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

Model name Authors Team Methods Complete metadata
AMM-EpiInvert Luis Alvarez, Jean-David Morel, Jean-Michel Morel AMM Learning from the past a short time forecast of the COVID-19 incidence curve trend. Metadata
BIOCOMSC-Gompertz Martí Català, Enric Álvarez, Sergio Alonso, Daniel López, Clara Prats BIOCOMSC Empirical model based on cases and deaths dynamics. Metadata
bisop-seirfilter Martin Šmíd, Jan Trnka, Vít Tuček, Milan Zajíček Centre for Modelling of Biological and Social Processes please see https://www.medrxiv.org/content/10.1101/2021.02.16.21251834v1 Metadata
bisop-seirfilterlite Martin Šmíd, Jan Trnka, Vít Tuček, Milan Zajíček Centre for Modelling of Biological and Social Processes A simple stochastic SEIR state space model Metadata
CovidMetrics-epiBATS Tom Zimmermann, Arne Rodloff University of Cologne Covid Metrics Forecasts are based on TBATS - models (DeLivera, Hyndman and Snyder (2011)) and are updated daily for each German state. Metadata
DirkBeckmann-Gompertz Dr. Dirk Beckmann DirkBeckmann gompertz model Metadata
DSMPG-bayes Sebastian B. Mohr, Jonas Dehning, Viola Priesemann Priesemann Group, MPI-DS Bayesian inference of SIR-dynamics Metadata
ECDC-hosp_model Rok Grah, Rene Niehus ECDC Modelling Team mechanistic estimation of hospitalisations using age disaggregated data of weekly cases, vaccination, and case hospitalisation rates Metadata
epiforecasts-arimareg Sophie Meakin epiforecasts A regression model forecasting admissions from 1-week-lagged cases, with ARIMA errors. Fit to weekly data. Metadata
epiforecasts-caseconv Sophie Meakin epiforecasts A convolution of cases and a delay distribution fit to weekly data. Metadata
epiforecasts-EpiExpert_direct Nikos Bosse, Sam Abbott, Sebastian Funk Epiforecasts / London School of Hygiene and Tropical Medicine Mean ensemble of human predictions Metadata
epiforecasts-EpiExpert_Rt Nikos Bosse, Sam Abbott, Sebastian Funk Epiforecasts / London School of Hygiene and Tropical Medicine Mean ensemble of human predictions of Rt which get mapped to cases and deaths using a renewal equation Metadata
epiforecasts-EpiExpert Nikos Bosse, Sam Abbott, Sebastian Funk Epiforecasts / London School of Hygiene and Tropical Medicine Mean ensemble of human predictions Metadata
epiforecasts-EpiNow2 Nikos Bosse, Sam Abbott, Sebastian Funk Epiforecasts / London School of Hygiene and Tropical Medicine Semi-mechanistic estimation of the time-varying reproduction number for latent infections mapped to reported cases/deaths. Metadata
epiforecasts-tsensemble Sophie Meakin epiforecasts A mean ensemble of three autoregressive time series models (ARIMA, ETS and “naive” - future admissions are equal to the last observed week, with expanding uncertainty). Metadata
epiforecasts-weeklygrowth Sam Abbott epiforecasts A Bayesian autoregressive model using weekly incidence data, application of the forecast.vocs R package. Metadata
epiMOX-SUIHTER Giovanni Ardenghi, Giovanni Ziarelli, Luca Dede’, Nicola Parolini, Alfio Quarteroni epiMOX Compartmental model SUIHTER Metadata
EuroCOVIDhub-baseline Hugo Gruson European COVID-19 Forecast Hub An baseline model against which other models can be evaluated Metadata
EuroCOVIDhub-ensemble Katharine Sherratt, Nikos Bosse, Sebastian Funk European COVID-19 Forecast Hub An ensemble, or model average, of submitted forecasts to the European COVID-19 Forecast Hub. Metadata
FIAS_FZJ-Epi1Ger Maria V. Barbarossa, Jan Fuhrmann, Stefan Krieg, Jan H. Meinke Frankfurt Institute for Advanced Studies & Forschungszentrum Jülich An extended SEIR model with additional compartments for undetected cases Metadata
fohm-c19inbel Sharon Kuhlmann-Berenzon, Martin Camitz FD-AN, Folkhälsomyndigheten Negative binomial fit/projection on reported cases by age group, then converted to hospitalizations by risk assumptions updated circa monthly. Metadata
GoeWroc-BaseBayes Tobias Weber, Viktor Bezborodov, Tyll Krueger, Dominic Schuhmacher GoeWroc A mixture between a SIR and Bayesian modelling approach, with regard to a possible spatial extension and local r values in later versions. Metadata
HZI-AgeExtendedSEIR Isti Rodiah, Berit Lange, Pratizio Vanella, Alexander Kuhlmann, Wolfgang Bock Helmholtz Zentrum fuer Infektionsforschung Deterministic SEIR type model Metadata
