New York University researchers are in the field capturing highly detailed three-dimensional data on human movements and behaviors—particularly around medical facilities, public transportation systems, and essential services—to document the complex landscape of “surface vectors” and thus opportunities for COVID-19 transmission.

Working under a National Science Foundation Rapid Response Research (RAPID) grant for proposals with severe urgency, the team from the NYU Tandon School of Engineering and the NYU School of Global Health is advancing epidemiological analysis beyond the two-dimensional concept that has been in use since 1854, when John Snow first mapped cholera cases to identify specific contaminated wells as the infection sources of a severe local outbreak in London.

Rapid, repeated documentation and mapping of current conditions around medical and transport facilities will make it possible to investigate the implementation of social distancing regulations and predict patterns of exposure and transmission moving forward, explained the professors. The lead investigator for the project is Debra Laefer, a professor of civil and urban engineering at NYU Tandon who also serves as a professor of urban informatics and director of citizen science at its Center for Urban Science and Progress (CUSP), and the co-leader for the project is Thomas Kirchner, director of the NYU mobile health lab and an assistant professor of social and behavioral sciences at the School of Global Public Health.

This first-of-a-kind study will also lay the groundwork to build machine learning models to speed the analysis of how a virus spreads in urban areas—not just in New York, but across the United States and beyond. For instance, the project is pioneering a new way of thinking and documenting transmission locations. This type of documentation and modeling could easily be applied to airports, grocery stores, and playgrounds—anywhere large groups of people come,

A type of artificial intelligence called machine learning can help predict which patients will develop diabetes, according to an ENDO 2020 abstract that will be published in a special supplemental section of the Journal of the Endocrine Society.

Diabetes is linked to increased risks of severe health problems, including heart disease and cancer. Preventing diabetes is essential to reduce the risk of illness and death. “Currently we do not have sufficient methods for predicting which generally healthy individuals will develop diabetes,” said lead author Akihiro Nomura, M.D., Ph.D., of the Kanazawa University Graduate School of Medical Sciences in Kanazawa, Japan.

The researchers investigated the use of a type of artificial intelligence called machine learning in diagnosing diabetes. Artificial intelligence (AI) is the development of computer systems able to perform tasks that normally require human intelligence. Machine learning is a type of AI that enables computers to learn without being explicitly programmed. With each exposure to new data, a machine-learning algorithm grows increasingly better at recognizing patterns over time.

“Using machine learning, it could be possible to precisely identify high-risk groups of future diabetes patients better than using existing risk scores,” Nomura said. “In addition, the rate of visits to healthcare providers might be improved to prevent future onset of diabetes.”

Nomura and colleagues analyzed 509,153 nationwide annual health checkup records from 139,225 participants from 2008 to 2018 in the city of Kanazawa. Among them, 65,505 participants without diabetes were included.

The data included physical exams, blood and urine tests and participant questionnaires. Patients without diabetes at the beginning of the study who underwent more than two annual health checkups during this period were included. New cases of diabetes were recorded during patients’ checkups.

The researchers identified a total of 4,696 new diabetes patients (7.2%) in