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Teams from anywhere in the world are invited to submit forecasts once a week for one or more of the countries. Take a look at the submission instructions and get in touch with any questions.

Teams

Here is the list of forecast models grouped by team:

European Centre for Disease Prevention and Control
ECDC
ARI Models
ARIMA model with seasonality
An ARIMA model with seasonality and with sqrt transformed data
Contributors
Lydia Champezou

Auto ETS model.
ETS model where R automatically selects the best ETS model (error, trend, seasonality) based on the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC).
Contributors
Ayleen Burt

ETS AAN model.
ETS model Additive error and trend, no seasonality.
Contributors
Ayleen Burt

Historical simplex pattern
Using historical data patterns with highest similarity to current data to foreast the future values
Contributors
ECDC Mathematical Modelling team

Detailed description
Model consists of 1) taking 'm' latest data points, 2) find 'n' closest neighbours from all historical data using L2-norm, 3) use the next data time point from each historical data points, 4) use data from point 3 to fit a log-normal distribution , 5) use the log-normal distirbution to estimate the quantiles/distribution, 6) return to step 3 but use two data points ahead for estimations of horizon 2, etc for horizon 3 and 4, 7) find optimal values of 'n' and 'm for a given country, using past 4 weeks of forecasts.

Data inputs
Historical ILIARIRates data as provided by RespiCast and ECDC

Norrsken Piecewise Linear Bayesian Model
A Bayesian piece-wise square-root-linear model fit.
Contributors
ECDC Mathematical Modelling Team

Detailed description
Model consists of 1) scaling of the data (convert to square-root-scale), 2) assigning data points into groups such that neighbouring data points belong to the same group, and simultaneously in Stan 3) fitting piece-wise intercept-slope models in each group, 4) regularizing the difference between neighbouring slopes to be close to 0, 5) generating a future slope based on the last slope plus fitted noise.

Data inputs
ECDC ERVISS

Norrsken Piecewise Linear Bayesian Model
A Bayesian piece-wise log-linear model fit.
Contributors
ECDC Mathematical Modelling Team

Detailed description
Model consists of 1) scaling of the data (convert to log-scale), 2) assigning data points into groups such that neighbouring data points belong to the same group, and simultaneously in Stan 3) fitting piece-wise intercept-slope models in each group, 4) regularizing the difference between neighbouring slopes to be close to 0, 5) generating a future slope based on the last slope plus fitted noise.

Data inputs
ECDC ERVISS

ILI Models
ARIMA model with seasonality
A simple ARIMA model with seasonality
Contributors
Lydia Champezou

Historical simplex pattern
Using historical data patterns with highest similarity to current data to foreast the future values
Contributors
ECDC Mathematical Modelling team

Detailed description
Model consists of 1) taking 'm' latest data points, 2) find 'n' closest neighbours from all historical data using L2-norm, 3) use the next data time point from each historical data points, 4) use data from point 3 to fit a log-normal distribution , 5) use the log-normal distirbution to estimate the quantiles/distribution, 6) return to step 3 but use two data points ahead for estimations of horizon 2, etc for horizon 3 and 4, 7) find optimal values of 'n' and 'm for a given country, using past 4 weeks of forecasts.

Data inputs
Historical ILIARIRates data as provided by RespiCast and ECDC

Norrsken Piecewise Linear Bayesian Model
A Bayesian piece-wise log-linear model fit.
Contributors
ECDC Mathematical Modelling Team

Detailed description
Model consists of 1) scaling of the data (convert to log-scale), 2) assigning data points into groups such that neighbouring data points belong to the same group, and simultaneously in Stan 3) fitting piece-wise intercept-slope models in each group, 4) regularizing the difference between neighbouring slopes to be close to 0, 5) generating a future slope based on the last slope plus fitted noise.

Data inputs
ECDC ERVISS

Norrsken Piecewise Linear Bayesian Model
A Bayesian piece-wise square-root-linear model fit.
Contributors
ECDC Mathematical Modelling Team

Detailed description
Model consists of 1) scaling of the data (convert to square-root-scale), 2) assigning data points into groups such that neighbouring data points belong to the same group, and simultaneously in Stan 3) fitting piece-wise intercept-slope models in each group, 4) regularizing the difference between neighbouring slopes to be close to 0, 5) generating a future slope based on the last slope plus fitted noise.

Data inputs
ECDC ERVISS

Fjordhest
fjordhest
ARI Models
fjordhest-ensemble
An inverse-WIS weighted ensemble of 3 component models - an exponential trend smoothing (ETS) model, a quantile AR model, and a baseline model of random walk with drift.
Contributors
Sasi Kandula
Birgitte De Blasio

Detailed description
Quantile autoregression model uses quantgen R package, and is similar to CMU-Timeseries model from 2022/23 FluSight season. Random walk and ETS models use fable R package. To build an ensemble, the quantile distributions of the component models are weighted (location- and target-specific) by the mean of inverse-WIS scores over the last 3 weeks that could be evaluated. The estimates are solely the responsibility of the contributors and do not represent, nor are endorsed by, the Norwegian Institute of Public Health.

Citation/s
Tibshirani R, Brooks L (2020). quantgen: Tools for generalized quantile modeling; https://github.com/cmu-delphi/flu-hosp-forecast/; O'Hara-Wild M, Hyndman R, Wang E (2022). fable: Forecasting Models for Tidy Time Series. R package version 0.3.2.

