Tulane math professor leads effort to map spread of coronavirus

Feb 19, 2020

2 min

James "Mac" Hyman

James “Mac” Hyman, the Evelyn and John G. Phillips Distinguished Professor in Mathematics at Tulane University, is using mathematical models to better understand and predict the spread of COVID-19 and to quantify the effectiveness of various efforts to stop it.

 

The goal of Hyman’s work in “mathematical epidemiology” at the Tulane School of Science and Engineering, is to help the public health community understand and anticipate the spread of the infection and evaluate the potential effectiveness of different approaches for bringing it under control.


“We’re trying to create models that can be more effective in guiding public health efforts to mitigate an epidemic,” Hyman said. “It’s about figuring out what needs to be in a model to estimate the risk of someone being infected and predict the risk that an infected person will be to someone else.



 

“In the coronavirus, we must account for the way that an infected person can infect the environment, such as a table or door handle, and others can be infected, even if they don’t have direct contact with an infected person.”


With several groups working independently to predict the spread of COVID-19, Hyman said coordination among the groups is essential and should be supported by government agencies.

 

He thinks the World Health Organization needs to take the lead by organizing an international effort that focuses on three specific aims – predicting further spread of the virus in China, predicting its potential to spread elsewhere or under various conditions, and predicting the effectiveness of mitigation strategies, such as quarantine, contact reduction, hand hygiene and face masks.


Hyman is available for media interviews and can be reached at mhyman@tulane.edu or by contacting Barri Bronston at bbronst@tulane.edu.

 


 


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James "Mac" Hyman

James "Mac" Hyman

Evelyn and John G. Phillips Distinguished Professor in Mathematics

James "Mac" Hyman works to build a solid mathematical foundation for difference approximations to partial differential equations

CoronavirusEpidemicsLife SciencesStatistical ModelingDisease Management

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