Predicting and understanding disease outbreaks doesn't just involve epidemiology. It takes math, too. For centuries mathematicians have tackled questions related to epidemics and pandemics, along with potential responses to them. For instance, 18th-century Swiss mathematician Daniel Bernoulli is credited with developing the first mathematical epidemiology model, which focused on analyzing the effects of smallpox inoculation on life expectancy. Mathematicians have continued this work to the present day, including during the COVID pandemic.

One such researcher is Abba Gumel, a mathematician and mathematical biologist at the University of Maryland, College Park. He was recently elected to the current class of fellows of the American Association for the Advancement of Science (AAAS). Mathematicians such as him are indispensable to the mission of identifying and averting the next pandemic. Succeeding in this quest, however, requires that they unite with experts from other fields and work together to solve these multifaceted disease-transmission problems.

Gumel spoke to Scientific American about how he is using mathematics to combat infectious diseases and about the questions he hopes to address before the next pandemic hits.

[An edited transcript of the interview follows.]

Tell me about a time that one of your recent findings surprised you.

We showed in our paper on COVID lockdown measures that the number of cases, hospitalizations and mortality would have been dramatically reduced if we had started community lockdowns a week or two earlier than we did. This means hitting the disease hard early, before it enters the exponential phase of transmission. It would have dramatically altered the course of the pandemic in the U.S. and perhaps saved hundreds of thousands of lives.

What role can mathematicians play in preventing the next pandemic?

What mathematicians are doing to help prevent the next one is basically working on lessons we have learned. We have learned that masks worked from mathematical analysis and modeling but also from what happened in society. Societies that have high coverage of masks and high-quality masks did well in reducing cases and mortality. Vaccines work, we have shown clearly, if we raise the level of herd immunity required. For the next pandemic, if we have certain vaccines with starting efficacies, we can predict the minimum proportion we need to vaccinate to achieve vaccine-induced herd immunity.

We're coming up with this bucket list of things to do to prevent, we hope, the next one but even if we do get hit—and we're going to get hit—to minimize the burden of the next one and to greatly suppress it before it becomes a problem. These are things we need to do to make sure the next one doesn't kill one million Americans.

Sometimes when I talk about it, I cry because I see that if we had done the right thing, none of this would have happened.

What are some pressing open questions you hope to address before the next pandemic hits?

I am interested in determining whether stockpiling high-quality face masks and making them widely available early in a new COVID-like pandemic can obviate the need to shut down the economy until a safe and effective vaccine becomes available.

I am interested in determining the impact of increases in global temperature caused by global warming on the population and distribution of wild animal populations and associated viral zoonotic diseases and the likelihood of a spillover event.

I am also interested in quantifying the burden of a potential highly contagious and highly fatal pandemic of a contact-based disease such as Ebola viral disease. The world community thankfully averted such a catastrophe when we came together and effectively contained the Ebola outbreaks that took place in Guinea, Liberia and Sierra Leone in 2014–2016.

Before the COVID pandemic, you mainly focused on mosquito-borne diseases. Are there fundamental differences in how you approach studying infectious diseases such as malaria that involve a vector?

Yes, there's a big difference. There's no direct human-to-human transmission. Mosquitoes get infected by biting infectious humans. If I'm infected and a mosquito takes a blood meal from me, there's some probability that the mosquito can also get my Plasmodium parasite and become infected. So the modeling types are different.

West Nile is transmitted by mosquitoes not only to humans but also to other hosts such as crows. But the birds fly long distances, so we use spatial models.

What are some other factors that affect your modeling decisions?

The type of model you choose depends on the level of data you have. An agent-based model allows you to track each individual: their risk of getting infected, what they do each day, and all that. That's very useful in determining who infected whom. But it's data-hungry. You need a lot of data at an individual level.

The type of model you choose depends on the problem you want to solve, the type of data you have and the quality of the data.

What does your selection as an AAAS Fellow mean to you?

It's a huge honor. And the honor belongs to the large number of people in my support network.

This gives me an additional platform to multiply my efforts in community outreach. I've been focused on Africa and other developing regions of the world to provide opportunities for people to be the best they can become in STEM [science, technology, engineering and mathematics]. I'm focused on young people, especially women. I'm focused on getting a lot more women in rural areas to get into STEM and be among the best. I'm very worried about gender inequity. I'm doing whatever I can to bridge that gap. Particularly, where I came from in Africa [Nigeria], we need a lot more women in STEM.

We have a tremendous responsibility. We need to make science accessible to everyone around the world. It doesn't work at all if only a few countries are scientifically advanced. Look at what's happened. COVID started in China, but it became a problem for everyone.

We're all vulnerable to what's happening in faraway places—the same with inequity in STEM, inequity in health care, inequity in economics. If we're doing well, but our neighbor is not, it's just a matter of time before we also suffer. It's the same thing with viral things happening in faraway places. We had better pay attention because it's a plane ride away from coming to us.