A Calculator for COVID-19 Severity Prediction Based on Patient Risk Factors and Number of Vaccines Received

Ariel Israel*, Alejandro A. Schäffer, Eugene Merzon, Ilan Green, Eli Magen, Avivit Golan-Cohen, Shlomo Vinker, Eytan Ruppin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Vaccines have allowed for a significant decrease in COVID-19 risk, and new antiviral medications can prevent disease progression if given early in the course of the disease. The rapid and accurate estimation of the risk of severe disease in new patients is needed to prioritize the treatment of high-risk patients and maximize lives saved. We used electronic health records from 101,039 individuals infected with SARS-CoV-2, since the beginning of the pandemic and until 30 November 2021, in a national healthcare organization in Israel to build logistic models estimating the probability of subsequent hospitalization and death of newly infected patients based on a few major risk factors (age, sex, body mass index, hemoglobin A1C, kidney function, and the presence of hypertension, pulmonary disease, and malignancy) and the number of BNT162b2 mRNA vaccine doses received. The model’s performance was assessed by 10-fold cross-validation: the area under the curve was 0.889 for predicting hospitalization and 0.967 for predicting mortality. A total of 50%, 80%, and 90% of death events could be predicted with respective specificities of 98.6%, 95.2%, and 91.2%. These models enable the rapid identification of individuals at high risk for hospitalization and death when infected, and they can be used to prioritize patients to receive scarce medications or booster vaccination. The calculator is available online.

Original languageEnglish
Article number1238
Issue number6
StatePublished - Jun 2022


FundersFunder number
U.S. Government
U.S. Department of Health and Human Services
National Cancer Institute
National Cancer Institute
National Institutes of Health


    • COVID-19
    • calculator
    • diabetes
    • disease severity
    • kidney disease
    • obesity


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