ICM-agentModel Rafał Bartczuk, Łukasz Górski, Magdalena Gruziel-Słomka, Artur Kaczorek, Jan Kisielewski, Antoni Moszyński, Karol Niedzielewski, Jędrzej Nowosielski, Maciej Radwan, Franciszek Rakowski, Marcin Semeniuk, Jakub Zieliński ICM / University of Warsaw Agent-based model Metadata
IEM_Health-CovidProject Brad Suchoski, Steve Stage, Heidi Gurung, Sid Baccam IEM Health SEIR model projections for daily incident confirmed COVID cases and deaths by using AI to fit actual cases observed. Metadata
ILM-EKF Stefan Heyder, Thomas Hotz ILM Extended Kalman filter based on reproduction equation Metadata
Imperial-DeCa Sangeeta Bhatia, Pierre Nouvellet Imperial College London Uses both cases and deaths to estimate an observed CFR. Projections are based on the estimated CFR. Metadata
Imperial-RtI0 Sangeeta Bhatia, Pierre Nouvellet Imperial College London Jointly estimates initial incidence and reproduction number Metadata
Imperial-sbkp Sangeeta Bhatia, Pierre Nouvellet, Kris V Parag Imperial College London Optimises the window over which reproduction number is assumed to be constant. Metadata
itwm-dSEIR Jan Mohring, Neele Leithäuser, Michael Helmling Fraunhofer Institute for Industrial Mathematics ITWM Integral equation model based on age cohorts taking into account vaccination and testing. The parameters are adjusted to the counted cases and deaths. Metadata
ITWW-county_repro Przemyslaw Biecek, Viktor Bezborodov, Marcin Bodych, Jan Pablo Burgard, Stefan Heyder, Thomas Hotz, Tyll Krüger ITWW Forecasts of county level incidence based on regional reproduction numbers. Metadata
JBUD-HMXK Jozef Budzinski JBUD Heavily modified infection-age SIR-X model with waning immunity, vaccinations, seasonality and undetected cases. Metadata
Karlen-pypm Dean Karlen Karlen Working Group Discrete-time difference equations with long periods of constant transmission rate Metadata
KITmetricslab-bivar_branching Johannes Bracher KITmetricslab Delta-variant and other cases are modelled as independent branching processes, with weekly growth  rates following random walks. Forecasts for 3 and 4 wk are likely unreliable. Metadata
LANL-GrowthRate Dave Osthus, Sara Del Valle, Carrie Manore, Brian Weaver, Lauren Castro, Courtney Shelley, Manhong (Mandy) Smith, Julie Spencer, Geoffrey Fairchild, Travis Pitts, Dax Gerts, Lori Dauelsberg, Ashlynn Daughton, Morgan, Gorris, Beth Hornbein, Daniel Israel, Nidhi Parikh, Deborah Shutt, Amanda Ziemann Los Alamos National Labs This model makes predictions about the future, unconditional on particular intervention strategies. Statistical dynamical growth model accounting for population susceptibility. Metadata
LeipzigIMISE-SECIR Yuri Kheifetz, Holger Kirsten, Markus Scholz Universitaet Leipzig IMISE/GenStat SECIR type model Metadata
MIMUW-StochSEIR Anna Gambin, Krzysztof Gogolewski, Blażej Miasojedow, Ewa Szczurek, Daniel Rabczenko, Magdalena Rosińska Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw Extended SEIR model Metadata
MIT_CovidAnalytics-DELPHI Michael Lingzhi Li, Hamza Tazi Bouardi, Dimitris Bertsimas CovidAnalytics at MIT This model makes predictions for future cases based on a heavily modified SEIR model taking into account underdetection and government intervention. Current interventions are assumed to continue. Metadata
MOCOS-agent1 Marek Bawiec, Marcin Bodych, Tyll Krueger, Tomasz Ozanski, Barbara Pabjan, Agata Migalska, Przemyslaw Biecak, Viktor Bezborodov, Ewa Szczurek, Ewaryst Rafajłowicz, Ewa Rafajłowicz, Wojciech Rafajłowicz MOCOS group Agent-based microsimulation model Metadata
MUNI_DMS-SEIAR Veronika Eclerova, Lenka Pribylova Department of Mathematics and Statistics Masaryk University Team SEIAR model with A compartment of absent unobserved infected estimated from hospital data with incorporated mobility data dependence; optimized to the compartment of all exposed (unobserved included) Metadata
MUNI-ARIMA Andrea Kraus, David Kraus Masaryk University ARIMA model with outlier detection fitted to transformed weekly aggregated series. Metadata
MUNI-LaggedRegARIMA Andrea Kraus, David Kraus Masaryk University Regression of hospitalizations and deaths on lagged cases with ARIMA errors. Metadata
