The coronavirus disease 2019 (COVID-19) pandemic, caused by the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has already claimed more than 470,000 deaths worldwide at the time of this writing (1) and is likely to claim many more. Models can help us determine how to stop the spread of the virus.
Chaotic systems such as pandemics are fundamentally unpredictable. A constructive role for science is to identify interventions—including social distancing, mask wearing, policies of isolation, and travel restrictions—that will help the number of active infections to decline exponentially. Image credit: Shutterstock/Travelerpix.
But it is important to distinguish between what models can and cannot predict. All models’ assumptions fail to describe the details of most real-world systems. However, these systems may possess large-scale behaviors that do not depend on all these details (2). A simple model that correctly captures these large-scale behaviors but gets some details wrong is useful; a complicated model that gets some details correct but mischaracterizes the large-scale behaviors is misleading at best. The accuracy and sophistication of a model’s details matter only if the model’s general assumptions correctly describe the real-world behaviors of interest.
Carefully delineating models’ strengths and shortcomings will not only clarify how they can help but also temper expectations among policymakers and members of the public looking to understand the full impact of the virus in the weeks and months ahead. More important even than prediction is the ability of models to guide actions that can change this impact, including actions that can potentially drive the virus to extinction.
Understanding what models cannot predict is sometimes more important than understanding what they can. For example, in a chaotic system such as the weather, only very short-term predictions are accurate; small changes in the present can result in very large changes in the …
[↵][1]1To whom correspondence may be addressed. Email: asiegenf{at}mit.edu.
[1]: #xref-corresp-1-1
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