New Mathematical Model Can More Effectively Track Epidemics

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A new model developed by Princeton and Carnegie Mellon researchers improves tracking of epidemics by accounting for mutations in diseases.

A new model developed by Princeton and Carnegie Mellon researchers improves tracking of epidemics by accounting for mutations in diseases. Now, the researchers are working to apply their model to allow leaders to evaluate the effects of countermeasures to epidemics before they deploy them.

“We want to be able to consider interventions like quarantines, isolating people, etc., and then see how they affect an epidemic’s spread when the pathogen is mutating as it spreads,” said H. Vincent Poor, one of the researchers on this study and Princeton’s interim dean of engineering.

The models currently used to track epidemics use data from doctors and health workers to make predictions about a disease’s progression. Poor, the Michael Henry Strater University Professor of Electrical Engineering, said the model most widely used today is not designed to account for changes in the disease being tracked. This inability to account for changes in the disease can make it more difficult for leaders to counter a disease’s spread. Knowing how a mutation could affect transmission or virulence could help leaders decide when to institute isolation orders or dispatch additional resources to an area.

Read more at Princeton University

Image: A new model developed by Princeton and Carnegie Mellon researchers improves tracking of epidemics by accounting for mutations in diseases. Now, the researchers are working to apply their model to allow leaders to evaluate the effects of countermeasures to epidemics before they deploy them.  CREDIT: Princeton University