Identifying Techniques and Models for COVID-19 Prediction

Document Type : Review article


1 Department of Health Information Technology, Esfarayen Faculty of Medical Sciences, Esfarayen, Iran

2 Nursing School, University of British Columbia, Vancouver, Canada

3 Department of Health Information Technology, Khalkhal University of Medical Sciences, Khalkhal, Iran

4 Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States

5 School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

6 Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High Risk Behaviors, Tehran University of Medical Sciences, Tehran, Iran, School of Nursing, University of British Columbia, Vancouver, Canada

7 Department of Midwifery, School of Nursing and Midwifery, Kermanshah University of Medical Sciences, Kermanshah, Iran

8 School of Health Information Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran

9 Khalkhal University of Medical Sciences, Khalkhal, Iran

10 School of Nursing, University of British Columbia, Vancouver, Canada


Background: Technologies can predict various aspects of COVID-19, such as early prediction of cases and those ‎at higher risks of severe disease. Predictions will yield numerous benefits and can result in a lower number of cases ‎and deaths. Herein, we aimed to review the published models and techniques that predict various ‎COVID-19 outcomes and identify their role in the management of the COVID-19.‎ 
Methods: This study was a review identifying the prediction models and techniques for management of the COVID-19. Web of Science, Scopus, and PubMed were searched from December 2019 until ‎September 4th, 2021. In addition, Google Scholar was also searched.‎ 
Results: We have reviewed 59 studies. The authors reviewed prediction techniques in COVID-19 disease ‎management. Studies in these articles have shown that in the section medical setting, most of the subjects were ‎inpatients. In the purpose of the prediction section, mortality was also the most item. In the type of data/predict ‎section, basic patient information, demographic, and laboratory values were the most cases. Also, in the type of ‎technique section, logistic regression was the most item used. Training, internal and external validation, and cross-validation were among the issues raised in the type of validation section.‎ 
Conclusion: Artificial intelligence and machine learning methods were found to be useful in disease control and ‎prevention. They accelerate the process of diagnosis and move toward great progress in emergency ‎circumstances like the COVID-19 pandemic.‎  


Main Subjects

Background: Technologies can predict various aspects of COVID-19, such as early prediction of cases and those ‎at higher risks of severe disease. Predictions will yield numerous benefits and can result in a lower number of cases ‎and deaths. Herein, we aimed to review the published models and techniques that predict various ‎COVID-19 outcomes and identify their role in the management of the COVID-19.‎ 
Methods: This study was a review identifying the prediction models and techniques for management of the COVID-19. Web of Science, Scopus, and PubMed were searched from December 2019 until ‎September 4th, 2021. In addition, Google Scholar was also searched.‎ 
Results: We have reviewed 59 studies. The authors reviewed prediction techniques in COVID-19 disease ‎management. Studies in these articles have shown that in the section medical setting, most of the subjects were ‎inpatients. In the purpose of the prediction section, mortality was also the most item. In the type of data/predict ‎section, basic patient information, demographic, and laboratory values were the most cases. Also, in the type of ‎technique section, logistic regression was the most item used. Training, internal and external validation, and cross-validation were among the issues raised in the type of validation section.‎ 
Conclusion: Artificial intelligence and machine learning methods were found to be useful in disease control and ‎prevention. They accelerate the process of diagnosis and move toward great progress in emergency ‎circumstances like the COVID-19 pandemic.‎  
Keywords: COVID-19, Diagnosis, Prediction, SARS-CoV-2 

Since coronavirus disease 2019 (COVID-19) emerged, it has caused nearly 272 million cases and 5.3 million deaths as of December 20th, 2021 (1). COVID-19 is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) (2). This disease can invoke systemic inflammatory response that can involve various organs in the body, most notably the lungs (3-5). SARS-CoV-2 also has noticeable potentials for mutations that can cause further increase in incidence and mortality rates if not controlled properly and cause new and more dangerous variants, such as Delta and Omicron (6). Global vaccinations have been useful to contain the spreading and mortality of this disease, but still a long road is ahead to vaccinate a proper portion of people worldwide, especially in lower income countries (1,7). Overall, high incidence and deaths caused by the COVID-19 as well as its economic and social detriments attracted specific attentions, and gaining knowledge on its various aspects seems necessary (6,8). 
Predicting different features of this disease have their specific benefits (9,10). Various technologies exist to predict the parameters related to this disease, such as Artificial Intelligence (AI)-based technologies (11-13). For example, predicting the cases earlier will lead to earlier diagnoses that may cause better quarantines to decrease the risk of transmission, as well as more timely management of the patients towards better outcomes (12). Furthermore, contacts of the sick patients will be notified earlier and seek measures to limit their spread to others and improve their health during the disease period (14-17). Several groups of people are also at increased risk for severe COVID-19 outcomes, e.g., older patients, those with underlying conditions, or the patients with high values of some inflammatory conditions (18,19). Prediction methods can identify those that are probably at higher risks for severe COVID-19; and therefore, offer greater preventive measures to these groups to avoid contracting the disease in the first place, and if infected, place specific emphasis on their early and correct treatment (11,20). For instance, such groups with underlying diseases may be advised to receive online chronic disease management to limit their contacts with healthcare facilities and other possible dangerous sites (13,16,21,22).
To the best of our knowledge, many studies and systematic reviews are available to this date that focus on the benefits and applications of technologies during the COVID-19 pandemic. However, the literature lacks adequate systematic reviews on prediction methods associated with the COVID-19. Therefore, the authors aimed to systematically review the published models and techniques that predict various COVID-19 outcomes and identify their role in the management of the disease.

Materials and Methods
This study was a review to study the prediction models and techniques for management of the COVID-19. Scopus, PubMed, and Web of Science were searched from December 2019 until September4th, 2021. In addition, Google Scholar was also searched.

Search strategy
Search strategy was organized by first and cor-responding authors. The keywords were as the following: COVID-19, SARS-CoV-2, prediction, and system. The complete search strategy was as follows:
A: COVID-19 OR “Coronavirus” OR “Corona virus” OR SARS-CoV-2
B: Prognostic OR Prognoses OR Prognosis OR Prediction OR Diagnosis OR Prognostication OR Anticipation OR Forecast
C: Design OR Development OR Implementation OR System
D: [A] AND [B] AND [C]

Eligibility criteria
The authors included all studies retrieved from databases that report the prediction, incidence and diagnosis of COVID-19 disease. Excluded articles were at least one of the following criteria:
- Non-original studies, including position papers, case reports, case series, reviews, editorials, comments, and clinical trial protocols.
- Non-full texts articles, short communications, conference abstracts, and abstract papers.
- Any duplicated outcomes in databases.
- Non-human studies.
- Non-English language.

Data screening and selection study
EndNote X9 software was used to manage studies. Search results joined in a single EndNote library and duplicate studies of the similar reports removed. Three authors independently screened titles and abstracts of the retrieved articles to evaluate whether they meet the inclusion and exclusion criteria of the selected articles.

Data extraction
The following data were extracted from eligible studies: first author, type of study, country, population, medical setting, purpose of prediction, type of data/predict, type of technique, type of validation, classification measure, Area Under the Receiver Operating Characteristic (AUROC), and other findings. Data were extracted independently by three authors. The corresponding author resolved discrepancies in data extraction and checked the retrieved data to rule out duplication.

