When researchers study the transmission of an infectious disease such as COVID-19 or want to make predictions about how it might impact people in the future, they create epidemiological models.
These models can be computer simulations or other mathematical representations of the virus and its impacts. Government officials and public health leaders rely on them to make decisions affecting public health. That’s why journalists covering the new coronavirus need a basic understanding of what epidemiological models can and cannot do and how to explain the knowledge they reveal.
“Mathematics may sound like an unlikely hero to help us overcome a global epidemic; however, the insights we gain from studying the dynamics of infectious diseases by using equations describing fundamental variables are not to be underestimated,” the editors of the PLOS ONE academic journal write in a recent article.
“Mathematical modelers make use of available data from current and previous outbreaks to predict who may get infected, where vaccination efforts will be most effective, and how to limit the spread of the disease.”
Another reason reporters need to know about epidemiological modeling: As COVID-19 infection and death rates have risen across our planet, thousands of new academic papers have flooded the internet, many of which are based on models. Most papers posted online and uploaded to preprint research servers have not been vetted by scholars. In fact, a small fraction have undergone formal peer review, a process by which experts in that particular field of study analyze and critique the paper and help guide revisions.
Journalists unfamiliar with models such as the commonly used SIR model will have trouble spotting problems in these new studies. They’re more likely to make mistakes and might unknowingly exclude crucial context.
Denise-Marie Ordway – Journalist’s Resource – May 8, 2020.
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