A new study has shown that flawed epidemic modeling techniques can lead to overestimating the number of people who could get infected during a pandemic—resulting in unnecessary measures such as lockdowns and mass vaccination campaigns.
The peer-reviewed study, published in the Journal of Physics Complexity on Jan. 9, associated existing models of forecasting epidemics with the structure of social networks among people. The most widely used forecasting method is a “compartmental model,” which usually makes an assumption of “random mixing,” meaning that any individual can infect any other person. However, this is a flawed assumption that can lead to “greatly overestimating the number of infections,” the study pointed out.
