COVID-19 pandemic has caused unprecedented negative impacts on our society, including further exposing inequity and disparity in public health. To study the impact of socioeconomic factors on COVID transmission, we first propose a spatial-temporal model to examine the socioeconomic heterogeneity and spatial correlation of COVID-19 transmission at the community level. Second, to assess the individual risk of severe COVID-19 outcomes after a positive diagnosis, we propose a dynamic, varying-coefficient model that integrates individual-level risk factors from electronic health records (EHRs) with community-level risk factors. The underlying neighborhood prevalence of infections (both symptomatic and pre-symptomatic) predicted from the previous spatial-temporal model is included in the individual risk assessment so as to better capture the background risk of virus exposure for each individual. We design a weighting scheme to mitigate multiple selection biases inherited in EHRs of COVID patients. We analyze COVID transmission data in New York City (NYC, the epicenter of the first surge in the United States) and EHRs from NYC hospitals, where time-varying effects of community risk factors and significant interactions between individual-and community-level risk factors are detected. By examining the socioeconomic disparity of infection risks and interaction among the risk factors, our methods can assist public health decision-making and facilitate better clinical management of COVID patients.

COVID-19 pandemic has caused unprecedented negative impacts on our society, including further exposing inequity and disparity in public health. To study the impact of socioeconomic factors on COVID transmission, we first propose a spatial-temporal model to examine the socioeconomic heterogeneity and spatial correlation of COVID-19 transmission at the community level. Second, to assess the individual risk of severe COVID-19 outcomes after a positive diagnosis, we propose a dynamic, varying-coefficient model that integrates individual-level risk factors from electronic health records (EHRs) with community-level risk factors. The underlying neighborhood prevalence of infections (both symptomatic and pre-symptomatic) predicted from the previous spatial-temporal model is included in the individual risk assessment so as to better capture the background risk of virus exposure for each individual. We design a weighting scheme to mitigate multiple selection biases inherited in EHRs of COVID patients.

The feature determines the vertical position of the point, and the Shapley value determines the horizontal position. The color of the point represents whether the value of the feature is low or high. Our experiment uses red and blue to represent low or high feature values, respectively. For example, for a feature Age, an older man would be drawn as red or a redder point, whereas a younger would be described as blue or a bluer point. Overlapping points are jittered in the y-axis position. The SHAP summary plot indicates a possible relationship between feature value and the impact on model prediction. However, it does not prove any causal relationship.

Clinical scientists have explored de-identified electronic health record data in the National COVID Cohort Collaborative(N3C), a National Institutes of Health-funded national clinical database, using machine learning models to help decipher characteristics of individuals with long COVID and attributes that may help identify such patients using information from medical records. The discoveries published in The Lancet Digital Health have the potential to enhance clinical research on extended COVID and inspire a more consistent COVID treatment regimen. The author Emily R. Pfaff, Ph.D., an assistant professor in the UNC School of Medicine's Division of Endocrinology and Metabolism, said that characterizing, diagnosing, treating, and caring for long COVID patients has turned out to be difficult owing to the list of characteristic symptoms constantly evolving over time. They needed to better grasp the intricacies of long COVID, and it made sense to use current data analysis methods and a unique, extensive data resource like N3C, which represents many of the properties of long COVID. The N3C data enclave, funded by the National Institutes of Health's National Center for Advancing Translational Sciences (NCATS), already has information on more than 13 million people from 72 locations, including approximately 5 million COVID-19-positive patients.

A tool has been developed to help healthcare professionals identify hospitalised patients most at risk of dying from COVID-19 using artificial intelligence (AI). The algorithm could help doctors to direct critical care resources to those in most immediate need, which the developers of the AI tool say could be especially valuable to resource-limited countries. And with no end in sight for the coronavirus pandemic, with new variants leading to fresh waves of sickness and hospitalisation, the scientists behind the tool say there is a need for generalised tools like this which can be easily rolled out. To develop the tool, scientists used biochemical data from routine blood samples taken from nearly 30,000 patients hospitalised in over 150 hospitals in Spain, the US, Honduras, Bolivia and Argentina between March 2020 and February 2022. Taking blood from so many patients meant the team were able to capture data from people with different immune statuses – vaccinated, unvaccinated and those with natural immunity – and from people infected with every variant of COVID-19.

Long Covid, with its constellation of symptoms, is proving a challenging moving target for researchers trying to conduct large studies of the syndrome. As they take aim, they're debating how to responsibly use growing piles of real-world data -- drawing from the full experiences of long Covid patients, not just their participation in stewarded clinical trials. "People have to really think carefully about what does this mean," said Zack Strasser, an internist at Massachusetts General Hospital who has used existing patient records to study the characteristics of long Covid. Is this not some artifact that's just happening because of the people that we're looking at within the electronic health record? One of the largest sources of real-world data on long Covid is a first-of-its-kind centralized federal database of electronic health records called the National Covid Cohort Collaborative, or N3C.

Testing shortages, long waits for results, and an overtaxed health care system have made headlines throughout the COVID-19 pandemic. These issues can be further exacerbated in small or rural communities in the US and globally. Additionally, respiratory symptoms of COVID-19 such as fever and cough are also associated with the flu, which complicates non-lab diagnoses during certain seasons. A new study by George Mason University College of Health and Human Services researchers is designed to help identify which symptoms are more likely to indicate COVID during flu season. This is the first study to take seasonality into account.

Researchers may have developed a new tool that uses machine learning to better predict health outcomes for hospitalized Covid patients, and help physicians make more informed treatment decisions. A German research team from Charity-University Medicine in Berlin – one of the country's largest university hospitals – developed an Artificial Intelligence tool that can estimate how well an infected person will fare based off of a blood sample. The levels of fourteen proteins found in a person's blood can indicate whether a person who suffers a severe enough hospitalization will survive or die from the virus, and the tool developed by researchers can accurately asses their risk. In times of crisis, where resources are especially scarce, the tool can help determine what patients require the most intensive care to survive, and who is more fit to fight off the virus themselves. Using blood samples from Covid patients, a German research team has found that levels of 14 proteins can help determine whether a person survives the virus.

Addenbrooke's Hospital in Cambridge and 20 other hospitals from across the world and healthcare technology leader NVIDIA have used artificial intelligence (AI) to predict COVID patients' oxygen needs on a global scale. The research was sparked by the pandemic and set out to build an AI tool to predict how much extra oxygen a COVID-19 patient might need in the first days of hospital care, using data from across four continents. The technique, known as federated learning, used an algorithm to analyze chest X-rays and electronic health data from hospital patients with COVID symptoms. To maintain strict patient confidentiality, the patient data was fully anonymized and an algorithm was sent to each hospital so no data was shared or left its location. Once the algorithm had "learned" from the data, the analysis was brought together to build an AI tool which could predict the oxygen needs of hospital COVID patients anywhere in the world.

A recent partnership between BrainChip Holdings and NaNose Medical has yielded a new COVID-19 testing solution boasting higher accuracy than RT-PCR screening. The Nano Artificial Nose is said to actively analyze patient breath samples while remaining highly portable. How exactly does it work? Officially dubbed the DiaNose, NaNose's technology has actually existed since 2017. Based on the Technion Israeli Institute of Technology's artificial nose, the device has since screened numerous patients for Parkinson's disease, cancer, kidney failure, and MS.