Global data networks that connect people through their devices have made it possible to create accurate short-term forecasts of new COVID-19 cases, using a method pioneered by two researchers at Sandia National Laboratories.
Jaideep Ray and Cosmin Safta used a model developed by Ray more than a decade ago to track plague epidemics using statistics. For COVID-19 they also drew upon the advice of their Sandia co-workers with expertise in modeling, mathematics and software engineering.
“I first started using this method in 2008-09. Cosmin and I adapted it in 2010 to track influenza-like illnesses,” Ray said. “When COVID-19 began to spread so rapidly, we knew we could use the same method to help forecast the outbreak.”
Ray and Safta use publicly available data from the Centers for Disease Control and Prevention, The New York Times Data Repository, Johns Hopkins University and various state departments of health. Within minutes, and without the need for high-performance computing resources, the researchers can forecast new cases in a region or nationally for the next seven to 10 days. Since April, the number of new cases have roughly followed the trends predicted by Ray and Safta.
“This method is a relatively easy and inexpensive way to get short-term forecasts about new coronavirus cases that decision-makers can use to allocate health care resources and response,” Safta explained. “This method is much easier and cheaper to do than methods that require more robust computers and manpower.”
The range of accuracy for the predictions