The COVID Conversation | Inference

THE ENTIRE WORLD has experienced the COVID-19 pandemic, and within three months of the first recorded case, more than half of the global population was undergoing some form of quarantine. Scientists struggled to make sense of a new, poorly understood disease, and decision-makers scrambled to find data that could help them guide policy. From roughly January 2020 to the present, scientific papers devoted to COVID-19 doubled every fourteen days, for a total of more than 100,000 papers. As the pressure for information increased, the health sciences embraced preprint publication—work that was uploaded to the web without scrutiny.

The global media has been galvanized by the COVID-19 pandemic, with print, video, and audio outlets scrambling for news. This followed a decade-long change in the media landscape. Once dominated by a small number of high-profile outlets, it has become fractured: sound bites, headlines, and video fragments now a part of the public conversation. The new media landscape did not scrupulously distinguish among peer-reviewed papers, preprint uploads, and opinion pieces. A preprint study on COVID-19 seroprevalence in Santa Clara County, California, quickly hit the front pages following its publication on the preprint site medRxiv.1 News outlets reported that the virus had spread “50 to 85 times more than confirmed cases”2 before epidemiologists had the chance to comment on the paper’s many flaws.3 Other stories promoted drugs such as hydroxychloroquine,4 and still others were devoted to predictions from various infectious disease models.5 “2.2 Million People Could Die in US,” claimed one news site, citing a controversial model released by the Imperial College in London.6 Politicians reacted to the rapidly evolving narratives, using fragmentary stories of complicated scientific observations to inform policies that ultimately influenced the lives of millions.

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