The Science Behind Studying Viruses
Profile: Jude Kong, Disease Modeler
Dr. Jude Kong has spent the COVID-19 pandemic building models. Not the physical kind—the kind that run on computers.
During the COVID-19 pandemic, these models are saving lives. Epidemiologists use them to monitor the spread of the pandemic, to evaluate efforts to control it, and to guide policymakers as they make decisions about measures like vaccination, school closing, and allocating healthcare resources.
Kong, an assistant professor in the Mathematics and Statistics Department at York University in Toronto, Canada, grew up in Cameroon, the third of five children of a single mother, a subsistence farmer. As a little boy he would finish his math homework quickly, then help his friends with theirs. He worked his way through college as a secondary-school math teacher.
The year he graduated, he attended a workshop on disease modeling, something he didn’t know much about. But he had plenty of firsthand experience with infectious diseases—malaria in particular, which had taken the lives of his aunt and several of his cousins.
“People were dying a lot. People I knew, loved ones. I wanted to prevent that from repeating itself, within my community, anymore,” he says. Here were mathematicians working to figure out how diseases spread—and how to stop them. He knew this was what he had to do.
“A model is a representation of a system that can be used to explore its behavior,” says Kong. Kong and his colleagues build models and use them to make recommendations to policymakers.
The models themselves are key, of course. But to work, they need the right data—and plenty of it. Without locally relevant data, says Kong, the models will not produce useful results. That’s why it’s vital for modelers to collaborate with community leaders.
“You ask what they need in their communities. As a scientist, as a modeler, you have the tools, but you don't know the problem," says Kong. "[The community leaders] describe the problem to you. You work with them and present the tools. Then you tell them what data to collect. They get that data, it goes into your model, and it ends up informing policies in the right direction, because the data was informed by the community. And there's a feedback into the community, too.”
Kong and his colleagues used this approach to study the effects of what he calls self-medication on the spread of COVID-19. By self-medication, he means unproven treatments that some people believe will cure or prevent the disease, which they rely on instead of on proven treatment.
In the United States, for example, some people self-medicated with ginger, hydroxychloroquine, or Ivermectin. In Ghana, some believed a particular kind of green tea would keep them safe or cure them. In Cameroon, it was a certain very strong alcoholic drink, or local traditional medicines.
“You need to respect that people will believe in things because that’s what defines them, what makes them unique. But you need to take that into your model and account for that,” says Kong.
He and his colleagues built a model that incorporated self-medication, something previous models had not done. They took their results to policymakers there, who worked with community leaders to address the issue. “Those community leaders will know how to discourage such practices within their communities,” he says. “They can use local relationships, local radio stations, and so on. It's very important that local community leaders are involved in the entire process.”
It's very important that local community leaders are involved in the entire process.
Information from local communities is especially important because differences in communities’ history translate into disparities in health outcomes. That’s particularly true during a pandemic. COVID-19 is spreading differently between different racial and ethnic groups based on their susceptibility, says Kong, due to historical factors such as crowded, segregated housing.
“If you assume that one size fits all the country,” he says, “you will not be able to capture the dynamic, because you are ill-informed through the data that you have.”
Kong stresses that disease modeling is a dynamic process. As a pandemic spreads, its course will change. Interventions, treatments, public messaging, public behavior, vaccination rates—all change. Even the disease itself will change, as new variants arise.
“If you keep working with community leaders, they will inform you, ‘Oh, things are changing in this direction. How do we address this?’ And then you come in and you adjust your model,” says Kong. “That is the right way to go.”
Created with the support of the City of New York Department of Health and Mental Hygiene. © 2023 City of New York