Ever since the coronavirus outbreak was first reported, several epidemiological models and their predictions have become part of our daily routine. However, these models are confusing at times and may not tell you a clear picture. Justin Lessler and Elizabeth Lee of the Bloomberg School’s Infectious Disease Dynamics group have tried to highlight things that are takin into consideration while making such predictions.
Different models are based on different types of assumptions. Elizabeth Lee feels that there should be clarity about ground rules that modelers are following. Each model tries to explore questions like how long infected individuals can be contagious considering the effectiveness of stay-at-home orders. For instance, a model talking about the viral spread is most likely to assume that a community mostly stays hidden but this soon starts falling apart.
Some models are built on incomplete information. “Models are most useful when we have the least data on which to base our decisions,” Lessler says, “but that’s also when the models are the least well-informed.” Scientists have now got some clarity on things like the role of superspreading events and infection fatality rate. These are crucial information, but they still lack some critical information.
Insights are not appropriate for each and every situation. Initial findings from China and Italy were base for many researchers. But the model developed on these findings may not be directly comparable to other parts of the world. Things like ways to counter pandemic and health care systems – for example, access to testing and patient treatment protocols – are crucial for these models. Lessler pointed out that it remains unclear why New York faced such a severe crisis in comparison to other US cities. “Maybe the disease isn’t as transmissible in less dense areas as [it is] in denser areas, or maybe there’s a big effect of climate,” he says. “But we are still figuring that out.”
These are predictions and not prophecies. People always think why good models fail to predict the actual outcome. The main reason is that these models often help us to formulate a policy to prevent the fate.
