| Model | Final classification | Agreement | Total raters | Semi-mechanistic | Mechanistic | Agent-based | Statistical | Judgement | Other | Machine Learning |
|---|---|---|---|---|---|---|---|---|---|---|
| AMM-EpiInvert | Statistical | FALSE | 3 | 0 | 0 | 0 | 2 | 0 | 1 | 0 |
| DSMPG-bayes | Semi-mechanistic | FALSE | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| ILM-EKF | Semi-mechanistic | FALSE | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| ITWW-county_repro | Semi-mechanistic | FALSE | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| Imperial-DeCa | Semi-mechanistic | FALSE | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| Imperial-RtI0 | Semi-mechanistic | FALSE | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| Imperial-sbkp | Semi-mechanistic | FALSE | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| KITmetricslab-bivar_branching | Statistical | FALSE | 3 | 1 | 0 | 0 | 2 | 0 | 0 | 0 |
| Karlen-pypm | Mechanistic | FALSE | 3 | 0 | 2 | 0 | 1 | 0 | 0 | 0 |
| LANL-GrowthRate | Semi-mechanistic | FALSE | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| SDSC_ISG-TrendModel | Statistical | FALSE | 3 | 0 | 0 | 0 | 2 | 0 | 1 | 0 |
| UMass-SemiMech | Semi-mechanistic | FALSE | 4 | 3 | 1 | 0 | 0 | 0 | 0 | 0 |
| USC-SIkJalpha | Mechanistic | FALSE | 4 | 1 | 3 | 0 | 0 | 0 | 0 | 0 |
| UpgUmibUsi-MultiBayes | Semi-mechanistic | FALSE | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| bisop-seirfilter | Mechanistic | FALSE | 4 | 1 | 3 | 0 | 0 | 0 | 0 | 0 |
| bisop-seirfilterlite | Mechanistic | FALSE | 4 | 1 | 3 | 0 | 0 | 0 | 0 | 0 |
| prolix-euclidean | Semi-mechanistic | FALSE | 4 | 3 | 0 | 0 | 0 | 0 | 1 | 0 |
| CovidMetrics-epiBATS | Statistical | TRUE | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 |
| EuroCOVIDhub-baseline | Statistical | TRUE | 4 | 0 | 0 | 0 | 4 | 0 | 0 | 0 |
| FIAS_FZJ-Epi1Ger | Mechanistic | TRUE | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |
| GoeWroc-BaseBayes | Semi-mechanistic | TRUE | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| HZI-AgeExtendedSEIR | Mechanistic | TRUE | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |
| ICM-agentModel | Agent-based | TRUE | 3 | 0 | 0 | 3 | 0 | 0 | 0 | 0 |
| IEM_Health-CovidProject | Mechanistic | TRUE | 4 | 0 | 4 | 0 | 0 | 0 | 0 | 0 |
| JBUD-HMXK | Mechanistic | TRUE | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |
| LeipzigIMISE-SECIR | Mechanistic | TRUE | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |
| MIMUW-StochSEIR | Mechanistic | TRUE | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |
| MIT_CovidAnalytics-DELPHI | Mechanistic | TRUE | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |
| MOCOS-agent1 | Agent-based | TRUE | 3 | 0 | 0 | 3 | 0 | 0 | 0 | 0 |
| MUNI-ARIMA | Statistical | TRUE | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 |
| MUNI-LaggedRegARIMA | Statistical | TRUE | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 |
| MUNI-VAR | Statistical | TRUE | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 |
| MUNI_DMS-SEIAR | Mechanistic | TRUE | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |
| PL_GRedlarski-DistrictsSum | Mechanistic | TRUE | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |
| RobertWalraven-ESG | Statistical | TRUE | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 |
| UB-BSLCoV | Statistical | TRUE | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 |
| UC3M-EpiGraph | Agent-based | TRUE | 3 | 0 | 0 | 3 | 0 | 0 | 0 | 0 |
| ULZF-SEIRC19SI | Mechanistic | TRUE | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |
| UMass-MechBayes | Mechanistic | TRUE | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |
| UNED-PreCoV2 | Statistical | TRUE | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 |
| UNIPV-BayesINGARCHX | Statistical | TRUE | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 |
| epiMOX-SUIHTER | Mechanistic | TRUE | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |
| epiforecasts-EpiExpert | Judgement | TRUE | 3 | 0 | 0 | 0 | 0 | 3 | 0 | 0 |
| epiforecasts-EpiExpert_Rt | Judgement | TRUE | 3 | 0 | 0 | 0 | 0 | 3 | 0 | 0 |
| epiforecasts-EpiExpert_direct | Judgement | TRUE | 3 | 0 | 0 | 0 | 0 | 3 | 0 | 0 |
| epiforecasts-EpiNow2 | Semi-mechanistic | TRUE | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| epiforecasts-weeklygrowth | Statistical | TRUE | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 0 |
| itwm-dSEIR | Mechanistic | TRUE | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |
Covariate derivation
Model structure classification
Human raters classified models according to methodological structures.
Epidemic trend identification
We categorised each week as “Stable”, “Decreasing”, or “Increasing”, based on the difference over a three-week moving average of incidence (with a change of +/-5% as “Stable”).
Variant phase identification
Genomic surveillance data were obtained from three sources: ECDC (covering 30 European countries), UKHSA (Great Britain), and the Swiss Federal Office of Public Health (Switzerland). Variant lineages were mapped to six named phases in expected chronological order: Alpha, Delta, Omicron-BA.1, Omicron-BA.2, Omicron-BA.4/5, and Omicron-BQ/XBB. For each country, we identified the first week in which each named variant exceeded 50% of sequenced samples. We enforced chronological ordering by removing any out-of-sequence phases, then expanded phase assignments to all weeks by filling forward and backward from observed transition dates. This per-location approach accounts for the fact that variant dominance dates differed substantially across European countries. Where genomic surveillance data were too sparse to identify a transition (Hungary), we supplemented with epidemiological reports to set the Alpha-to-Delta transition date.
