key: cord-0690306-om7aa212
authors: Bonate, Peter L.
title: COVID-19: opportunity arises from a world health crisis
date: 2020-03-26
journal: J Pharmacokinet Pharmacodyn
DOI: 10.1007/s10928-020-09681-5
sha: 49588a26c0af04084badce50e660dcec4da41953
doc_id: 690306
cord_uid: om7aa212

nan

hypothetical town and how to ''flatten the curve'' [2] . Even medical journals that normally do not publish mathematical models are quick to publish modeling results related to coronavirus. The Lancet [3] published a study using a 4-state model (susceptible, exposed, infectious, and removed) combined with a random walk process to understand the transmission rate of the virus over time.

Scientific papers, like the Lancet paper, are generating a lot of press around their results and implications. As modelers, care needs to be taken in disseminating these results and I would like to highlight a few issues regarding their dissemination. First, model predictions themselves have errors associated with them and we need to ensure that these are reported as well. Second, the assumptions we use to build our models must always remain transparent. Lastly, we must remember who the audience is-the general public or our fellow scientists. We must always present our results with the audience in mind. The NY Times published a graph of fatality rate vs. rate of disease spreading for a variety of different viruses, trying to put into context how coronavirus compares to say SARS or chickenpox [4] . Because of the range of the fatality rate data, the authors used a log-scale without any minor tics. Without these minor tics, the average reader would conclude that the death rate was near 10%, but because of the log-scale, the actual value was around 2 to 3% (the true value is still being debated). The graph was not intentionally misleading, but it was also not drawn with the average reader in mind. This graph was corrected a few weeks later by the authors, along with some added text to explain what a log-scale. Errors like these decrease the credibility of the point you are trying to make and distract from what your message is.

Mathematical models are all around us, even if the average person does not realize it. They help us every day to reduce uncertainty. Should I take an umbrella because it might rain? How can I get to this new address I am unfamiliar with? What is the risk of a heart attack for someone like me? I don't want to minimize the tragedy that is happening, and will continue to happen in the near future, but coronavirus offers the modelers of the world an opportunity-an opportunity to reduce the uncertainty and fear of what will come, an opportunity to provide advice on how ''flatten the curve'' and reduce the death toll, and there are many other opportunities just waiting for an answer. If you see an opportunity, take it. Mankind may thank you.

Be well and stay healthy,

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How outbreaks like coronavirus spread exponentially, and how to ''flatten the curve

Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The Lancet: Infectious Diseases

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Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations

Peter L. Bonate, PhD Editor-in-Chief