MUNI-VAR Andrea Kraus, David Kraus Masaryk University Vector autoregression model fitted to outlier-corrected transformed weekly aggregated series. Metadata
PL_GRedlarski-DistrictsSum Grzegorz Redlarski Grzegorz Redlarski Modified SIR method, applied to all districts. Forecasts for districts are summed up. Metadata
prolix-euclidean Loïc Pottier prolix Offsets obtained by correlations, best linear approximation of reproduction rates (using vaccination approximation) by least euclidean distance, and linear prediction. Metadata
RobertWalraven-ESG Robert Walraven Robert Walraven Multiple skewed gaussian distribution peaks fit to raw data Metadata
SDSC_ISG-TrendModel Ekaterina Krymova, Dorina Thanou, Benjamin Bejar Haro, Tao Sun, Gavin Lee, Elisa Manetti, Christine Choirat, Antoine Flahault, Guillaume Obozinski Swiss Data Science Center / University of Geneva The Trend Model predicts daily cases and deaths using linear extrapolation on the linear or log scale of the underlying trend estimated by a robust LOESS seasonal-trend decomposition model. Metadata
Statgroup19-richards Marco Mingione, Pierfrancesco Alaimo Di Loro Statgroup19 Richards’ curve based generalized growth model Metadata
Statgroup19-spatialrichards Marco Mingione, Pierfrancesco Alaimo Di Loro Statgroup19 Richards’ curve based generalized growth model taking into account spatial dependence Metadata
UB-BSLCoV David Moriña UB Bayesian synthetic likelihood estimation for underreported non-stationary time series Metadata
UC3M-EpiGraph David E. Singh, Miguel Guzman Merino, Maria Cristina Marinescu, Jesus Carretero, Alberto Cascajo Garcia Universidad Carlos III de Madrid Agent-based parallel simulator that models individual interactions extracted from social networks and demographical data. Metadata
ULZF-SEIRC19SI Janez Zibert University of Ljubljana, Faculty of Health Sciences Team SEIHR model extended with compartments for hospitals, intensive care units, asymptomatic cases, separate submodels for vaccinated and unvaccinated, divided to 5 age subgroups of population Metadata
UMass-MechBayes Dan Sheldon, Graham Gibson, Nick Reich UMass-Amherst Bayesian compartmental model with observations on cumulative case counts and cumulative deaths. Model is fit independently to each state. Model includes observation noise and a case detection rate. Metadata
UMass-SemiMech Dan Sheldon, Graham Gibson, Nick Reich UMass-Amherst Bayesian semi-compartmental model with observations on incident case counts and incident deaths. Model is fit independently to each state. Model includes observation noise and a case detection rate. Metadata
UNED-PreCoV2 José L. Aznarte, César Pérez, José Almagro, Pedro Álvarez, Álvaro Ortiz, Fernando Blat UNED Bayesian time series models with ARIMA noise and fixed transfer functions for each input. Metadata
UNIPV-BayesINGARCHX Paolo Giudici, Barbara Tarantino UNIPV Periscope Working Group Bayesian estimation of time-dependent models with time-varying coefficients to predict COVID-19 positive counts. Metadata
UpgUmibUsi-MultiBayes Francesco Bartolucci, Fulvia Pennoni, Antonietta Mira UNIPG_UNIMIB_USI_UNINSUBRIA Bayesian Dirichlet-Multinomial models for counts of patients in mutually exclusive and exhaustive categories such as hospitalized in regular wards and in intensive care units, deceased and recovered Metadata
USC-SIkJalpha Ajitesh Srivastava, Frost Tianjian Xu University of Southern California A heterogeneous infection rate model with human mobility for epidemic modeling. Our model adapts to changing trends and provide predictions of confirmed cases and deaths. Metadata
USyd-OneModelMan Pablo Montero Manso University of Sydney Forecast Lab A single autoregressive model fit jointly to all European time series, adding time series from the top regions across the world. A high-dimensional manifold embedding is used capture the process. Metadata
UVA-Ensemble Aniruddha Adiga, Lijing Wang, Srinivasan Venkatramanan, Akhil Sai Peddireddy, Benjamin Hurt, Przemyslaw Porebski, Bryan Lewis, Madhav Marathe, Jiangzhou Chen, Anil Vullikanti University of Virginia, Biocomplexity COVID-19 Response Team An ensemble of multiple methods such as auto-regressive (AR)models with exogenous variables, Long short-term memory (lSTM) models,Kalman filter and PatchSim (an SEIR model). Metadata