Data inputs
ECDC ERVISS

License
CC-BY_SA-4.0

ILI Models
fjordhest-ensemble
An inverse-WIS weighted ensemble of 3 component models - an exponential trend smoothing (ETS) model, a quantile AR model, and a baseline model of random walk with drift.
Contributors
Sasi Kandula
Birgitte De Blasio

Detailed description
Quantile autoregression model uses quantgen R package, and is similar to CMU-Timeseries model from 2022/23 FluSight season. Random walk and ETS models use fable R package. To build an ensemble, the quantile distributions of the component models are weighted (location- and target-specific) by the mean of inverse-WIS scores over the last 3 weeks that could be evaluated. The estimates are solely the responsibility of the contributors and do not represent, nor are endorsed by, the Norwegian Institute of Public Health.

Citation/s
Tibshirani R, Brooks L (2020). quantgen: Tools for generalized quantile modeling; https://github.com/cmu-delphi/flu-hosp-forecast/; O'Hara-Wild M, Hyndman R, Wang E (2022). fable: Forecasting Models for Tidy Time Series. R package version 0.3.2.

Data inputs
ECDC ERVISS

License
CC-BY_SA-4.0

ISI Foundation
ISI
ARI Models
AriABCaster
A stochastic, age structured compartmental model calibrated via ABC-SMC techniques.
Contributors
Nicolò Gozzi

License
CC-BY-4.0

EpiNowARI
A semimechanistic model based on Rt estimation developed by the epiforecasts team at LSHTM [Reference] (https://epiforecasts.io/EpiNow2/)
Contributors
Stefania Fiandrino
Nicolò Gozzi

Data inputs
ECDC ERVISS

Generalized Logistic Model for ARI
A generalized logistic model for ARI.
Contributors
Nicolò Gozzi

License
CC-BY-4.0

ILI Models
EpiNow
A semimechanistic model based on Rt estimation developed by the epiforecasts team at LSHTM
Contributors
Stefania Fiandrino
Nicolò Gozzi

FluABCaster
A stochastic, age structured compartmental model calibrated via ABC-SMC techniques.
Contributors
Nicolò Gozzi

License
CC-BY-4.0

GLEAM
A stochastic, age-structured compartmental model based on a metapopulation approach that uses real-world data on populations and human mobility to simulate epidemic spreading on a global scale.
Contributors
Luca Rossi

Generalized Logistic Model
A generalized logistic model.
Contributors
Nicolò Gozzi

License
CC-BY-4.0

IPSICast
An exogenous autoregressive model that integrates information on digital surveillance data from the Influweb participatory system.
Contributors
Stefania Fiandrino

Data inputs
Surveillance data from the official Italian Institute (Istituto Superiore di Sanità, ISS), and participatory surveillance data from Influweb platform.

ItaLuxColab
ItaLuxColab
ILI Models
Epidemic projections by SIRS+EKF
Stochastic SIRS model with automatic data integration by the extended Kalman filter (EKF) with adaptive hyperparameter estimation, applied separately on each region (github.com/AtteAalto/EpiEKF)
Contributors
Atte Aalto
Daniele Proverbio
Giulia Giordano
Jorge Goncalves

Data inputs
ECDC ERVISS and FluID (as provided in flu-forecast-hub)

License
CC-BY-4.0

PoliTo Complex System Lab
CSL_PoliTo
ILI Models
Metapopulation SEINR model
An epidemic model based on the susceptible-exposed-infectious-noninfectious-removed dynamic, with meta-population and activity-driven modelling, class subdivision, Bayesian parameter optimization
Contributors
Alessandro Rizzo
Lorenzo Zino
Francesco Celino

Data inputs
epidemiological, demographic, and behavioural data

QMUL
QMUL
ILI Models
ARIMA Model
Autoregressive integrated moving average model.
Contributors
Nicola Perra
Yuhan Li

License
CC-BY-4.0

SEIR Model
Stochastic age-structed SEIR model.
Contributors
Nicola Perra
Yuhan Li

License
CC-BY-4.0

RespiCast
respicast
ARI Models
Hub Ensemble
An Ensemble model that computes the median across different models' quantiles.
Contributors
RespiCast Team

License
CC-BY-4.0

Quantile Baseline
A simple baseline model that samples future target changes from historical increments.
Contributors
RespiCast Team

Detailed description
The baseline model consistently predicts as median value the last data point within the calibration period, while its confidence intervals are estimated on past data increments. Similar baseline models have become a standard neutral benchmark providing a simple reference for all models in the context of collaborative forecasting hubs, see Cramer et al. "Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States", 2022 and Sherratt et al. "Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations", 2023.

License
CC-BY-4.0

ILI Models
Hub Ensemble
An Ensemble model that computes the median across different models' quantiles.
Contributors
RespiCast Team

License
CC-BY-4.0

Quantile Baseline
A simple baseline model that samples future target changes from historical increments.
Contributors
RespiCast Team

Detailed description
The baseline model consistently predicts as median value the last data point within the calibration period, while its confidence intervals are estimated on past data increments. Similar baseline models have become a standard neutral benchmark providing a simple reference for all models in the context of collaborative forecasting hubs, see Cramer et al. "Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States", 2022 and Sherratt et al. "Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations", 2023.

License
CC-BY-4.0