Stopping the Next Big One: Harnessing Network Science to Counter Modern Global Health Threats
by Grant Rosenteel (C'19)
This year we commemorate the centennial of the global 1918 influenza pandemic, one of the most catastrophic events of the past century, that killed millions and wreaked havoc on the global economy. Although infectious diseases, such as influenza, have caused massive mortality and morbidity rates in the twentieth century, in 2018 attention has shifted from infectious diseases to chronic diseases. While we have made great strides to reduce the number of deaths due to infectious diseases since the 1918 pandemic, humanity again faces the threat of emerging and reemerging infectious diseases in a rapidly changing and increasingly connected world.
Infectious diseases are making a comeback. In the twenty-first century alone, there have been numerous outbreaks of infectious diseases, including SARS-CoV, MERS-CoV, Nipah virus, Ebola virus, and yellow fever virus. Infectious diseases are far from being eliminated, and with the potential to cause extreme damage to the global economy and stability, new tools are needed in our epidemiological toolbox to outsmart them. Luckily, network science has revolutionized the way we think about infectious disease transmission.
Network science is a relatively new field of computer science that is concerned with complex systems of distinct entities and their connections, called nodes and edges, respectively. Network science offers an alternate way to analyze systems, whether they be telecommunications, social, or biological systems. The branch of network science related to infectious diseases and their transmission between individuals is known as network epidemiology. Network epidemiology differs from traditional epidemiology because it takes into account the differences and connections between individuals within a population and between populations, as well as the strength, timing, and location of those connections.
Network epidemiology has transformed the study of infectious diseases. In the past, research on disease transmission was limited to studying naturally occurring outbreaks or infecting animals in a laboratory. Now, using computers, it is possible to simulate the spread of infectious diseases in a virtual population, provided that a few parameters about the disease and population are known or can be estimated. These virtual experiments have been essential in gaining insight into the characteristics of different pathogens and evaluating the most effective public health interventions to stop an outbreak.
Network epidemiology is a vital tool that can be used to prepare us for the next global pandemic. Simulating hypothetical outbreaks will allow us to identify areas and populations most vulnerable to such an event, and this data can be used by policymakers and stakeholders to test mitigation techniques before an outbreak occurs. Infectious diseases, such as pandemic flu, are incredibly difficult to control once they exist in a population of susceptible individuals. The best way to prevent an event like the 1918 flu pandemic from happening again is to have systems in place to detect and stop it before spreading. By utilizing the tools of network epidemiology, it is possible to identify those systems and understand how they will work in the event of an outbreak.
Those of you who have not gotten a good night's rest since seeing the movie Contagion can now rest easy knowing that we are not empty-handed in our war against lurking diseases.
Grant Rosensteel (C'19) is an undergraduate studying biology of global health and a student fellow with the Global Health Initiative.