A research team from Johns Hopkins Medicine and Johns Hopkins University has developed a machine-learning (ML) tool capable of predicting who has the highest probability of being naturally resistant to COVID-19 infection despite being exposed to SARS-CoV-2, the virus that causes it.
The study, published this week in PLOS One, aims to better understand the factors that influence COVID-19 resistance.
“If we can identify which people are naturally able to avoid infection by SARS-CoV-2, we may be able to learn — in addition to societal and behavioral factors — which genetic and environmental differences influence their defense against the virus,” said Karen (Kai-Wen) Yang, lead study author and a biomedical engineering graduate student in the Translational Informatics Research and Innovation Lab at Johns Hopkins University, in the press release. “That insight could lead to new preventive measures and more highly targeted treatments.”
To develop their model, the researchers gathered data from the Johns Hopkins COVID-19 Precision Medicine Analytics Platform Registry (JH-CROWN), which contains information for patients with a suspected or confirmed SARS-CoV-2 infection seen within the Johns Hopkins Health System, the press release states.
From this information, the research team selected patients who had received a COVID-19 test between June 10, 2020, and Dec. 15, 2020, and reported “potential exposure to the virus” as the reason for testing. Dec. 15 was chosen as the end date because it was just before large-scale COVID-19 vaccination efforts began in the US, which allowed researchers to avoid the confounding effects of vaccines, rather than natural resistance, on preventing COVID-19 infection.
The final cohort comprised 8,536 study participants who were divided into two groups: those who either did not share a household with any COVID-19 patients or whose household had 10 or more patients, and those who shared a residence with 10 or fewer people, with at least one being a COVID-19 patient.
The first group, consisting of 8,476 participants, served as the training and initial testing test, while the remaining 60 participants were grouped into a Household Index (HHI) Set, which served as a separate testing set.
EHR data from the cohort was analyzed using the Maximal-frequent All-confident pattern Selection Pattern-based Clustering (MASPC) algorithm, which combines patient demographic information, the relevant International Statistical Classification of Diseases and Related Health Problems (ICD) medical diagnostic codes, outpatient medication orders, and the number of comorbidities present for each patient.
“We hypothesized that MASPC would enable us to cluster patients with similar patterns in their data to define them as resistant and non-resistant to SARS-CoV-2, and with the hope that the algorithm would learn with each analysis how to improve the accuracy and reliability of future assignments,” explained co-senior study author Stuart Ray, MD, vice chair of medicine for data integrity and analytics, and professor of medicine at the Johns Hopkins University School of Medicine, in the press release. “This initial study using JH-CROWN data was conducted to give life to that hypothesis, a proof-of-concept trial of our statistical model to show that resistance to COVID-19 might be predictable based [on] a patient’s clinical and demographic profile.”
The researchers were able to identify 56 of these patterns, five of which captured who was most likely exposed to the virus.
“Looking for these patterns in HHI Set — the individuals most likely to have been exposed to SARS-CoV-2 in close quarters — and then statistically analyzing the results, our model’s best performance was 0.61,” says Ray. “Since a score of 0.5 shows only chance association between the prediction and reality, and 1 is 100% association, this shows the model has promise as a tool for identifying people with COVID-19 resistance who can be further studied.”
The researchers noted that the study has multiple limitations, such as potential bias from the self-reporting of COVID-19 exposure by participants, the small number of participants in the HHI group, the short timeframe of the study, and the possibility that participants may have taken tests for SARS-CoV-2 using home kits or at facilities outside the Johns Hopkins system, which would not have been recorded in the JH-CROWN database.

News
Studies detail high rates of long COVID among healthcare, dental workers
Researchers have estimated approximately 8% of Americas have ever experienced long COVID, or lasting symptoms, following an acute COVID-19 infection. Now two recent international studies suggest that the percentage is much higher among healthcare workers [...]
Melting Arctic Ice May Unleash Ancient Deadly Diseases, Scientists Warn
Melting Arctic ice increases human and animal interactions, raising the risk of infectious disease spread. Researchers urge early intervention and surveillance. Climate change is opening new pathways for the spread of infectious diseases such [...]
