Table 1.

The summary of studies in terms of the applications of AI in epidemiology

Reference	Task	Data source & size	Model	Result
Parbat et al. (May 2020) [15]	Predict the total number of deaths, recovered cases, cumulative number of confirmed cases, and number of daily cases.	Johns Hopkins Github repository (https://github.com/CSSEGISandData/COVID-19) between 01/03/2020–30/04/2020 cases: 35,043 deaths: 1,147 recovered patients: 8,889.	Support vector regression model	The proposed model was efficient and has higher accuracy (more than 87%) than linear or polynomial regression methods.
Zeynep Ceylan (April 2020) [145]	Estimate the prevalence of COVID-19 in Italy, Spain, and France.	The data of COVID-19 collected from the WHO website (https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/) between 21/02/2020–15/04/2020 Italy: mean prevalence case 57,262, mean incidence case 3,009; Spain: mean prevalence case 54,075, mean incidence case 3,521; France: mean prevalence case 30,233, mean incidence case 2,092.	Auto-Regressive Integrated Moving Average (ARIMA) model	ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (0,2,1) showed the best prediction performance (more than 82% accuracy) for Italy, Spain, and France, respectively.
Benvenuto et al. (February 2020) [146]	Predict the epidemiological trend of the prevalence and incidence of COVID-2019	the Johns Hopkins epidemiological data (https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html)	Auto-Regressive Integrated Moving Average (ARIMA) model	ARIMA (1,0,4) and ARIMA (1,0,3) showed the best performance in terms of determining the prevalence and incidence of COVID-2019, respectively.
Rodriguez et al. (September 2020) [147]	Real-time COVID-19 forecasting including incidence and cumulative weekly deaths and Incidence daily hospitalizations.	Johns Hopkins University (JHU) COVID Tracking Project (https://covidtracking.com)	DeepCOVID including data module, prediction module, and explainability module based on deep learning model	The proposed model was used in CDC COVID-19 Forecast Hub (since April 2020).
Singh et al. (September 2020) [148]	Predict the spread of COVID-19	Data collected from Kaggle website (https://www.kaggle.com/imdevskp/covid19-corona-virus-india-dataset) Data covered 15 States of India.	Random Forest and Kalman Filter	The proposed model showed good performance in terms of short-term estimation, but not so good for long-term forecasting.
Zheng et al. (July 2020) [24]	Predict the development and spread of the COVID-19	Data collected from the national and provincial health commissions, and dxy.com website (Real-time data API for COVID-19 epidemic) (https://lab.isaaclin.cn/nCoV/zh)	Hybrid AI Model based on susceptible-infected (ISI) model and RNN model	The proposed model acquired the lower mean absolute percentage errors in Wuhan (0.52%), Beijing (0.38%), Shanghai (0.38%), and countrywide (0.86%) for the next 6 days.
Huang et al. (May 2021) [22]	Forecast the trend of COVID-19 pandemics under the influence of reopening policies.	Hospitalization and cumulative morality of COVID-19. Houston, Texas, May 1, 2020 – June 29, 2020	Risk-stratified SIR-HCD	The proposed model obtained lower mean squared error (MSE) and higher prediction accuracy compared to other models, and supports counterfactual analysis.
Liu et al. (May 2021) [149]	Investigate the influence (reproduction number) of non-pharmaceutical public health interventions on COVID-19 epidemics in the United States	COVID Tracking Project (https://covidtracking.com)	A generalized linear model (GLM)	Different NPIs showed different levels of reproduction numbers. The stay-at-home played the most important role and contributed approximately 51% (95% CI: 46%−57%). The gathering ban (more than 50 people) was not very important, which only contributed 7% (2%−11%).
Tian et al. (July 2020) [150]	Compare the effect of mild interventions in Shenzhen and countries in the United States	Daily cumulative confirmed cases of COVID-19 in Shenzhen, China and the countries in the United States (https://github.com/CSSEGISandData/COVID-19)	A synthetic control method with a modified selection of control variables and the proposed SIHR model	Implementing the early mild interventions has the potential to subdue the epidemic of COVID-19.
