214506000001437openalex-snapshot
Strictly Proper Scoring Rules, Prediction, and Estimation
inferredcites →https://doi.org/10.1214/16-aoas1000openalex-snapshot
Forecasting seasonal influenza with a state-space SIR model
inferredcites →https://doi.org/10.1371/currents.outbreaks.90b9ed0f59bae4ccaa683a39865d9117openalex-snapshot
Twitter Improves Influenza Forecasting
inferredcites →https://doi.org/10.1371/journal.pcbi.1004382openalex-snapshot
Flexible Modeling of Epidemics with an Empirical Bayes Framework
inferredcites →https://doi.org/10.1371/journal.pcbi.1004513openalex-snapshot
Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance
inferredcites →https://doi.org/10.1371/journal.pcbi.1005248openalex-snapshot
A human judgment approach to epidemiological forecasting
inferredcites →https://doi.org/10.1371/journal.pcbi.1005910openalex-snapshot
Prediction of infectious disease epidemics via weighted density ensembles
inferredcites →https://doi.org/10.1371/journal.pone.0065459openalex-snapshot
Predictive Validation of an Influenza Spread Model
inferredcites →https://doi.org/10.1371/journal.pone.0094130openalex-snapshot
Influenza Forecasting in Human Populations: A Scoping Review
inferredcites →https://doi.org/10.18637/jss.v027.i03openalex-snapshot
Automatic Time Series Forecasting: The forecast Package for R
inferredcites →https://doi.org/10.21236/ada164453openalex-snapshot
Learning Internal Representations by Error Propagation
inferredcites →https://doi.org/10.2196/jmir.7486openalex-snapshot
Subregional Nowcasts of Seasonal Influenza Using Search Trends
inferredcites →https://doi.org/10.26180/5090230openalex-snapshot
Automatic time series forecasting: the forecast package for R
inferredcites →https://doi.org/10.32614/cran.package.forecastopenalex-snapshot
forecast: Forecasting Functions for Time Series and Linear Models
inferredcites →https://doi.org/10.32614/r.manualsopenalex-snapshot
R: A Language and Environment for Statistical Computing
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4687124openalex-extracted
Magnitude of Changes of Influenza a and B Infection Due to Environmental Factors Through Vector Auto Regressive Impulse and Response Function in Bangladesh
inferred← citeshttps://doi.org/10.1007/s41745-020-00200-6openalex-snapshot
Mathematical Models for COVID-19 Pandemic: A Comparative Analysis
inferred← citeshttps://doi.org/10.1007/s42979-024-02950-xopenalex-snapshot
Enhanced Multi-variate Time Series Prediction Through Statistical-Deep Learning Integration: The VAR-Stacked LSTM Model
inferred← citeshttps://doi.org/10.1016/j.asoc.2021.107994openalex-snapshot
Comprehensive learning particle swarm optimization enabled modeling framework for multi-step-ahead influenza prediction
inferred← citeshttps://doi.org/10.1016/j.cacint.2026.100313openalex-snapshot
Weekly extremes of PM2.5-attributable cardiovascular mortality in Northeastern Thailand: Extreme-value return levels and 26-week forecasts
inferred← citeshttps://doi.org/10.1016/j.epidem.2026.100901openalex-snapshot
Enhancing Influenza-Like Illness forecasting: An ensemble approach combining mathematical and deep learning models amidst the COVID-19 pandemic
inferred← citeshttps://doi.org/10.1016/j.eswa.2021.115153openalex-snapshot
Multi-step influenza outbreak forecasting using deep LSTM network and genetic algorithm
inferred← citeshttps://doi.org/10.1016/j.ijforecast.2026.03.001openalex-snapshot
Restoring the forecasting power of Google Trends with statistical preprocessing
inferred← citeshttps://doi.org/10.1016/j.mex.2024.103093openalex-snapshot
Hybrid neural network models for time series disease prediction confronted by spatiotemporal dependencies
inferred← citeshttps://doi.org/10.1016/j.onehlt.2025.101128openalex-snapshot
Epidemiological insights into canine rabies in Chennai: Trends, forecasting and One Health implications
inferred← citeshttps://doi.org/10.1016/j.rinp.2021.104070openalex-snapshot