Quality assessment
This study was a review to study the prediction models and techniques for management of the COVID-19. Three independent and experienced authors checked the quality of the studies and the probable risk of bias. Any disagreement in judgment was resolved by the first author review and consensus.

In this study, 306 articles were collected using a systematic search strategy. After the initial review of the retrieved articles, 87 items were deleted due to duplication and the titles and abstracts of the remaining 117 articles were reviewed. After reviewing the inclusion criteria, 58 articles were removed and 59 articles had inclusion criteria and were included in the final review (Figure 1).
In the type of data/predict section, basic patient information, demographic characteristics, and laboratory values were the most common cases. Also in the type of technique section, logistic regression was the most item. Training, internal and external validation, and cross-validation were among the issues raised in the type of validation section. 
Most studies were related to China with 20 articles, the USA with 14 articles, and Italy and Spain with 4 articles. The rest of the selected articles were for South Korea with 3 articles and Philippines, UK, France, and India with 2 articles. Israel, Greece, Algeria, Mexico, Austria, Japan, Switzerland, and Turkey also had one article. 
Eventually, modeling method, final model, and sample size of training were expressed in other findings (Table 1).


Table 1: Prediction methods to management of COVID-19

First author (re)



Medical setting

Purpose of prediction

Type of data/ predict

Type of technique

Type of validation

Classification measure


(area Under the receiver operating characteri-


Other findings

Prediction accuracy rate


Abdulaal A, et al (23)

398 patients with COVID-




Demographics, comorbidities, smoking history, and presenting symptoms

Neural network

k-fold cross-validation


Patient-specific mortality= 86.25%,

with a sensitivity=

87.50%, The negative predictive value=


The AUROC was 90.12%.

Predict mortality for that admission: Digital format, ANN. The ANN

Allenbach Y, et al (24)

152 patients with COVID-19


Being discharged alive and severe status at D14 (remaining with ventilation, or death), intensive care unit (ICU) transfer or death at day 14 (D14)

Demography and epidemiology features

the clinical presentation along with the laboratory

comorbidity profile.

Routine blood examinations

chest computed tomography (CT) scan

echocardiogram data

previous treatments

Logistic regression

Internal and external validation of the model:

-the C-index (equivalent to AUC)= 0.80

-after correction for over-optimism by resampling= 0.78

-on the external cohort= 0.78

ICU transfer or death= 32%

A 9-point ordinal scale scoring system:

-defined low (score 0–2)

-moderate (score 3–5)

-high (score 6–8) risk patients




A simplified scoring system: admission predicted at D14

Barda N, et al (25)

Patients with COVID-19



Primary care, specialist care, laboratory data, in-network hospitalization data, imaging data


External validation, training–validation


At a 5% risk threshold, 15% of patients are marked as high-risk, achieving a sensitivity of 88%


The baseline model:

-initial training for feature selection

-final training for the creation of the baseline model

Bartoletti M, et al (26)

1113 patients with COVID-19



Age, sex, body mass index, being obese, Hypertension. Immunosup-pression, solid organ transplan-

tation, hematopoietic stem cell transplan-

tation, corticosteroid therapy, uncontrolled human immunodefi-ciency virus infection

The multivariate logistic regression

Validation cohort

At a score of >3, sensitivity:  71.6% (65–79%)

At a score of >3, sensitivity:  80%


At a score of >3, specificity, and positive and negative predictive values were:

-89.1% (86–92%)

-74% (67–80%)

-89% (85–91%)


-PREDI-CO score: to allocate resources and prioritize treatments

-Risk factors for SRF

Bellos I, et al (27)

67 patients with COVID-19



Demographic, clinical and laboratory findings




Specificity:  93.3%




-Selection operator (LASSO) regression model

-A10-variable: to predict critical illness amongst hospitalized COVID-19 patients

Bennouar S, et al (28)

330 patients with COVID-19



CRP and a total blood count with the calculation of the NLR ratio, blood glucose and renal function markers including blood urea nitrogen, serum creatinine, and electrolytes (sodium and potassium), albumin and total protein, hepatic enzymes: LDH, GOT and GPT, γ-GT and alkaline phosphatases (PAL)


Validation cohort


0.74 [0.66–0.82] and 0.90 [0.87–0.94], p<0.0001, respectively for severity and mortality prediction



Berenguer J, et al (29)

4035 patients with COVID-19



Demographics, comorbidities defined as diagnoses, signs or symptoms, low age-adjusted capillary SaO2 on room air, test results, including white cell count, neutrophil-to-lymphocyte ratio, platelet count, INR, eGFR measured

Multivariable logistic regression

External validation cohort


-The risk low: 0–2.1%

-moderate: 4.7–6.3%



-very high: 27.7–100%


SEIMC score.

A simple prediction score, based on readily available clinical and laboratory data, provides a useful tool to predict 30-day mortality probability with a high degree of accuracy among hospitalized patients with COVID-19

Berry DA, et al (30)

3123 patients with COVID-19



Demographic, clinical characteristics, treatments






Most important characteristic for survival: age

Bolourani S, et al (31)

11,525 patients with COVID-19


Predict 48-hour respiratory failure



-home medications

-initial vitals

-laboratory values


-clinical outcomes

Logistic regression

Cross-hospital validation, external validation




The XGBoost + SMOTEENN method (combined oversampling using SMOTE): to predict 48-hr respiratory failure in admitted patients with COVID-19

Booth AL, et al (32)

398 patients with COVID-19



C-reactive protein, blood urea nitrogen, serum calcium, serum albumin, and lactic acid

Logistic regression



91% specificity


-Pairwise relationship between each laboratory value

-SHAP value

the magnitude of other members of the five selected laboratory parameters

Cho SY, et al (33)


patients with COVID-19



-Demographic characteristics

-Epidemiological characteristics

-hemogram parameters

-maximal severity

-clinical outcome obtained

Logistic regression

Validation cohort


28-day survival rates:

-low-risk: 99.8%



risk: 95.4%

-high-risk: 82.3%

-high-risk: 55.1%


-COPS: assist in making risk-adapted decisions for the allocation of medical resources

-Cox proportional hazard regression model

Chow DS, et al (34)


patients with COVID-19


-ICU admission



-Patient comorbidities

-presenting vital signs

-laboratory values

Multivariable logistic regression

External validation

Critical disease= 65%




Chung H, et al (35)


patients with COVID-19


The clinical severity of COVID-19

-Basic patient information

-a physical index

-initial examination findings

-clinical findings

-comorbid diseases

-general blood test results

Artificial intelligence


Sensitivity=  90.2%

-Specificity= 90.4%

-Accuracy= 90.4%

-Balanced accuracy= 90.3%


For predicting COVID-19 severity:


-random forest


-the AI model

Das AK, et al (36)


patients with COVID-19



Demographic, exposure and diagnosis confirmation features along with the outcome

Logistic regression



CoVID-19 mortality risk prediction of 94.1% for a male patient aged between 80 and 89 years


-Support vector machine

-random forest

-gradient boosting


Dixit A, et al (37)

Covid-19 suspected cases


To diagnose the COVID-19 suspected individual


Artificial intelligence





Utilize chest X-rays, K-means clustering and feature extraction.



