When disease outbreaks occur, people with essential roles -- healthcare workers, first responders, and teachers, for example -- are typically up close and personal with infected people. As these front-line workers become infected, healthy individuals take their places.
Mathematical biologist Samuel Scarpino, who creates and analyzes epidemiological models, wondered how this exchange of critical people affects the spread of disease. The practice clearly raises the risk of infection for the replacement individuals - but the population dynamics of this increase are neglected in existing epidemiological models.
Scarpino, while he was an Omidyar Fellow at the Santa Fe Institute, set out to quantify that risk and understand its influence. He enlisted the help of theoretical physicist Laurent Hébert-Dufresne, a James S. McDonnell Fellow at the Institute, and Antoine Allard, Hébert-Dufresne's longtime collaborator from the University of Barcelona. Both study complex patterns in networks.
The trio integrated this "human exchange" into network models of disease and found that replacing sick individuals with healthy ones can actually accelerate the spread of infection. Scarpino and Hébert-Dufresne tested their ideas on 17 years' worth of data on two diseases: influenza and dengue. Their analysis, just published in Nature Physics, reveals that human exchange likely accelerates outbreaks of influenza, which spreads via human contact. But it has no effect on the spread of dengue - which makes sense, as dengue spreads via mosquitoes.
"We didn't see a strong signal in diseases where we didn't expect it," says Hébert-Dufresne.
Scarpino, now an assistant professor at the University of Vermont, says he hopes to see this effect integrated into future epidemiological models. "Models where you start to incorporate slightly more realistic human behavior are essential if we're going to make high-fidelity public health and clinical decisions," he says.
The research was published online August 1 in Nature Physics.
Source: Santa Fe Institute