A team of researchers from Indiana University performed random testing for SARS-CoV-2 across the state. The results offer some of the most accurate data to date about important aspects of the virus.

Overwhelmed by all the COVID-19 statistics in the news? Here are five tips from data scientists for handling the glut of information.

The COVID-19 death toll in the US is now over 130,000. What do 130,000 fatalities look like? A biostatistician provides some perspective.

A global team of researchers searches for insights during a weeklong virtual “datathon.”

Many more people have been infected with coronavirus than the statistics suggest.

Without a vaccine, the cost of reaching herd immunity during a pandemic is counted in lives lost, and it won't quickly stop the virus's spread.

Why one city suffers significantly more deaths than another isn't always obvious. A simple experiment shows how failing to consider certain factors can point policy makers in the wrong direction.

Isolat, a volunteer collaboration organized by the Institute for Data, Systems, and Society, informs coronavirus policy by analyzing data associated with the pandemic.

Three graphs of mortality data tell the story of the direction the UK and the world are heading in after the peak of the coronavirus outbreak.

Researchers and public health officials still don't know how widespread nor how deadly the coronavirus really is. Random testing is a way to quickly and easily learn this important information.