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As COVID-19 spreads worldwide, leaders are relying on mathematical models to make public health and economic decisions. A new model 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.
John Sullivan, Senior Editor at Princeton University, Engineering School writes, As COVID-19 spreads worldwide, leaders are relying on mathematical models to make public health and economic decisions.
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| 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...
"The spread of a rumor or of information through a network is very similar to the spread of a virus through a population," Poor said. "Different pieces of information have different transmission rates. Our model allows us to consider changes to information as it spreads through the network and how those changes affect the spread."
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Journal Reference:
- Rashad Eletreby, Yong Zhuang, Kathleen M. Carley, Osman Yağan, H. Vincent Poor. The effects of evolutionary adaptations on spreading processes in complex networks. Proceedings of the National Academy of Sciences, 2020; 117 (11): 5664 DOI: 10.1073/pnas.1918529117
