Interacting contagions call for complex models
When disease modelers map the spread of viruses like the novel coronavirus, Ebola, or the flu, they traditionally treat them as isolated pathogens. Under these so-called “simple” dynamics, it’s generally accepted that the forecasted size of the affected population will be proportional to the rate of transmission. But according to former SFI postdoc Laurent Hébert-Dufresne at the University of Vermont and his co-authors Samuel Scarpino at Northeastern University, a former Omidyar Fellow, and Jean-Gabriel Young at the University of Michigan, the presence of even one more contagion in the population can dramatically shift the dynamics from simple to complex.