The aim of the AI algorithm is to separate healthy patients from ill but asymptomatic ones based on coughing tone, feeling, and other biomarkers. In a continuation of the involvement of artificial intelligence (AI) in healthcare, a new app may help diagnose asymptomatic COVID-19 carriers through a simple listening test -- a cough from someone infected versus a regular cough. It may seem straight from science fiction, but a research team at MIT believes that asymptomatic carriers may be showing subtle signs through the sound of their cough. They've built an app, programming it with thousands of data points from healthy and sick volunteers. AI was able to identify coughs coming from those infected with the virus with a 98.5% accuracy rate.

At least one out of every five people who get the coronavirus doesn't show symptoms and can unknowingly spread the virus to others. Those who don't feel sick and aren't notified of exposure can't know that they should get tested. But researchers at the Massachusetts Institute of Technology may have found a way to identify these silent coronavirus carriers without a test. A study published in September describes an artificial-intelligence model that can distinguish between the coughs of people with the coronavirus and those who are healthy. It can even tell from voluntary, forced coughs whether people were healthy or were asymptomatic carriers, based on sound variations too subtle for the human ear to discern.

As millions of people worldwide battle the symptoms of COVID-19, a group of "silent patients" may not even know they're sick and spreading the virus. Asymptomatic people, by definition, have no physical symptoms of the illnesses they carry. Researchers at the Massachusetts Institute of Technology (MIT) however, say they may be showing symptoms after all -- in the sound of their cough. Their study has created an artificial intelligence program that can identify if someone has coronavirus by the way their coughing sounds. Researchers programmed their AI model with thousands of different recorded coughs from both healthy and sick volunteers.

When an infection spreads in a community, an individual's probability of becoming infected depends on both her susceptibility and exposure to the contagion through contact with others. While one often has knowledge regarding an individual's susceptibility, in many cases, whether or not an individual's contacts are contagious is unknown.We study the problem of predicting if an individual will adopt a contagion in the presence of multiple modes of infection (exposure/susceptibility) and latent neighbor influence. We present a generative probabilistic model and a variational inference method to learn the parameters of our model. Through a series of experiments on synthetic data, we measure the ability of the proposed model to identify latent spreaders, and predict the risk of infection. Applied to a real dataset of 20,000 hospital patients, we demonstrate the utility of our model in predicting the onset of a healthcare associated infection using patient room-sharing and nurse-sharing networks. Our model outperforms existing benchmarks and provides actionable insights for the design and implementation of targeted interventions to curb the spread of infection.