Olmedo JL,

et al (38)


patients with





Logistic regression


0.94 for accuracy

0.77 for the F-score, 0.93 sensitivity, and 0.91 for specificity


The gradient boosting method to develop a predictive model

Ebell MH, et al (39)


patients with COVID-19



Demographic, clinical, and laboratory parameters

Logistic regression

Internal validation


-The COVID-No Lab risk score: AUROCC = 0.803

-The COVID-Simple Lab score: AUROCC = 0.833


-COVID-No Lab risk

-COVID-Simple Lab

Fink DL, et al (40)

581 individuals were admitted with suspected COVID-19



Clinical observations and blood test results

Logistic regression

Internal validation




-Specificity= 86.8%

-COVID-19 prevalence= 10%

-NPV= 96.5%.


Risk score is the first developed for COVID-19 diagnosis using the TRIPOD checklist. It may be effective as a tool to rule out COVID-19 and function at different pandemic phases of variable COVID-19 prevalence

Gao Y, et al (41)


patients with COVID-19




-Logistic regression

-Support vector machine

-Gradient boosted decision tree

-Neural network

Internal validation





Mancilla-Galindo J, et al (42)


Inpatients and outpatients


Demographic and patient history

-Multivariable cox regression model


–Meier analysis

-Cox proportional hazards regression analysis

-Validation cohort




Multivariable cox regression model

Mamidi, TKK, et al (43)

7,262 COVID19 patients


To predict an individual’s risk for COVID-19 infection

-Respiratory symptoms

-chronic conditions: nicotine dependence and major depressive disorder


-logistic regression method

Stratified cross-validation (CV)


Elastic-Net models:

Accuracy= 0.76

AUC=0.79 [CI: 0.76–0.83] for the all-time data


Credit scorecard modeling approach

Makridis CA, et al (44)

11097 patients



Demographic and patient history, laboratory dysfunction and vital sign measures


cross-validation (CV)AUROC, F1 and mean scores of recalls


F1= 0.4

recall score= 0.76

AUROC= 0.87

AUPRC= 0.41

Final model: XGBoost model

Ma XD, et al (45)

305 patients


Mortality risk

LDH, CRP, and age

Multivariate logistic regression models


four-fold cross-validation



AUROC= 0.9521

Final model: multivariate logistic regression model

Training method: Machine-learning (Random Forest and XGboost methods)

Sample size of training: 75%

Ma B, et al (46)



An early warning system for severe symptoms

Clinical characteristics, Multiple lobe infiltrate in CT, sepsis, WBC count, smoking history, HTN, and age

Chi-square or fisher exact test



Sensitivity= 0.651

Specificity= 0.954

Accuracy= 0.93

AUROC = 0.927


Final model:   ROC curve analysis

Liu J, et al (47)

COVID-19 cases aged>60 years


Early identification of critically ill elderly COVID-19 patients

Demographic and patient history, physical examination

Multivariable logistic regression model

Internal validation cohort

external cohort


Hosmer-lemeshow goodness of fit test (p=0.393)

0.77 (95% CI: 0.71-0.83)

Final model:

Nomogram model

Training method:  discrimina-


AUC and



size of



Li S, et al (48)

2924 patients



Demographic, clinical, laboratory, radiological characteristics, and treatment and outcomes data

CART regression tree

Fivefold cross-validation


Sensitivity =0.899

specificity =0.889

PPV = 0.432

NPV= exceeded 97%


Final model:   GBDT

sample size of training: 152

Training method: 

gradient boosting decision tree (GBDT), logistic regression (LR) model, and simplified LR

Li L, et al (49)



Deaths caused by COVID-19 in hospitals

Age, disease severity, respiratory symptoms, cardiovascular disease, LDH, bilirubin, blood sugar, and urea

Univariate and multivariate COX proportional hazards regression analysis

Bootstrap resampling

Internal validation

external  validation


Internal resampling (C-index) =0.97

Internal validation =0.96

External validation 0.92


Training method:   nomogram modeling

Leoni, MLG, et al (50)

242 patients


Deaths from COVID-19 in critically ill patients at 4 weeks

Age, obesity, procaltitonin, SOFA score, and PaO2/FiO2


Internal validation using the bootstrap resampling technique





0.822 (95% CI 0.770–0.873)



Lehmann J, et al (51)

451 patients

Residents (general population)

SARS-CoV-2 antibodies

Self-reported symptoms, gustatory/olfactory alterations, and limb pain

Univariate analyses

multivariate binary logistic regression


and multivari-


cox propor-

tional hazards regression models


Sensitivity = 0.612

Specificity = 0.852

0.773 (95% CI 0.727–0.820)

Sample size of training: 451

Lasbleiz A, et al (52)

344 COVID-19 cases with diabetes




Older, with more class III obesity, hypertension, insulin therapy, and lower SpO2

Multivariate logistic regressions

ROC analyses

External validation


Sensitivity =77.7%, specificity =89.2%

AUC = 0.895

Final model:   multivariate logistic regression models

sample size of training: 344

Kodama T, et al (53)

207 patients


Higher demand for oxygen in patients with pneumonia

CURB-65, expanded CURB-65, and A-DROP assessment tools





AUC CURB-65=0.6961

A-DROP= 0.6980

expanded CURB-65 scores= 0.8327

The strongest correlation was found for expanded CURB-65 scores (Spearman’s coefficient=

0.48; p<0.0001) and was the most useful

Ji D, et al  (54)

208 patients


Disease progression

Having comorbid conditions, being older, having a lower lymphocyte count, and having a higher LDH




Concordance indexes = 0.86 (95%CI 0.81 - 0.91)

PPV= 50.7% (38.9% - 62.4%)

NPV= 98.5% (94.7-99.8%)

0.91 (95% CI 0.86 to 0.94)

Final model:   univariate and multivariate COX regression

Kaplan-Meier analysis

Jehi L, et al (55)

4,536 patients

Inpatient and outpatient

The risk of hospitali-zation






laboratory measures

The least absolute shrinkage and selection operator (LASSO) logistic regression algorithm

A 10-fold cross validation method


Scaled Brier score= 25.6%

Sensitivity= 0.769

Specify= 0.726





Final model: median imputation, A sample size of training: 2852, Training method: A development cohort, multivariable logistic model, multivariate imputation by chained equations (MICE)

least absolute shrinkage and selection operator (LASSO) logistic regression algorithm

Ikemura K, et al (56)

4313 patients

Inpatient and outpatient

Patients’ chances of surviving a SARS-CoV-2 infection

Systolic and diastolic blood pressure, age, pulse oximetry level, blood urea nitrogen level, lactate dehydrogenase level, D-dimer level, troponin level, respiratory rate, and Charlson comorbidity score.


 10-fold cross-validation






PPV= 0.513

NPV= 0.968

The maximum F2 score of MODEL-10 = 0.779

The probability threshold = 0.202.



