Contagions, severe weather, natural disasters, civil unrest – whatever data scientists are forecasting using network models, simulation-based methods are often the most effective, according to researchers at the University of Virginia Biocomplexity Institute and Princeton University, whose findings were published in the paper, “Fundamental Limitations on Efficiently Forecasting Certain Epidemic Measures in Network Models,” by PNAS (Proceedings of the National Academy of Sciences).
Researchers from UVA’s Biocomplexity Institute and School of Engineering and Applied Science, working with a team of multi-disciplinary scientists from around the world, have spent the last two years developing highly advanced computational models designed to inform policy makers, save lives and prepare for future global epidemics.
As the COVID-19 global health crisis continues to unfurl worldwide, the questions around global pandemics are no longer “if” they will occur, but how frequent, widespread, and severe those that come next will become.
The Biocomplexity Institute and Initiative at the University of Virginia is pleased to announce the appointments of Dr. Daniel J. Rosenkrantz, Dr. Richard E. Stearns, and Dr. Catherine E. Woteki as Distinguished Institute Professors.