Zou et al. (May 2020) [23]	Forecast the spread of COVID-19	The Johns Hopkins University Center for Systems Science and Engineering; The New York Times data; The data from most states between 03/22/2020 and 05/10/2020. More than 40,000 cases.	SuEIR model	The proposed model has been adopted by the CDC for COVID-19 death forecasts.
Friedman et al. (May 2021) [151]	Predict mortality of patients with COVID-19	Public data: https://github.com/pyliu47/covidcompare.	SEIR model, Dynamic Growth, SIKJalpha.	Seven predictive models that showed better performance which had a median absolute percent error of 7% to 13% at six weeks.
Murray et al. (March 2020) [152]	Predict hospital bed-days, ICU-days, ventilator-days and deaths	Data from local government, national government, and WHO websites were used.	A statistical model based on parametrized Gaussian error function	They forecasted total beds (64,175), ICU beds (17,380), ventilators (19,481), deaths (81,114) at the peak of COVID-19 in the United States between March to June 2020.
Hsiang et al. (September 2020) [153]	Investigate the effect (rate of transmission) of non-pharmaceutical public health interventions on COVID-19 epidemics in China, South Korea, Italy, Iran, France and the United States	COVID-19 data collected from government reports, policy briefings and news articles (https://github.com/bolliger32/gpl-covid)	Reduced-form econometric model	The proposed model showed the interventions can reduce the rate of transmission and delay on the order of 61 million confirmed cases across 6 countries.
Li et al. (January 2021) [154]	Predict the epidemic trends in terms of future confirmed cases within 7 days	Coronavirus Update (Live): (https://www.worldometers.info/coronavirus/) Coronavirus (COVID-19) Lockdown Tracker Aura Vision. (https://auravision.ai/covid19-lockdown-tracker/) List of countries and dependencies by population: (https://en.wikipedia.org/w/index.php?title=List_of_countries_and_dependencies_by_population&oldid=960653268)	A transfer learning method called ALeRT-COVID using attention-based RNN architecture	ALeRT-COVID obtained a higher prediction in terms of future confirmed cases
Wang et al. (May 2021) [155]	Investigate the impact of the temperature and relative humidity on effective reproductive number in COVID-19 epidemics	Records of 69,498 patients from Chinese National Notifiable Disease Reporting System and 740,843 confirmed cases from COVID-19 database of JHU CSSE (https://github.com/CSSEGISandData/COVID-19/).	Fama-Macbeth Regression [34]	High temperature and humidity can make contributions to the reduction of the transmission of COVID-19.
Rockett et al. (July 2020) [25]	Revealing COVID-19 transmission in Australia	Data collected from infected patients during the first 10 weeks of COVID-19 containment in Australia, which reported by New South Wales (NSW) Ministry of Health	Agent-based model	The predictions from ABM were concordant with the local transmission rates.
Alzu'bi et al. (December 2020) [25]	Investigate the effect of non-pharmaceutical public health interventions on COVID-19 epidemics	Coronavirus data collected from two urban neighborhoods separated by crossings. 1,000 persons.	Agent-based model by extending the SIR model	The policies including staying home and hospital isolation policies, and preventing travel between cities made contributions to the reduction of the prevalence and the deaths.
Brauer et al. (May 2021) [156]	Estimated global access to handwashing with soap and water	Observational surveys in the context of the Global Burden of Diseases, Injuries, and Risk Factors Study in terms of access to a handwashing station with available soap and water for 1,062 locations from 1990 to 2019.	Spatiotemporal Gaussian process regression modeling	The handwashing access should be considered when building the forecasting models of COVID-19 in terms of low-income counties.
Jr et al. (October 2020) [157]	Investigate the effect of social distancing mandates and levels of mask use	COVID-19 case and mortality data from 1 February 2020 to 21 September 2020 in the United States	SEIR model	Keeping universal mask use was enough to relieve the worst effects of epidemic resurgences in multiple states in the United States. Keeping social distancing was helpful for reducing the number of deaths for patients with COVID-19.