Q&A: Why the next pandemic could be stopped by math, not borders

The common denominator between LinkedIn's vast professional network, the global airline system, and the human brain is network science, a field of study that explores connections and patterns. At the University of Virginia, professor YY Ahn uses this approach to tackle public health crises, including potential pandemics. Recently, the CDC made reporting of Cyclospora and five other pathogens optional, a decision that highlights the recurring issue of treating outbreaks as waste products after the panic subsides.

This pattern has been observed time and again, where prevention units are created after an outbreak has occurred.

When outbreaks happen, the instinctive response is often to close borders, but network science suggests an alternative approach: focusing on the source of the outbreak to contain and understand its spread. This strategy is critical because that's where the exponential growth is unfolding and where we have the most leverage to control it. The longer it takes to identify and isolate the source, the harder it becomes to curb the disease's spread.

Epidemiologists specializing in network epidemiology understand this concept, but it's not commonly known to the general public. One of the challenges they face is the "preparedness paradox" – the more successful they are at prevention, the less visible and necessary they become.

At the heart of network science is the discovery that once a disease starts spreading through the airline network, it's almost impossible to stop it by closing airports. Even if travel is reduced by 90%, the disease will still find its way elsewhere, just at a slightly later time. This is due to the airport network being optimized for easy movement of people worldwide, making it a powerful vector for disease transmission.

The general rule in disease prevention is to control it at the source as early as possible, before it explodes. The 2003 SARS outbreak, which started in China, serves as an example. It seeded an outbreak as far away as Toronto, but through contact tracing, it was eventually controlled. However, COVID-19, with its high contagiousness and many asymptomatic cases, posed a different challenge.

Network theory can help identify different contact tracing strategies. One approach involves going forward to trace who a patient has infected, while the other involves going backward to trace who infected the patient. The latter method often reveals highly connected individuals who are likely sources of superspreading. This insight comes from combining network theory with empirical data and simulations.

The role of misinformation in disease spread is another area where network science has made a significant impact. For instance, during the COVID-19 pandemic, people who were misinformed may have changed their behavior, particularly their attitude towards vaccination, leading to greater spread. Additionally, rumors of the new disease in November and December 2019 led people to buy masks before the official announcement, a behavior driven by rumors and social networks.

The power of social networks in spreading information and the resulting inequality in real-time during the pandemic are evident. Network science demonstrates that early containment at the outbreak source offers greater control than border closures, with travel restrictions only delaying the spread briefly. On the other hand, contact tracing, especially backward tracing, can identify highly connected superspreading sources.

Social networks also shape preventive behavior and can influence vaccination decisions driven by misinformation.