Table 1.

List of models, with key characteristics

Team	Model abbreviation	Model description	Ref.	Ext. data	Mech. model	Ens. model
CU	EAKFC_SEIRS	Ensemble adjustment Kalman filter SEIRS	(20)	x	x
	EAKFC_SIRS	Ensemble adjustment Kalman filter SIRS	(20)	x	x
	EKF_SEIRS	Ensemble Kalman filter SEIRS	(21)	x	x
	EKF_SIRS	Ensemble Kalman filter SIRS	(21)	x	x
	RHF_SEIRS	Rank histogram filter SEIRS	(21)	x	x
	RHF_SIRS	Rank histogram filter SIRS	(21)	x	x
	BMA	Bayesian model averaging	(22)
Delphi	BasisRegression*	Basis regression, epiforecast defaults	(23)
	DeltaDensity1*	Delta density, epiforecast defaults	(24)
	EmpiricalBayes1*	Empirical Bayes, conditioning on past 4 wk	(23, 25)
	EmpiricalBayes2*	Empirical Bayes, epiforecast defaults	(23, 25)
	EmpiricalFuture*	Empirical futures, epiforecast defaults	(23)
	EmpiricalTraj*	Empirical trajectories, epiforecast defaults	(23)
	DeltaDensity2*	Markovian Delta density, epiforecast defaults	(24)
	Uniform*	Uniform distribution
	Stat	Ensemble, combination of 8 Delphi models	(24)			x
LANL	DBM	Dynamic Bayesian SIR model with discrepancy	(26)		x
ReichLab	KCDE	Kernel conditional density estimation	(27)
	KDE	Kernel density estimation and penalized splines	(28)
	SARIMA1	SARIMA model without seasonal differencing	(28)
	SARIMA2	SARIMA model with seasonal differencing	(28)
UTAustin	EDM	Empirical dynamic model or method of analogues	(29)

Team abbreviations: CU, Columbia University; Delphi, Carnegie Mellon; LANL, Los Alamos National Laboratories; ReichLab, University of Massachusetts-Amherst; SEIRS, Suceptible-Exposed-Infectious-Recovered-Susceptible, and SIRS, Suceptible-Infectious-Recovered-Susceptible, compartmental models of infectious disease transmission; UTAustin, University of Texas at Austin. The “Ext. data” column notes models that use data external to the ILINet data from CDC. The “Mech. model” column notes models that rely to some extent on a mechanistic or compartmental model formulation. The “Ens. model” column notes models that are ensemble models.

*Note that some of these components were not designed as standalone models, so their performance may not reflect the full potential of the method’s accuracy (Materials and Methods).