Tulane Professor Using Math to Predict Spread of COVID-19

NEW ORLEANS – As the news of the coronavirus (now officially named COVID-19) continues to dominate international headlines, Tulane University mathematical epidemiologist James “Mac” Hyman has been busy using mathematical models to help predict the spread of the disease and the effectiveness of potential efforts to stop it.

Hyman and colleagues from other institutions recently published a paper that explains a method of using available data to predict the number of new infections that will occur. The goal is to give public health officials as much information as possible to help them plan the type and amount of interventions needed to halt an epidemic.

“We’re trying to create models that can be more effective in guiding public health efforts,” said 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.”

In Hyman’s estimation, COVID-19 has the potential to become a worldwide pandemic.

“I’m very concerned that this is highly infectious virus – more than we’ve seen in a long, long time,” he said. “Not since the days of measles, mumps and rubella. And it’s unrealistic that it can be contained forever. The question is not if but when – but I’m hopeful that the vaccine development will happen so rapidly that it will be able to keep it from being disastrous.”

Hyman said that in the “complex fog” of an outbreak, the world needs actions guided by expert consensus and data-driven models – and the information needs to be shared as efficiently as possible.

“Right now there are several separate repositories where people are storing the data,” he said. “It would be nice if they coordinated all these efforts and there was one where could all go or at least one central hub.”

This story has been updated to include additional quotes.

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