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An illustrated portrait of Caitlin Rivers. Rebecca Clarke for Vox

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Our health care data infrastructure is broken. Caitlin Rivers hopes to fix it.

Without accurate data on disease and outcomes, policymakers can’t make good public health decisions.

Without accurate data, it’s tough for policymakers to make good public health decisions. During the beginning of the Covid pandemic, the US was called out as one of the worst performers in response, partly because, beyond the sheer number of deaths, critical state-level data on positive tests, case counts, and contact tracing still weren’t being reported across most of the country.

Because of how fragmented the American health care system is, not every hospital or government agency tracks information the same way. This can lead to vital information getting lost or delayed when gathered by state or federal public health departments.

More information and better data infrastructure is exactly what Caitlin Rivers, an epidemiologist with the Johns Hopkins Center for Health Security, has worked tirelessly to bring attention to — in the hopes that government officials can improve outcomes for any disease or outbreak.

“The topic is not a magnet for clicks,” said Rivers about metrics and data in a Substack post on the monkeypox outbreak response. “But I think it remains a big missing piece of our response.”

In the first half of 2020, she testified in front of Congress, pushing hard to increase testing capacity and contact tracing so that states in lockdown could make an informed decision on when and how quickly to reopen. On Twitter and in interviews, she was outspoken about her recommendations, repeatedly emphasizing the need for information to come before decision-making.

“In the US, situational awareness is largely courtesy of volunteer projects and 50 state health dept websites,” she tweeted in March 2020, emphasizing the need to “modernize our surveillance and reporting infrastructure to be able to respond effectively.”

Rivers had already accumulated years of expertise in pandemic forecasting when COVID hit. She conducted her PhD studies at the same time as the Ebola outbreak in West Africa in 2014-16, during which she helped prepare reports for the White House. In 2019, working with the Center for Health Security at John Hopkins, she co-authored a report highlighting the need for “outbreak science” to model the course of a pandemic and aim the response.

The US response to Covid showed that much work still needed to be done. “Things did not unfold as I would have liked them to,” Rivers told Science in 2020. The infrastructure to rapidly scale up testing and contact tracing — or even for individual states to report their numbers consistently to the federal public health agencies — just didn’t exist. As part of the effort to improve pandemic response, the Centers for Disease Control and Prevention in 2021 established a “weather service” pandemic forecasting project, with Rivers — who had advocated for such efforts in the past — tapped to be associate director of the effort.

Beyond raising awareness for better data infrastructure, Rivers highlights places where public health experts themselves can improve. Her Substack regularly discusses the lessons learned from Covid and how to apply them to the recent monkeypox outbreak, future pandemics, or the return of older threats, like polio. Rivers stresses that preparations need to be in place before a pandemic and be maintained after the pandemic ends.

“A cycle of panic and neglect shadows public health: frenzied action tends to be followed by loss of interest as a threat recedes,” she wrote for Nature. “Public-health officials, governments and advocates must not let that impulse prevail.”