Scientists May Have Found a Secret Weapon To Stop Pancreatic Cancer Before It Starts
Researchers at Cold Spring Harbor Laboratory have found that blocking the FGFR2 and EGFR genes can stop early-stage pancreatic cancer from progressing, offering a promising path toward prevention. Pancreatic cancer is expected to become [...]
Breakthrough Drug Restores Vision: Researchers Successfully Reverse Retinal Damage
Blocking the PROX1 protein allowed KAIST researchers to regenerate damaged retinas and restore vision in mice. Vision is one of the most important human senses, yet more than 300 million people around the world are at [...]
Differentiating cancerous and healthy cells through motion analysis
Researchers from Tokyo Metropolitan University have found that the motion of unlabeled cells can be used to tell whether they are cancerous or healthy. They observed malignant fibrosarcoma cells and [...]
This Tiny Cellular Gate Could Be the Key to Curing Cancer – And Regrowing Hair
After more than five decades of mystery, scientists have finally unveiled the detailed structure and function of a long-theorized molecular machine in our mitochondria — the mitochondrial pyruvate carrier. This microscopic gatekeeper controls how [...]
Unlocking Vision’s Secrets: Researchers Reveal 3D Structure of Key Eye Protein
Researchers have uncovered the 3D structure of RBP3, a key protein in vision, revealing how it transports retinoids and fatty acids and how its dysfunction may lead to retinal diseases. Proteins play a critical [...]
5 Key Facts About Nanoplastics and How They Affect the Human Body
Nanoplastics are typically defined as plastic particles smaller than 1000 nanometers. These particles are increasingly being detected in human tissues: they can bypass biological barriers, accumulate in organs, and may influence health in ways [...]
Measles Is Back: Doctors Warn of Dangerous Surge Across the U.S.
Parents are encouraged to contact their pediatrician if their child has been exposed to measles or is showing symptoms. Pediatric infectious disease experts are emphasizing the critical importance of measles vaccination, as the highly [...]
AI at the Speed of Light: How Silicon Photonics Are Reinventing Hardware
A cutting-edge AI acceleration platform powered by light rather than electricity could revolutionize how AI is trained and deployed. Using photonic integrated circuits made from advanced III-V semiconductors, researchers have developed a system that vastly [...]
A Grain of Brain, 523 Million Synapses, Most Complicated Neuroscience Experiment Ever Attempted
A team of over 150 scientists has achieved what once seemed impossible: a complete wiring and activity map of a tiny section of a mammalian brain. This feat, part of the MICrONS Project, rivals [...]
The Secret “Radar” Bacteria Use To Outsmart Their Enemies
A chemical radar allows bacteria to sense and eliminate predators. Investigating how microorganisms communicate deepens our understanding of the complex ecological interactions that shape our environment is an area of key focus for the [...]
Psychologists explore ethical issues associated with human-AI relationships
It's becoming increasingly commonplace for people to develop intimate, long-term relationships with artificial intelligence (AI) technologies. At their extreme, people have "married" their AI companions in non-legally binding ceremonies, and at least two people [...]
When You Lose Weight, Where Does It Actually Go?
Most health professionals lack a clear understanding of how body fat is lost, often subscribing to misconceptions like fat converting to energy or muscle. The truth is, fat is actually broken down into carbon [...]
How Everyday Plastics Quietly Turn Into DNA-Damaging Nanoparticles
The same unique structure that makes plastic so versatile also makes it susceptible to breaking down into harmful micro- and nanoscale particles. The world is saturated with trillions of microscopic and nanoscopic plastic particles, some smaller [...]
AI Outperforms Physicians in Real-World Urgent Care Decisions, Study Finds
The study, conducted at the virtual urgent care clinic Cedars-Sinai Connect in LA, compared recommendations given in about 500 visits of adult patients with relatively common symptoms – respiratory, urinary, eye, vaginal and dental. [...]