Many health experts are concerned that the COVID-19 pandemic could be having widespread effects on people's mental health, but assessing these concerns is difficult without data.

"Traditional public health surveillance lacks the agility to provide on-demand insights. As a result, when public leaders need real time data to inform their responses to COVID-19's mental health burdens, all that can be mustered is theoretical speculation," said Dr. John W. Ayers who specializes in monitoring the health needs of the public.

In March 2020, federal officials declared the COVID-19 outbreak a national emergency. Around the same time, most states implemented stay-at-home advisories - to different degrees and at different times. Publicly available cell phone location data - anonymized at the county-level - showed marked reductions in cell phone activity at the workplace and at retail locations, as well as increased activity in residential areas. However, it was not known whether these data correlate with the spread of COVID-19 in a given region.

Researchers at UC San Francisco have developed a "digital biomarker" that would use a smartphone's built-in camera to detect Type 2 diabetes - one of the world's top causes of disease and death - potentially providing a low-cost, in-home alternative to blood draws and clinic-based screening tools.

An international task force, including two University of Massachusetts Amherst computer scientists, concludes in new research that mobile health (mHealth) technologies are a viable option to monitor COVID-19 patients at home and predict which ones will need medical intervention.

Scientists at the University of California, Riverside, have used machine learning to identify hundreds of new potential drugs that could help treat COVID-19, the disease caused by the novel coronavirus, or SARS-CoV-2.

"There is an urgent need to identify effective drugs that treat or prevent COVID-19," said Anandasankar Ray, a professor of molecular, cell, and systems biology who led the research.

Engineers at the UCLA Samueli School of Engineering and their colleagues at Stanford School of Medicine have demonstrated that drug levels inside the body can be tracked in real time using a custom smartwatch that analyzes the chemicals found in sweat. This wearable technology could be incorporated into a more personalized approach to medicine - where an ideal drug and dosages can be tailored to an individual.

Biomedical engineers at Duke University have shown that different strains of the same bacterial pathogen can be distinguished by a machine learning analysis of their growth dynamics alone, which can then also accurately predict other traits such as resistance to antibiotics.