As COVID-19 spreads worldwide, leaders are relying on mathematical models to make public health and economic decisions.
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.
"In reality, these are physical things, but in this model, they are abstracted into parameters that can help us more easily understand the effects of policies and of mutations," Poor said.
If the researchers can correctly account for measures to counter the spread of disease, they could give leaders critical insights into the best steps they could take in the face of pandemics. The researchers are building on work published March 17 in the Proceedings of the National Academy of Sciences. In that article, they describe how their model is able to track changes in epidemic spread caused by mutation of a disease organism. The researchers are now working to adapt the model to account for public health measures taken to stem an epidemic as well.
The researchers' work stems from their examination of the movement of information through social networks, which has remarkable similarities to the spread of biological infections. Notably, the spread of information is affected by slight changes in the information itself. If something becomes slightly more exciting to recipients, for example, they might be more likely to pass it along or to pass it along to a wider group of people. By modeling such variations, one can see how changes in the message change its target audience.
"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."
"Our model is agnostic with regard to the physical network of connectivity among individuals," said Poor, an expert in the field of information theory whose work has helped establish modern cellphone networks. "The information is being abstracted into graphs of connected nodes; the nodes might be information sources or they might be potential sources of infection."
Obtaining accurate information is extremely difficult during an ongoing pandemic when circumstances shift daily, as we have seen with the COVID-19 virus. "It's like a wildfire. You can't always wait until you collect data to make decisions - having a model can help fill this void," Poor said.
"Hopefully, this model could give leaders another tool to better understand the reasons why, for example, the COVID-19 virus is spreading so much more rapidly than predicted, and thereby help them deploy more effective and timely countermeasures," Poor said.
Besides Poor, co-authors included researchers Rashad Eletreby, Yong Zhuang, Kathleen Carley and Osman Ya?an of Carnegie Mellon. The work was supported in part by the Army Research Office, the National Science Foundation and the Office of Naval Research.
Source: Princeton University
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