Epidemiological and clinical characteristics of influenza patients in respiratory department under the prediction of autoregressive integrated moving average model
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2021.145983openalex-snapshot
Spread of respiratory infections in student dormitories in China
inferred← citeshttps://doi.org/10.1038/s42256-024-00895-7openalex-snapshot
Machine learning for data-centric epidemic forecasting
inferred← citeshttps://doi.org/10.1073/pnas.1912147116openalex-snapshot
On the multibin logarithmic score used in the FluSight competitions
inferred← citeshttps://doi.org/10.1093/jac/dkaa388openalex-snapshot
Time-series modelling for the quantification of seasonality and forecasting antibiotic-resistant episodes: application to carbapenemase-producing Enterobacteriaceae episodes in France over 2010–20
inferred← citeshttps://doi.org/10.1101/2019.12.27.889212openalex-snapshot
Improving Probabilistic Infectious Disease Forecasting Through Coherence
inferred← citeshttps://doi.org/10.1101/2022.10.27.22281617openalex-snapshot
Forecasting Influenza-Like Illness (ILI) during the COVID-19 Pandemic
inferred← citeshttps://doi.org/10.1101/2023.09.28.23296216openalex-snapshot
Evaluating Performance of Near-Term Influenza Forecasters Across Consecutive Seasons
inferred← citeshttps://doi.org/10.1101/2024.07.31.24311314openalex-snapshot
An Imputation-Based Approach for Augmenting Sparse Epidemiological Signals
inferred← citeshttps://doi.org/10.1101/2025.08.01.25332807openalex-snapshot
Mind the Baseline: The Hidden Impact of Reference Model Selection on Forecast Assessment
inferred← citeshttps://doi.org/10.1186/s13104-020-4889-5openalex-snapshot
A comparative study on predicting influenza outbreaks using different feature spaces: application of influenza-like illness data from Early Warning Alert and Response System in Syria
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1007623openalex-snapshot
Improving probabilistic infectious disease forecasting through coherence
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1008651openalex-snapshot
Fast and accurate influenza forecasting in the United States with Inferno
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1010115openalex-snapshot
Addressing delayed case reporting in infectious disease forecast modeling
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1010602openalex-snapshot
An ensemble n-sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA
inferred← citeshttps://doi.org/10.1371/journal.pone.0252147openalex-snapshot
Evaluating data-driven methods for short-term forecasts of cumulative SARS-CoV2 cases
inferred← citeshttps://doi.org/10.21203/rs.3.rs-3799365/v1openalex-snapshot
Evaluating predictive hybrid neural network models in spatiotemporal context: An application on Influenza outbreak predictions
inferred← citeshttps://doi.org/10.3389/fimmu.2021.735576openalex-snapshot
Interferon Lambda Signaling in Macrophages Is Necessary for the Antiviral Response to Influenza
inferred← citeshttps://doi.org/10.3389/fphy.2023.1277052openalex-snapshot
Forecasting infections with spatio-temporal graph neural networks: a case study of the Dutch SARS-CoV-2 spread
inferred← citeshttps://doi.org/10.3389/frma.2022.1003972openalex-snapshot
Modeling COVID-19 incidence with Google Trends
inferred← citeshttps://doi.org/10.48550/arxiv.1910.07084openalex-snapshot
An extended note on the multibin logarithmic score used in the FluSight competitions
inferred← citeshttps://doi.org/10.48550/arxiv.2002.04663openalex-snapshot
TDEFSI: Theory Guided Deep Learning Based Epidemic Forecasting with Synthetic Information
inferred← citeshttps://doi.org/10.52420/2071-5943-2023-22-3-57-63openalex-snapshot
Predicting the dynamics of norovirus infection using time series models
inferred← citeshttps://doi.org/10.7189/jogh.15.04114openalex-snapshot
Infectious disease forecasting to support public health: use of readily available methods to predict malaria and diarrhoeal diseases in Mozambique
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