Published on in Vol 1, No 1 (2020): Jan-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24765, first published .
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”

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”

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”

Peer-Review Report

Medical Health Informatics Graduate Program, Northwestern University, Chicago, IL, United States

Corresponding Author:

Eric Abbott, BASc EE, PEng, PE, MBA, MSEE

Medical Health Informatics Graduate Program

Northwestern University

339 East Chicago Avenue

Chicago, IL

United States

Phone: 1 3125036950

Email: abbottericb@gmail.com



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.”


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.


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).

Conflicts of Interest

None declared.

Edited by G Eysenbach; This is a non–peer-reviewed article. submitted 04.10.20; accepted 04.10.20; published 19.10.20

Copyright

©Eric Abbott. Originally published in JMIRx Med (https://med.jmirx.org), 19.10.2020.

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.