Presentations

The ECDC hosts weekly calls which any forecasting team is welcome to join. Each week a different team is invited to present and discuss forecasting methods. Slides and extra content provided by the teams can be accessed below. Please note that slides might be released under a different license than the MIT license used for the rest of this website:

22/09/2022 Karlen pypm

08/09/2022 modelling_health_behaviours

19/05/2022 natural hybrid immunity

19/05/2022 ecdc immunity

05/05/2022 wastewater covid19

21/04/2022 UC3M EpiGraph

08/12/2021 Nowcasting Bracher

03/11/2021 ITWW county_repro

27/10/2021 Statgroup19 richards

20/10/2021 epiMOX SUIHTER

06/10/2021 UpgUmibUsi MultiBayes

22/09/2021 epiforecasts EpiNow2

07/09/2021 Hub forecast review

06/07/2021 Karlen pypm

22/06/2021 FIAS_FZJ Epi1Ger

08/06/2021 epiforecasts EpiExpert

25/05/2021 SEIAR MUNI

18/05/2021 Imperial

11/05/2021 ICM agentmodel

20/04/2021 MOCOS agent1

13/04/2021 UNED PreCoV2

30/03/2021 DSMPG bayes

23/03/2021 UNIPV BayesINGARCH

09/03/2021 Evaluation of interval forecasts

02/03/2021 The European Covid19 Forecast Hub