JMIRx Med

Editor-in-Chief:

Edward Meinert, MA (Oxon), MSc, MBA, MPA, PhD, CEng, FBCS, EUR ING


JMIRx Med (ISSN 2563-6316) is an overlay journal to JMIR Preprints and MedRxiv, (other preprint servers are being onboarded). JMIRx peer-reviews preprints and publishes their revised "version of record" with peer-review reports across a broad range of medical, clinical and related health sciences. Unlike the majority of JMIR journals, papers published in this journal do not require a digital health focus - in fact, most papers we published in 2020 were related to COVID19, but we publish all research that qualifies for preprinting on MedRxiv. 

Conceived to address the urgent need to make highly relevant scientific information available as early as possible without losing the quality of the peer-reviewed process, this innovative new journal is the first in a new series of “superjournals”. Superjournals (a type of "overlay" journal) sit on top of preprint servers (JMIRx-Med serves MedRxiv and JMIR Preprints), offering peer-review and everything else a traditional scholarly journal does. Our goal is to rapidly peer review and publish a paper. All JMIRx Med papers must have originated as a preprint. 

All JMIRx Med papers are rigorously peer-reviewed, copyedited and XML-tagged. Accepted papers are published along with the related Peer Review Reports and Author Responses to Peer Review Reports, providing an additional layer of transparency to the scholarly publishing process. 

There is currently no Article Processing Fee for this journal. We are selling institutional memberships to funders, libraries and library consortia under the name Plan P (Subscribe to Open Science).

To submit a preprint to JMIRx, either submit a Preprint directly to JMIRx (and opt-in when you are asked to expose the submission on JMIR Preprints) or upload a Preprint to MedRxiv and then user the M2J (or here called M2JMIR) functionality at MedRxiv to submit the paper to the Journal of Medical Internet Research - you will then receive an email with a link to the JMIR submission form, where you must change the target journal to JMIRx.

For more information please consult our Knowledge Base article "What is JMIRx?".  

      Recent Articles

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      Authors' Responses to Peer-Reviews
      Authors’ Response to Peer Reviews of “Evaluating Population Density as a Parameter for Optimizing COVID-19 Testing: Statistical Analysis”

      This is the author response to peer review.

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      Peer Reviews
      Peer Review of “Evaluating Population Density as a Parameter for Optimizing COVID-19 Testing: Statistical Analysis”

      This is a peer review report.

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      Peer Reviews
      Peer Review of “Evaluating Population Density as a Parameter for Optimizing COVID-19 Testing: Statistical Analysis”

      This is a peer review report for MS#22195.

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      Article Thumbnail
      Original Papers
      Evaluating Population Density as a Parameter for Optimizing COVID-19 Testing: Statistical Analysis

      SARS-CoV-2 transmission risk generally increases with the proximity of those shedding the virus to those susceptible to infection. Thus, this risk is a function of both the number of people and the area they occupy. However, the latter continues to evade the COVID-19 testing policy.

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      Authors' Responses to Peer-Reviews
      Author’s Response to Peer Review of “Predicting Health Disparities in Regions at Risk of Severe Illness to Inform Health Care Resource Allocation During Pandemics: Observational Study”

      This is the author response to peer reviews of MS#22470

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      Peer Reviews
      Peer Review of “Predicting Health Disparities in Regions at Risk of Severe Illness to Inform Health Care Resource Allocation During Pandemics: Observational Study”

      This is a peer review report.

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      Article Thumbnail
      Original Papers
      Predicting Health Disparities in Regions at Risk of Severe Illness to Inform Health Care Resource Allocation During Pandemics: Observational Study

      Pandemics including COVID-19 have disproportionately affected socioeconomically vulnerable populations.

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      Peer Reviews
      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”

      This is a peer review report.

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      Article Thumbnail
      Peer Reviews
      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”

      This is a peer review report.

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      Article Thumbnail
      Original Papers
      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

      Approximately 80% of those infected with COVID-19 are immune. They are asymptomatic unknown carriers who can still infect those with whom they come into contact. Understanding what makes them immune could inform public health policies as to who needs to be protected and why, and possibly lead to a novel treatment for those who cannot, or will not, be vaccinated once a vaccine is available.

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      Authors' Responses to Peer-Reviews
      Author Response to Peer Reviews 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”

      This is the Author's Response to Peer Review Reports

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      Authors' Responses to Peer-Reviews
      Authors’ Response to Peer Reviews of “No Time to Waste: Real-World Repurposing of Generic Drugs as a Multifaceted Strategy Against COVID-19”

      Author response to peer review for MS#19583

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      • Committee on Publication Ethics
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