Final model: automated machine learning, A sample size of training: 80%, Training method: automated machine learning, gradient boost machine, extreme gradient boost models, deep learning model, random forest, general linear models

Heo JN, et al (57)

4663 patients


Predicting patients with COVID19 requiring intensive care

Two models:

1.Using only clinical variables

2.Added radiologic and laboratory data

Logistic regression

External validation

hosmer-lemeshow test



Model 1:  0.88 [95% CI 0.85–0.92]

Model 2:  0.90 [95% CI 0.86–0.93],


Mean variable inflation factor = 1.08 (range 1.01–1.24)

Sample size of training: 3238

Final model:  Multivariate logistic regression models

Halasz G, et al (58)



30-day mortality

Age, MCHC, PaO2 /FiO2 ratio, T, stroke history, and sex


External validation

test cohort


Sensitivity = 95% Specificity = 44%

NPV = 97%

PPV = 37%

Brier score = 0.16

AUC= 0.79 (Brier score=0.16)

Final model:   Naïve Bayes classifier

Sample size of training: 70%


halian K, et al (59)



Prediction of 7- and 14-day mortality

Age, severity of hypoxia, mean arterial pressure and renal failure at hospital presentation

Multivariable regression

External validation

receiver operating charac-

teristic curve

hosmer-lemeshow goodness of fit (GOF) test



7 days: 0.85 (GOF


14 days: 0.83 (GOF


Multivariable regression model

Haimovich A.D, et al (60)

1,792 patients with COVID-19


Respiratory failure among emergency department patients in the early stages of hospitaliza-


Elixhauser comorbidity In                dex,qSOFA, and CURB-65





Sensitivity = 0.79

specificity= 0.78

PPV= 0.36

NPV= 0.96

LRþ= 3.55

LR- = 0.27

Brier Score= 0.25

AU-ROC= 0.76 (0.65–0.86)*


Gude-Sampedro F, et al (61)

10454 patients with COVID-19


Disease severity (hospitaliza-

tion, ICU admit and mortality)

Age, sex and comorbidities


Internal validation using the bootstrap procedure

Brier scores of Gal-COVID-19 and Charlson index = 0.150 and 0.157 (for hospitaliza-

tion) = 0.025 and 0.026 (for ICU admission)

= 0.043 and 0.046 (death)


AUC for:


tion= 0.77

admission to ICU= 0.83

death= 0.89

Final model: Logistic regression models Nagelkerke R2 = 0.25

Nagelkerke R2 = 0.17

Nagelkerke R2 = 0.31

Sample size of training: 70%

Wang S, et al (62)

5372 patients


Diagnostic and prognostic

Raw chest CT image, type of disease, demographic, Comorbidity, follow up

Kaplan–Meier analysis and log-rank test

Externally validate


(p=0.013 and p=0.014)

AUC: 0.87 and 0.88


80.39% and 79.35%


city: 76.61% and 81.16%,

Final model:  deep

learning system


Prabhaker M, et al (63)

1349 petient with COVID-19




Demographic data such as age upon admission, gender, and clinical symptoms: fever, dry cough, sore throat, breathlessness or shortness of breath and related comorbidities



lemeshow goodness of fit test


The mortality observed in the validation cohort, high (8–9), medium (5–7) and low (0–4) CSS groups was 54.80%, 28.60% and 6.5%.

AUROC curve of the model:


Final model: scoring system (CSS)


Zhang C, et al (64)

80 patients

with COVID-19


severity of COVID-19 infection

Age, white blood cell count, neutrophil, glomerular filtration rate, and myoglobin

Logistic regression



The risk of sever Covid-19 infection in high-risk group was 20.24 times than in low-risk group.

AUC of scoring system: 0.906

sensitivity of prediction is 70.8%, and the specificity is 89.3%.

Final model: scoring system

Yan L, et al  (65)


patients with COVID-19



Epidemiological, demographic, clinical,

laboratory and mortality outcome information

Decision-trees (random forest and logistic regression)

External test


100% survival prediction accuracy 81% mortality prediction accuracy


Modeling method: mathematical modelling

 Final model: machine learning-based model

Mei J, et al (66)

1364 adult patients with COVID-19



Age, respiratory failure, white cell count, lymphocytes,

platelets, D-dimer

and lactate dehydrogenase

Univariate logistic regression

External validation



AUC statistics based on derivation

cohort: 0.96

The AUC statistics based on the external validation cohort: 0.97 and

0.88 for simple model.

Prediction algorithms


Parchure P, et al (67)

567 patients

with COVID-19



Administrative data (including admission type, source of admission); data from nursing flowsheets; related laboratory results and ECG-derived



RF algorithm

Accuracy of 65.5%


RF classifier yielded a sensitivity of 87.8% and

specificity of 60.6%




Machine learning (ML) was randomly split into training (~70%) and test (~30%) sets

Mussini C, et al (68)

266 patients with COVID-19


Treatment (outcome)

Sex, PaO2/FiO2 ratio, platelets and CRP

Multivariable logistic regression

K-fold cross validation


The accuracy of the score in AUC was 0.80 and 0.70 in internal validation

, test for the composite endpoint

AUC = 0.89


Schöning V, et al (69)

N= 657 patients tested positive for SARS-CoV-2

Inpatient & outpatient


Sex, C-reactive protein, sodium, hemoglobin, glomerular filtration rate, glucose, and leucocytes around the

time of first positive testing

-Logistic regression

-Decision tree induction (DTI)

-Regression trees (CART)  -Random forest

Training and prospective validation cohort


PPV = 0.90

NPV = 0.58

AUROC: (median = 0.96, interquartile range = 0.85–0.99)


and machine learning model

Tanboğa IH, et al (70)

60,980 patients with COVID-19




biomarkers, medications, comorbidities, and clinical outcomes

during index hospitalization

Multivariable logistic regression

Internal–external validation (temporal and geographic




Area under the curve-receiver operating characteristic = 0.942



Rodriguez VA, et al (71)

N=1330   patients with COVID-19

Inpatient & outpatient


Age, total white blood cell count, chest x-ray

appearances and contact history as significant predictors

Multivariable logistic regression


Lemeshow (H–L) test and calibration plot









AUC = 0.880

 [CI = 0.844-0.916]


Ng MY, et al (72)

1330 patients with and without COVID-19

Inpatient & outpatient


Haematological and

biochemical blood tests and CXR


Multivariable logistic regression

Externally validated








The first prediction model: (AUC = 0.911 [CI = 0.880 0.941]).

The second Model:

(AUC = 0.880 [CI = 0.844 0.916])


Mei Q, et al (73)

492 patients with Covid-19



Demographic characteristics, clinical information, vital signs and laboratory reports

Multivariate analysis

Validation cohorts



 AUC:  0.912, 0.928, and 0.883


Wu G, et al (74)

725 patients with COVID-19




laboratory and radiological variables

Logistic regression

Internal &

External validation

74.4% to


PPVs: 66.7% to 84.1%,

NPVs: 73.9% to 95.7%.

