JMed JMIRx Med JMIRx Med 2563-6316 JMIR Publications Toronto, Canada v1i1e24765 10.2196/24765 Peer-Review Report Peer-Review Report Peer Review of “A Machine Learning Explanation of the Pathogen-Immune Relationship of SARS-CoV-2 (COVID-19), and a Model to Predict Immunity and Therapeutic Opportunity: A Comparative Effectiveness Research Study” Eysenbach Gunther Abbott Eric BASc EE, PEng, PE, MBA, MSEE 1
Medical Health Informatics Graduate Program Northwestern University 339 East Chicago Avenue Chicago, IL United States 1 3125036950 abbottericb@gmail.com
https://orcid.org/0000-0003-0705-6712 Medical Health Informatics Graduate Program Northwestern University Chicago, IL United States Jan-Dec 2020 19 10 2020 1 1 e24765 4 10 2020 4 10 2020 ©Eric Abbott. Originally published in JMIRx Med (https://med.jmirx.org), 19.10.2020. 2020

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the JMIRx Med, is properly cited. The complete bibliographic information, a link to the original publication on https://med.jmirx.org/, as well as this copyright and license information must be included.

 https://preprints.jmir.org/preprint/23582 https://med.jmirx.org/2020/1/e24739/ https://med.jmirx.org/2020/1/e23582/ infectious disease SARS-CoV-2 COVID-19 public health immunity: vaccinations therapeutics stem-cell growth factor-beta

This is a peer review submitted for the paper “A Machine Learning Explanation of the Pathogen-Immune Relationship of SARS-CoV-2 (COVID-19), and a Model to Predict Immunity and Therapeutic Opportunity: A Comparative Effectiveness Research Study.”

General Comments

This paper is excellent and very timely given its importance as it relates to supporting forthcoming mass vaccinations to address COVID-19 and potential prioritization of such vaccinations based on the study findings.

 Specific Comments Major Comments

1. None

2. None

3. None

 Minor Comments

4. Consider consistency of using COVID-19 vs SARS-CoV-2 (abstract vs text body).

None declared.