References	How Was SM or SN Used?	Platform/ Application?	Calibrated with SM or SN?	Goal/Predict no of?	Type	Model
[96]	-----	-----	No	Susceptible cases Infected cases Recovered cases	Epidemiological State models	Susceptible, Infected, Recovered (SIR)
[97]	-----	-----	No	Susceptible cases infected cases	Epidemiological State models	Susceptible, Infected, Susceptible (SIS)
[98]	-----	-----	No	Susceptible cases infected cases Recovered cases Deaths cases	Epidemiological State models	Susceptible, Infected, Recovered, Deceased (SIRD)
[99]	-----	-----	No	Susceptible cases infected cases Recovered cases	Epidemiological State models	Maternally derived immunity, Susceptible, Infected, Recovered (MSIR)
[82]	Use media to modify public behavior	Media awareness programs	Yes/SM	Exposed cases Infected cases Recovered cases	Epidemiological State models	Susceptible, Exposed, Infected (SEI)
[100]	-----	-----	No	Susceptible cases Exposed cases infected cases Recovered cases	Epidemiological State models	Susceptible, Exposed, Infected, Recovered (SEIR)
[101]	-----	-----	No	Susceptible cases Exposed cases infected cases	Epidemiological State models	Susceptible, Exposed, Infected, Susceptible (SEIS)
[102]	-----	-----	No	Susceptible cases Exposed cases infected cases Recovered cases	Epidemiological State models	Maternally derived immunity, Susceptible, Exposed, Infected, Recovered (MSEIR)
[103]	-----	-----	No	Susceptible cases Exposed cases infected cases Recovered cases	Epidemiological State models	Maternally derived immunity, Susceptible, Exposed, Infected, Recovered, Susceptible (MSEIRS)
[104]	-----	-----	No	Susceptible cases Latent cases Infected cases Recovered cases Deaths cases	Epidemiological State models	Susceptible-Latent-Infected-Recovered-Dead-Susceptible (SLIRDS)
[105]	Use media to modify public behavior	Media awareness programs	Yes/SM	Exposed cases Infected cases Hospitalized cases	Epidemiological State models	Exposed, Infected, Hospitalized (EIH)
[78]	Use media to modify public behavior	Media awareness programs	Yes/SM	Susceptible cases Infected cases Hospitalized cases Recovered cases	Epidemiological State models	Susceptible, Infected, Hospitalized, Recovered (SIHR)
[106]	-----	-----	No	Infected cases Hospitalized cases Deaths cases	Epidemiological Statistical Forecast Models	Differential Equations Leads to Predictions of Hospitalizations and Infections (DELPHI);
[83]	Data source	Google and Twitter	Yes/SM	Infected cases	Epidemiological Statistical Forecast Models	Auto regressive integrated moving average (ARIMA)
[61]	-----	-----	No	Infected cases Death cases Predict time of pandemic peak	Epidemiological Statistical Forecast Models	Los Alamos National Laboratory COVID-19 forecasting using Fast Evaluation and Estimation (LANL COFFEF)
[107]	-----	-----	No	“Forecast how likely a patient’s disease is to worsen while being treated in a hospital and at what point in their care that might happen”	Epidemiological Statistical Forecast Models	John Hopkins model COVID-19 prediction (JHU COVID-19 prediction)
[20]	-----	-----	No	Susceptible cases Exposed cases Infected cases Quarantined cases Recovered cases Vaccinated cases	Epidemiological Statistical Forecast Models	Susceptible, Exposed, Infected, Quarantined, Recovered, Dead, Vaccinated forecasting (SEIQRDV.F).
[26]	Data source	Facebook	Yes/SM	Promote population to follow healthy behavior Predict changes in health behaviors of individuals	Theoretical Interventions model	Health Belief Model (HBM)
[27]	-----	-----	No	Predict the human behavior	Theoretical Interventions model	The Theory of Planned Behavior (TPB)
[28]	-----	-----	No	Explains how individuals are motivated to act to protect themselves	Theoretical Interventions model	Protection Motivation Theory (PMT)
[51]	-----	-----	No	Modeling disease dynamics and fear as two interacting contagion processes	Agent-based model	The Coupled Contagion Dynamics of Fear and Disease (CCDFD) model
[52]	-----	-----	No	Testing effects of different levels of social distancing policies on the diseases spread	Agent-based model	The Social Distancing (SD) model
[53]	-----	-----	No	Project epidemic trends Explore intervention scenarios Estimate resource needs.	Agent-based model	COVID-19 Agent-based Simulator (COVASIM) model
[54]	-----	-----	No	Simulate the epidemiological and economic impacts of social distancing policies	Agent-based model	COVID-19 agent-based simulation (COVID-ABS) model
[55]	-----	-----	No	“Effectiveness of a nationwide vaccine campaign in response to different vaccine efficacies, the willingness of the population to be vaccinated, and the daily vaccine capacity under two different federal plans”. Studying the interactions between nonpharmaceutical interventions and vaccines	Agent-based model	COVID-19 Agent-based Simulator (COVASIM) and Vaccination model
[50]	-----	-----	No	Susceptible cases Infected cases Recovered cases Quarantine impact Transport restrictions impact Effectiveness of the interventions on the disease spread	Multiagent system model	DMAS-SIR model
[57]	Data source	Mobile phones-Calls	Yes/SN	Trace users’ phones and their mobility through network to study effects of government’ interventions on virus spread	Agent-based model	Frias-Martinez model (FM)
[56]	Data source	Mobile phones-GPS traces	Yes/SN	Trace users’ phones and their mobility through GPS to study effects of government’ interventions on virus spread	Agent-based model	University of Texas at Austin’s (UT COVID-19-Social distancing) model
[59]	-----	-----	No	Infected cases Deaths cases	Artificial Intelligence and Hybrid models	Y Youyang Gu COVID-19 (YYG) model
[61]	-----	-----	No	Processing population’ images to detect who wear mask or who not	Artificial Intelligence and Hybrid models	Deep transfer learning (DTL) model
[62,63]	Data source	Mobile phones-GPS	Yes/SN	Effectiveness of the interventions on the disease spread No of required beds and at hospitals and care units Trace users’ phones and their mobility through GPS	Artificial Intelligence and Hybrid models	University of Virginia Biocomplexity Center PatchSim COVID-19 (UVA COVID-19)
[66]	Data source	Mobile phones-GPS	Yes/SN	Effectiveness of the interventions on the disease spread Trace users’ phones and their mobility through GPS	Artificial Intelligence and Hybrid models	Institute for Health Metrics and Evaluation COVID-19 (IHME COVID-19)
[67]	-----	-----	No	Infected cases Deaths cases No of required beds and at hospitals and care units	Artificial Intelligence and Hybrid models	Massachusetts Institute of Technology COVID-19 (MIT University COVID-19) model
[87]	Data source	Twitter/Users’ tweets	Yes/SM	Study and analyze Twitter users’ opinions, beliefs, and emotions about vaccination	Artificial Intelligence and Hybrid models	Twitter vaccination analysis (TWVA) model