AUCs: 0.84 to 0.93

Final model: Machine

-Learning model

Tsui E LH (75)

1037 COVID-19 laboratory-confirmed patients



Epidemiological, clinical

and laboratory

Univariate logistic regression

External validation


and 99.5%


Odds ratios (ORs) with correspond-

ing 95%

(AUC: 0.86, 95% CI: 0.82–0.91)

Final model:  scoring system

Zhou J, et al (76)

4442 patients with COVID-19

Inpatient & outpatient


Age, gender, medical

comorbidities, medication records, and laboratory examination


Logistic regression

External validation & cross-validation

83% to 87%


AUC: 0.86, 95%

CI: 0.82–0.91

Final model:  scoring system

Vila-Corcoles A, et al (77)

282 laboratory-confirmed COVID-19

Inpatient & outpatient


Demographics, pre-existing comorbidities

and early symptomatology

Logistic regression


External cohort validation



Area under ROC curve: 0.828; 95% CI: 0.774-0.882

Final model: prognostic rule

Zhang Y, et al (78)

Patient with COVID-19

Inpatient & outpatient

Outbreak rate

Numbers of con

firmed diagnosis, recoveries and fatalities

Mathematical methods





Final model: stochastic dynamic


Pan P, et al (79)

123 patients with COVID-19 in the ICU



Baseline patient information,

clinical diagnosis, vital signs, laboratory test results, medical

advice, and nursing care

Machine learning, Logistic regression)

5-fold cross validation



Training (AUC=0.86)

verification queue (AUC=0.92)

eXtreme Gradient Boosting (XGBoost) model, 80% of these data as the training set

Peng Y, et al (80)

Patients with COVID-19

Inpatient & outpatient

Incidence rate

Candidate features

associated to COVID-19

Random forest regression algorithm





Modeling method: techniques of features engineering, Final model: machine learning algorithm

* Se: Sensitivity, Sp: Specificity, PPV: Positive Prediction Value, NPV: Negative Prediction Value, ANN: Artificial Neural Network, CHS: Clalit Health Services, SRF: Severe Respiratory Failure, LASSO: Least Absolute Shrinkage and Selection Operator, GOT: Glutamo-Oxaloacetic Transaminase, GPT: Glutamo-Pyruvic Transaminase, γ-GT: Gamma-Glutamyl-Transpeptidase, SaO2: Oxygen Saturation, INR: International Normalized Ratio, eGFR: estimated Glomerular Filtration Rate, CKD-EPI: Chronic Kidney Disease Epidemiology Collaboration, COPS: COVID-19 Prognosis Score, BFGS: Broyden–Fletcher–Goldfarb–Shanno, SMOTE: Synthetic Minority Oversampling Technique, NPV: Negative Predictive Value, TRIPOD: Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis, MRPMC: Mortality Risk Prediction Model for COVID-19, ICU: Intensive Care Unit.


Studies in these articles have shown that in the section of medical setting, most of the subjects were inpatients. In the purpose of prediction section, mortality (n=27) was also the most common item. In addition, severe status, prognosis, diagnostic, ICU transfer, treatment, survival, hospitalization, incidence rate, discharge, outbreak rate, and SARS-CoV-2 antibodies were other cases that were obtained from the articles. Figure 2 demonstrates the frequency of variables from purpose of prediction.

The authors conducted review on the 59 included studies from different countries like USA, China, Italy, Japan, Spain and France. The selected papers deal with different techniques used to help better management of COVID-19. Inpatient and outpatient populations were evaluated to assess the new methods’ efficacy in risk prediction of COVID-19 to assist management in making decisions as soon as possible.
The majority of the studies identified in this review, used novel methodologies to predict mortality in COVID-19. These models also were used in order to detect or predict diagnosis, treatment, prognosis, early warning of severe symptoms like oxygen requirement and patients requiring intensive care (62,64,70).
Most of the studies were cohort and different modeling methods including ANN (Artificial Neural Networks), XGBoost model (eXtreme Gradient Boosting), GBDT (Gradient Boosted Decision Tree), also regression models like Multivariate logistic regression models, Univariate, and Multivariate COX regression were used (23,31,42). These prediction models utilized different types of data including demographic characteristics, laboratory tests, CT scan and imaging data (24,27,31,44).
One advantage of these models is that all these data can be prepared and collected by the physician very fast. They are time and money consuming and are available in most of the health-care centers. In the USA, Bolourani et al utilized XGBoost model on 11,525 patients with COVID-19. They collected patients’ demographics, laboratory data, vital signs and treatment to predict 48-hour respiratory failure (31).
Tsui et al’s study was performed with patients demographic and laboratory data using scoring system to predict the prognosis. 1,037 COVID-19 laboratory-confirmed patients were evaluated and the prediction accuracy rate was 92.3 to 99.5% (75).
In a cohort study by Bartoletti et al, they collected patients’ demographic data, comorbidities, laboratory tests and medical history for prediction of treatment on 1,113 patients with COVID-19. They utilized PREDI-CO score and risk factors for SRF (severe respiratory failure) (At a score of >3 specificity, positive predictive value and negative predictive value were 89.1%) (26).
Three studies also have used hematological and biochemical blood tests results included full blood count, glycemia, renal and liver function tests, creatine kinase, lactate dehydrogenase, C-reactive protein (CRP), procalcitonin, fibrinogen, D-dimer, troponin, ferritin and interleukin-6 (IL-6) as the predictors. But in these studies, other factors such as patients’ information, comorbidities, physical index, clinical observations and initial examination findings and chest X rays (CXR) findings were assessed. Therefore, the prediction rate is not specific to the blood tests (35, 40).
Vital signs of patients were mentioned as a predictor in five studies. These data source of information seemed to have less validation compared to other prediction tools (nearly 60-70% accuracy). COPS (COVID-19 Prognosis Score) and Cox proportional hazard regression model (which are based on demographic data, epidemiological characteristics, hemogram parameters at admission , maximal severity and clinical outcome) can estimate 28-day survival rates at the low-risk, intermediate-risk, high-risk and very high-risk condition (33,34).
During the COVID-19 pandemic, this system could aid in the allocation of medical resources, including intensive care, based on risk. Among demographic characteristics of patients, like gender, age, body mass index, comorbidities, etc., the single most important factor in surviving this viral infection has been reported to be age. Regarding the plausibility of models, and the time issue in mortality prediction, some models like admitted COVID-19 patients, XGBoost has the ability to predict 48-hour respiratory failure which is a valuable achievement (26,31).
Two studies used developed web-based tools to input patient data and to enable clinicians to view likelihood of critical disease and patients’ need for critical care as a useful prognostication model (34,47). Models based on web applications have this advantage that anyone can access them. Moreover, sharing the AI models with the public has benefits in enhancing efficiency of tools and validating them. Almost all modeling methods reported good predictive performance. Most of the methods showed accuracy rates (or sensitivity) near 90% (range: 57-99.34%). Chung et al performed a cohort study on 5,601 patients with COVID-19 in South Korea. The XGBoost model was used to predict the COVID-19 severity via artificial intelligence. The sensitivity, specificity and balanced accuracy were all about 90% (35).
Study of Dixit et al also represented the 99% prediction accuracy rate of their model using artificial intelligence in interpreting chest X rays to diagnose the COVID-19 suspected patients (37). The predictive performance and validation of the studies were measured mostly by cross validation, validation cohort and C-index (50,52).
Although most of the studies were conducted in the USA and China, there were other studies from other countries like Israel, Australia, France, UK and Italy. Thus, different populations and races were included in this study and data were not limited to only one or two countries. This is an advantage of this study. Machine learning is a new technique, in which computers evaluate data. Different types of data were used in this method to evaluate and predict severity of disease, patients’ mortality and treatment decision. Based on these results, machine learning methods found to be useful in predicting the future scenario of disease based on present facts and it can be used by healthcare professionals to make decisions for managing the COVID-19 accordingly. Although the sample size of the studies consists of nearly large populations from different countries, maybe further investigation should be done in order to generalize the results for all populations.

