Table 1.

A summary of recent COVID-19 forecast models.

(A) Recent COVID-19 forecast models
Types	Method	Performance				Religion	Ref.
		MAE	RMSE	Pearson	Spearman
Dynamics	SAHQD model (Susceptible, infected, hospitalized, quarantined, deceased)	N.P.	N.P.	N.P.	N.P.	U.S.	[19]
	SCUAQIHMRD model (Susceptible, close contact, uninfected under home quarantine, asymptomatic under home quarantine, mild symptoms under home quarantine, severe symptoms under home quarantine, infectious in Designed Hospitals, infectious in Fangcang Hospitals, Recovered, Death)	N.P.	N.P.	N.P.	N.P.	Wuhan, China	[31]
	SEPIAHR model (Susceptible, exposed, pre-symptomatic infectious, ascertained infectious, unascertained infectious, isolation in hospital and removed	N.P.	N.P.	N.P.	N.P.	Wuhan, China	[30]
	SEIAIR model (Susceptible, incubation, asymptotic infected, recovered)	N.P.	N.P.	N.P.	N.P.	Wuhan, China	[28]
	SEIRMH model (Susceptible, exposed without symptoms, infected with symptoms, with medical care, and removed from the system)	N.P.	N.P.	0.84	N.P.	Belgium	[29]
	Adaptive interacting cluster-based SEIR (AICSEIR) model	N.P.	N.P.	0.84	N.P.	Italy, the U.S., and India	[44]
	modified SEIR model (Including vaccination)	N.P.	N.P.	N.P.	N.P.	NYC, U.S.	[45]
	SEIR model with Bayesian inference	N.P.	N.P.	N.P.	N.P.	Israel	[46]
	SLIR model (Susceptible, latent, infected, recovered	N.P.	N.P.	N.P.	N.P.	China	[27]
	SEIR model	N.P.	N.P.	N.P.	N.P.	Texas, USA	[26]
	Sequential compartmental models	N.P.	N.P.	N.P.	N.P.	Homeless Shelter, Chicago, Illinois, USA	[47]
Time series	smooth transition autoregressive (STAR) model	0.208	0.297	N.P.	N.P.	Africa sub-region	[35]
	Linear AR model	0.251	0.385	N.P.	N.P.	Africa sub-region	[35]
	ARIMA	27.86	35.69	N.P.	N.P.	Malaysia	[32]
	ARIMA	N.P.	N.P.	N.P.	N.P.	France	[33]
	Modified VAR regression	47.43	N.P.	N.P.	N.P.	NYC, U.S.	[36]
	Linear regression	N.P.	7.562	N.P.	N.P.	Iran	[40]
	Poisson count time series model (Disease surveillance and Twitter-based population mobility data)	N.P.	N.P.	N.P.	N.P.	South Carolina	[48]
	ARIMA	50.109	95.322	N.P.	N.P.	India	[34]
Grey forecast	Fractional Order Accumulation Grey Model (FGM)	N.P.	109496/96411/14560/64253/15/1123/106223	N.P.	N.P.	U.S., France, UK, Germany, China, Japan, India	[37]
	Hybrid grey exponential smoothing approach	N.P.	5.05	N.P.	N.P.	Sri Lanka	[38]
	Internally Optimized Grey Prediction Models (IOGMs)	N.P.	N.P.	N.P.	N.P.	Rajasthan, Maharashtra, Delhi	[39]
ML methods	random forest regression algorithm	5.42	9.27	0.89	0.84	215 countries and territories	[49]
	long short-term memory (LSTM) models	N.P.	27.187	N.P.	N.P.	Iran	[40]
	multilayer perceptron (MLP) neural network (n hidden layer)	0.36 (n = 1) 0.40 (n = 2)	0.64 (n = 1) 0.84 (n = 2)	0.36 (n = 1) 0.47 (n = 2)	N.P.	U.S.	[50]
	Pearson correlation test and general linear model	N.P.	N.P.	0.978	N.P.	U.S.	[51]
	a simple random forest statistical model	N.P.	N.P.	0.89	N.P.	Ohio, U.S.	[52]
	WEKA tool	≈1200	≈1000	N.P.	N.P.	Pakistan	[53]
	deep interval type-2 fuzzy LSTM (DIT2FLSTM)	N.P.	N.S.	N.P.	N.P.	USA, Brazil, etc.	[41]
	generalized linear and tree-based machine learning models	0.21	N.P.	0.99	N.P.	Tennessee	[54]
	an ensemble of 10 LSTM-based networks	90.38	N.P.	N.P.	N.P.	The county-level in the US	[42]
	LSTM + Rt method	N.P.	N.P.	0.872	N.P.	West Virginia	[43]
	Least-Square Boosting Classification algorithm	1200	N.P.	N.P.	N.P.	Countries having maximum number >2000 of confirmed cases in a day	[55]

(B) Comparison between different types of COVID-19 forecast models
Types	Strength	Weakness
Dynamics	➢Able to forecast over a wide future time window ➢The physical meaning of the model is very clear	➢Cannot be adapted to situations where the model subject has increased or where model parameters change with specific policies, disease pathogen variability, etc. ➢High requirements for parameter estimation ➢High demands on data sources, some of which are often missing or inaccessible, and their neglect often leads to unrealistic model assumptions
Time series	➢Simple and reproducible steps ➢The required data are easily available ➢Particularly suitable for cases where time series are periodic	➢Application scenarios are limited, e.g. ARMA model can only be applied to stationary situations, etc.
Grey forecast	➢Simple and reproducible steps ➢The required data are easily available	➢Similar to fuzzy mathematical theory, the physical meaning of the model is not clear
Machine learning	➢Higher potential for hybrid integration ➢Satisfactory performance for information mining ➢Satisfactory accuracy if the amount of data permits	➢Features obtained by deep learning methods do not have a clear physical meaning ➢The generalization ability of the model may be insufficient

Notes: N.P. = Not provided. Meanwhile, it is worth noting that although we give specific model performance in above table, it is not generalizable and comparable across datasets due to the different number of infections within different geographic regions.