Artificial intelligence and deep learning are effective methods for detecting COVID-19 early and accurately. It may accelerate the diagnosis process and a step forward to automation and shortening of diagnostic evaluation. This innovation can aid in development and progression of clinical skills in diagnosis of the disease. It could help with disease control and prevention especially in emergency circumstances like COVID-19 pandemic. However, this new technique needs to be developed and refined and spread beyond clinicians to become more applicable. 

The present research was extracted from the research project with code IR.ESFARAYENUMS.REC.1400.015 entitled “Predictor technique and models to management of COVID-19” conducted at Esfarayen University of Medical Sciences in 2022. We thank the statistical population and all the participants for taking time to contribute to the study.

  1. References

    1. Legro RS, Arslanian SA, Ehrmann DA, Hoeger KM, Murad MH, Pasquali R, et al. Diagnosis and treatment of polycystic ovary syndrome: an Endocrine Society clinical practice guideline. J Clin Endocrinol Metab 2013 Dec;98(12):4565-92.
    2. Dadras O, Afsahi AM, Pashaei Z, Mojdeganlou H, Karimi A, Habibi P, et al. The relationship between COVID-19 viral load and disease severity: a systematic review. Immun Inflamm Dis 2022 Mar;10(3):e580.
    3. Mehraeen E, Salehi MA, Behnezhad F, Moghaddam HR, SeyedAlinaghi S. Transmission modes of COVID-19: a systematic review. Infect Disord Drug Targets 2021;21(6):e170721187995.
    4. Oliaei S, SeyedAlinaghi S, Mehrtak M, Karimi A, Noori T, Mirzapour P, et al. The effects of hyperbaric oxygen therapy (HBOT) on coronavirus disease-2019 (COVID-19): a systematic review. Eur J Med Res 2021 Aug 19;26(1):96.
    5. SeyedAlinaghi S, Karimi A, MohsseniPour M, Barzegary A, Mirghaderi SP, Fakhfouri A, et al. The clinical outcomes of COVID-19 in HIV-positive patients: a systematic review of current evidence. Immun Inflamm Dis 2021 Dec;9(4):1160-85.
    6. Hatmi ZN. A systematic review of systematic reviews on the COVID-19 pandemic. SN Compr Clin Med 2021;3(2):419-36.
    7. Twohig KA, Nyberg T, Zaidi A, Thelwall S, Sinnathamby MA, Aliabadi S, et al. Hospital admission and emergency care attendance risk for SARS-CoV-2 delta (B.1.617.2) compared with alpha (B.1.1.7) variants of concern: a cohort study. Lancet Infect Dis 2022 Jan;22(1):35-42.
    8. Pak A, Adegboye OA, Adekunle AI, Rahman KM, McBryde ES, Eisen DP. Economic consequences of the COVID-19 outbreak: the need for epidemic preparedness. Front Public Health 2020 May 29;8:241.
    9. Mehraeen E, Mehrtak M, SeyedAlinaghi S, Nazeri Z, Afsahi AM, Behnezhad F, et al. Technology in the era of COVID-19: a systematic review of current evidence. Infect Disord Drug Targets 2022;22(4):e240322202551.
    10. Shamsabadi A, Pashaei Z. Internet of things in the management of chronic diseases during the COVID-19 pandemic: a systematic review. Health Sci Rep 2022 Mar 14;5(2):e557.
    11. Bai X, Fang C, Zhou Y, Bai S, Liu Z, Chen Q, et al. Predicting COVID-19 malignant progression with AI techniques. MedRxiv 2020 Mar 23:2020-03.
    12. Nasajpour M, Pouriyeh S, Parizi RM, Dorodchi M, Valero M, Arabnia HR. Internet of things for current COVID-19 and future pandemics: an exploratory study. J Healthc Inform Res 2020;4(4):325-64.
    13. Noori T, Ghazisaeedi M, Aliabad GM, Mehdipour Y, Mehraeen E, Conte R, et al. International comparison of thalassemia registries: challenges and opportunities. Acta Inform Med 2019 Mar;27(1):58-63.
    14. Gandhi M, Yokoe DS, Havlir DV. Asymptomatic transmission, the Achilles’ heel of current strategies to control Covid-19. N Engl J Med 2020 May 28;382(22):2158-60.
    15. Mehraeen E, Safdari R, Mohammadzadeh N, Seyedalinaghi SA, Forootan S, Mohraz M. Mobile-based applications and functionalities for self-management of people living ith HIV. Stud Health Technol Inform 2018;248:172-9.
    16. Mehraeen E, Safdari R, Seyedalinaghi SA, Mohammadzadeh N, Arji G. Identifying and Validating Requirements of a Mobile-Based Self-Management System for People Living with HIV. Stud Health Technol Inform 2018;248:172-9.
    17. Niakan S, Mehraeen E, Noori T, Gozali E. Web and mobile based HIV prevention and intervention programs pros and cons–a review. Stud Health Technol Inform. 2017 Jan 1;236:319-27.
    18. Karimi A, Shobeiri P, Kulasinghe A, Rezaei N. Novel systemic inflammation markers to predict COVID-19 prognosis. Front Immunol 2021 Oct 22;12:741061.
    19. Mehraeen E, Karimi A, Barzegary A, Vahedi F, Afsahi AM, Dadras O, et al. Predictors of mortality in patients with COVID-19-a systematic review. Eur J Integr Med 2020 Dec;40:101226.
    20. d’Arma A, Rossi V, Pugnetti L, Grosso C, Sinatra M, Dos Santos R, et al. Managing chronic disease in the COVID-19 pandemic: an e-learning application to promote a healthy lifestyle for persons with multiple sclerosis. Psychol Health Med 2022 Feb;27(2):428-35.
    21. Anushiravani A, Vahedi H, Fakheri H, Mansour-Ghanaei F, Maleki I, Nasseri-Moghaddam S, et al. A supporting system for management of patients with inflammatory bowel disease during COVID-19 outbreak: Iranian experience-study protocol. Middle East J Dig Dis 2020 Oct;12(4):238-45.
    22. Ben Hassen H, Ayari N, Hamdi BA home hospitalization system based on the internet of things, fog computing and cloud computing. Inform Med Unlocked 2020;20:100368.
    23. Abdulaal A, Patel A, Charani E, Denny S, Mughal N, Moore L. Prognostic modeling of COVID-19 using artificial intelligence in the United Kingdom: model development and validation. J Med Internet Res 2020 Aug 25;22(8):e20259.
    24. Allenbach Y, Saadoun D, Maalouf G, Vieira M, Hellio A, Boddaert J, et al. Development of a multivariate prediction model of intensive care unit transfer or death: a French prospective cohort study of hospitalized COVID-19 patients. PLoS One 2020 Oct 19;15(10):e0240711.
    25. Barda N, Riesel D, Akriv A, Levy J, Finkel U, Yona G, et al. Developing a COVID-19 mortality risk prediction model when individual-level data are not available. Nat Commun 2020 Sep 7;11(1):4439.
    26. Bartoletti M, Giannella M, Scudeller L, Tedeschi S, Rinaldi M, Bussini L, et al. Development and validation of a prediction model for severe respiratory failure in hospitalized patients with SARS-CoV-2 infection: a multicentre cohort study (PREDI-CO study). Clin Microbiol Infect 2020 Nov;26(11):1545-53.
    27. Bellos I, Lourida P, Argyraki A, Korompoki E, Zirou C, Kokkinaki I, et al. Development of a novel risk score for the prediction of critical illness amongst COVID-19 patients. Int J Clin Pract 2021 Apr;75(4):e13915.
    28. Bennouar S, Cherif AB, Kessira A, Bennouar DE, Abdi S. Development and validation of a laboratory risk score for the early prediction of COVID-19 severity and in-hospital mortality. Intensive Crit Care Nurs 2021 Jun;64:103012.
    29. Berenguer J, Borobia AM, Ryan P, Rodríguez-Banõ J, Bellón JM, Jarrín I, et al. Development and validation of a prediction model for 30-day mortality in hospitalised patients with COVID-19: The COVID-19 SEIMC score. Thorax 2021 Sep;76(9):920-9.
    30. Berry DA, Ip A, Lewis BE, Berry SM, Berry NS, MrKulic M, et al. Development and validation of a prognostic 40-day mortality risk model among hospitalized patients with COVID-19. PLoS One 2021 Jul 30;16(7):e0255228.
    31. Bolourani S, Brenner M, Wang P, McGinn T, Hirsch JS, Barnaby D, et al. A machine learning prediction model of respiratory failure within 48 hours of patient admission for COVID-19: model development and validation. J Med Internet Res 2021 Feb 10;23(2):e24246.
    32. Booth AL, Abels E, McCaffrey P. Development of a prognostic model for mortality in COVID-19 infection using machine learning. Mod Pathol 2021 Mar;34(3):522-31.
    33. Cho SY, Park SS, Song MK, Bae YY, Lee DG, Kim DW. Prognosis score system to predict survival for COVID-19 cases: A Korean nationwide cohort study. J Med Internet Res 2021 Feb 22;23(2):e26257.
    34. Chow DS, Glavis-Bloom J, Soun JE, Weinberg B, Loveless TB, Xie XH, et al. Development and external validation of a prognostic tool for COVID-19 critical disease. PLoS One 2020 Dec 9;15(12):e0242953.
    35. Chung H, Ko H, Kang WS, Kim KW, Lee H, Park C, et al. Prediction and feature importance analysis for severity of COVID-19 in South Korea using artificial intelligence: model development and validation. J Med Internet Res 2021 Feb 22;23(2):e26257.
    36. Das AK, Mishra S, Gopalan SS. Predicting CoVID-19 community mortality risk using machine learning and development of an online prognostic tool. PLoS One 2020 Dec 9;15(12):e0242953.
    37. Dixit A, Mani A, Bansal R. CoV2-detect-net: design of COVID-19 prediction model based on hybrid DE-PSO with SVM using chest X-ray images. Inf Sci (N Y) 2021 Sep;571:676-92.
    38. Domínguez-Olmedo JL, Gragera-Martínez Á, Mata J, Pachón Álvarez V. Machine learning applied to clinical laboratory data in Spain for COVID-19 outcome prediction: model development and validation. J Med Internet Res 2021 Apr 14;23(4):e26211.
    39. Ebell MH, Cai XY, Lennon R, Tarn DM, Mainous AG, Zgierska AE, et al. Development and validation of the COVID-NoLab and COVID-SimpleLab risk scores for prognosis in 6 US health systems. J Am Board Fam Med 2021 Feb;34(Suppl):S127-S135.
    40. Fink DL, Khan PY, Goldman N, Cai J, Hone L, Mooney C, et al. Development and internal validation of a diagnostic prediction model for COVID-19 at time of admission to hospital. QJM 2021 Dec 20;114(10):699-705.
    41. Gao Y, Cai GY, Fang W, Li HY, Wang SY, Chen L, et al. Machine learning based early warning system enables accurate mortality risk prediction for COVID-19. Nat Commun 2020 Oct 6;11(1):5033.
    42. Mancilla-Galindo J, Vera-Zertuche JM, Navarro-Cruz AR, Segura-Badilla O, Reyes-Velázquez G, Tepepa-López FJ, et al. Development and validation of the patient history COVID-19 (PH-Covid19) scoring system: a multivariable prediction model of death in Mexican patients with COVID-19. Epidemiol Infect 2020 Nov 26;148:e286.
    43. Mamidi TKK, Tran-Nguyen TK, Melvin RL, Worthey EA. Development of an individualized risk prediction model for COVID-19 using electronic health record data. Front Big Data 2021 Jun 4;4:675882.
    44. Makridis CA, Strebel T, Marconi V, Alterovitz G. Designing COVID-19 mortality predictions to advance clinical outcomes: evidence from the department of veterans affairs. BMJ Health Care Inform 2021 Jun;28(1):e100312.
    45. Ma XD, Ng M, Xu S, Xu ZM, Qiu H, Liu YW, et al. Development and validation of prognosis model of mortality risk in patients with COVID-19. Epidemiol Infect 2020 Aug 4;148:e168.
    46. Ma B, Gong J, Yang Y, Yao X, Deng X, Chen X. Applicability of MuLBSTA scoring system as diagnostic and prognostic role in early warning of severe COVID-19. Microb Pathog 2021 Jan;150:104706.
    47. Liu J, Tao LY, Gao ZC, Jiang RM, Liu M. Development and validation of a prediction model for early identification of critically ill elderly COVID-19 patients. Aging (Albany NY) 2020 Oct 6;12(19):18822-32.
    48. Li SM, Lin YL, Zhu T, Fan MJ, Xu SC, Qiu WH, et al. Development and external evaluation of predictions models for mortality of COVID-19 patients using machine learning method. Neural Comput Appl 2021 Jan 5;1-10.
    49. Li L, Fang XY, Cheng LX, Wang PH, Li S, Yu H, et al. Development and validation of a prognostic nomogram for predicting in-hospital mortality of COVID-19: a multicenter retrospective cohort study of 4086 cases in China. Aging (Albany NY) 2021 Feb 9;13(3):3176-89.
    50. Leoni MLG, Lombardelli L, Colombi D, Bignami EG, Pergolotti B, Repetti F, et al. Prediction of 28-day mortality in critically ill patients with COVID-19: development and internal validation of a clinical prediction model. PLoS One 2021 Jul 13;16(7):e0254550.
    51. Lehmann J, Giesinger JM, Rumpold G, Borena W, Knabl L, Falkensammer B, et al. Estimating seroprevalence of SARS-CoV-2 antibodies using three self-reported symptoms: development of a prediction model based on data from Ischgl, Austria. Epidemiol Infect 2021 Feb 18;149:e52.
    52. Lasbleiz A, Cariou B, Darmon P, Soghomonian A, Ancel P, Boullu S, et al. Phenotypic characteristics and development of a hospitalization prediction risk score for outpatients with diabetes and COVID-19: the DIABCOVID study. J Clin Med 2020 Nov 20;9(11):3726.
    53. Kodama T, Obinata H, Mori H, Murakami W, Suyama Y, Sasaki H, et al. Prediction of an increase in oxygen requirement of SARS-CoV-2 pneumonia using three different scoring systems. J Infect Chemother 2021 Feb;27(2):336-41.
    54. Ji D, Zhang D, Xu J, Chen Z, Yang T, Zhao P, et al. Prediction for progression risk in patients with COVID-19 pneumonia: the CALL score. Clin Infect Dis 2020 Sep 12;71(6):1393-9.
    55. Jehi L, Ji XG, Milinovich A, Erzurum S, Merlino A, Gordon S, et al. Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19. PloS One 2020 Aug 11;15(8):e0237419.
    56. Ikemura K, Bellin E, Yagi Y, Billett H, Saada M, Simone K, et al. Using automated machine learning to predict the mortality of patients with COVID-19: prediction model development study. J Med Internet Res 2021 Feb 26;23(2):e23458.
    57. Heo JN, Han D, Kim HJ, Kim D, Lee YK, Lim D, et al. Prediction of patients requiring intensive care for COVID-19: development and validation of an integer-based score using data from Centers for Disease Control and Prevention of South Korea. J Intensive Care 2021 Jan 29;9(1):16.
    58. Halasz G, Sperti M, Villani M, Michelucci U, Agostoni P, Biagi A, et al. A machine learning approach for mortality prediction in COVID-19 pneumonia: development and evaluation of the Piacenza score. J Med Internet Res 2021 May 31;23(5):e29058.
    59. Hajifathalian K, Sharaiha RZ, Kumar S, Krisko T, Skaf D, Ang B, et al. Development and external validation of a prediction risk model for short-term mortality among hospitalized U.S. COVID-19 patients: a proposal for the COVID-AID risk tool. PLoS One 2020 Sep 30;15(9):e0239536.
    60. Haimovich AD, Ravindra NG, Stoytchev S, Young HP, Wilson FP, van Dijk D, et al. Development and validation of the quick COVID-19 severity index: a prognostic tool for early clinical decompensation. Ann Emerg Med 2020 Oct;76(4):442-53.
    61. Gude-Sampedro F, Fernandez-Merino C, Ferreiro L, Lado-Baleato O, Espasandin-Dominguez J, Hervada X, et al. Development and validation of a prognostic model based on comorbidities to predict COVID-19 severity: a population-based study. Int J Epidemiol 2021 Mar 3;50(1):64-74.
    62. Wang S, Zha Y, Li W, Wu Q, Li X, Niu M, et al. A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur Respir J 2020 Aug 6;56(2):2000775.
    63. Mishra P, Singh RK, Nath A, Pande S, Agarwal A, Sanjeev OP, et al. A novel epidemiological scoring system for the prediction of mortality in COVID-19 patients. Trans R Soc Trop Med Hyg 2022 May 2;116(5):409-16.
    64. Zhang C, Qin L, Li K, Wang Q, Zhao Y, Xu B, et al. A novel scoring system for prediction of disease severity in COVID-19. Front Cell Infect Microbiol 2020 Jun 5;10:318.
    65. Yan L, Zhang H-T, Goncalves J, Xiao Y, Wang M, Guo Y, et al. An interpretable mortality prediction model for COVID-19 patients. Nat Machine Intellig 2020;2(5):283-8.
    66. Mei J, Hu W, Chen Q, Li C, Chen Z, Fan Y, et al. Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study. BMJ Open 2020 Dec 24;10(12):e044028.
    67. Parchure P, Joshi H, Dharmarajan K, Freeman R, Reich DL, Mazumdar M, et al. Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19. BMJ Support Palliat Care 2020 Sep 22;bmjspcare-2020-002602.
    68. Mussini C, Cozzi-Lepri A, Menozzi M, Meschiari M, Franceschini E, Milic J, et al. Development and validation of a prediction model for tocilizumab failure in hospitalized patients with SARS-CoV-2 infection. PloS One 2021 Feb 23;16(2):e0247275.
    69. Schöning V, Liakoni E, Baumgartner C, Exadaktylos AK, Hautz WE, Atkinson A, et al. Development and validation of a prognostic COVID-19 severity assessment (COSA) score and machine learning models for patient triage at a tertiary hospital. J Transl Med 2021 Feb 5;19(1):56.
    70. Tanboğa IH, Canpolat U, Çetin E, Kundi H, Çelik O, Çağlayan M, et al. Development and validation of clinical prediction model to estimate the probability of death in hospitalized patients with COVID-19: Insights from a nationwide database. J Med Virol 2021 May;93(5):3015-22.
    71. Rodriguez VA, Bhave S, Chen R, Pang C, Hripcsak G, Sengupta S, et al. Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients. J Am Med Inform Assoc 2021 Jul 14;28(7):1480-8.
    72. Ng MY, Wan EYF, Wong HYF, Leung ST, Lee JCY, Chin TW, et al. Development and validation of risk prediction models for COVID-19 positivity in a hospital setting. Int J Infect Dis 2020 Dec;101:74-82.
    73. Mei Q, Wang AY, Bryant A, Yang Y, Li M, Wang F, et al. Development and validation of prognostic model for predicting mortality of COVID-19 patients in Wuhan, China. Sci Rep 2020 Dec 31;10(1):22451.
    74. Wu G, Yang P, Xie Y, Woodruff HC, Rao X, Guiot J, et al. Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study. Eur Respir J 2020 Aug 20;56(2):2001104.
    75. Tsui ELH, Lui CSM, Woo PPS, Cheung ATL, Lam PKW, Tang VTW, et al. Development of a data-driven COVID-19 prognostication tool to inform triage and step-down care for hospitalised patients in Hong Kong: a population-based cohort study. BMC Med Inform Decis Mak 2020 Dec 7;20(1):323.
    76. Zhou J, Lee S, Wang X, Li Y, Wu WKK, Liu T, et al. Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong. NPJ Digit Med 2021 Apr 8;4(1):66.
    77. Vila-Corcoles A, Satue-Gracia E, Vila-Rovira A, de Diego-Cabanes C, Forcadell-Peris MJ, Ochoa-Gondar O. Development of a predictive prognostic rule for early assessment of COVID-19 patients in primary care settings. Atencion Primar 2021;53(9):102118.
    78. Zhang Y, You C, Cai Z, Sun J, Hu W, Zhou XH. Prediction of the COVID-19 outbreak based on a realistic stochastic model. MedRxiv 2020 Mar 13:2020-03.
    79. Pan P, Li Y, Xiao Y, Han B, Su L, Su M, et al. Prognostic assessment of COVID-19 in the intensive care unit by machine learning methods: model development and validation. J Med Internet Res 2020 Nov 11;22(11):e23128.
    80. Peng Y, Li C, Rong Y, Pang CP, Chen X, Chen H. Real-time prediction of the daily incidence of COVID-19 in 215 countries and territories using machine learning: model development and validation. J Med Internet Res 2021 Jun 14;23(6):e24285.