In the debate over responses to the coronavirus pandemic, the scientific models used to project the future course of viral spread have become deeply politicized. “The minute I hear anybody start talking about models and modeling, I blanch,” Rush Limbaugh told his listeners at the end of March, after lambasting the pandemic modelers’ “wild-ass numbers.” Other prominent conservatives have kept up the attack, calling such models “garbage” or worse, and are even demanding congressional hearings on the matter. This treatment of pandemic science is pathological, and threatens our ability to implement effective responses.
As the US and the world grapple with next steps in the unfolding pandemic, our model-based predictions are indeed varied, of uncertain or poor accuracy, and wielded selectively. That these necessary and useful scientific tools should also be politicized creates an extremely troubling situation.
It need not be so. Lessons learned about the use and misuse of models in other contexts, most notably weather forecasting, can help us to flatten the curve on politicized pandemic science. We still have time. But to improve the use of models in the pandemic, and limit their further politicization, we need to move beyond model-bashing on one side, and the facile characterizations on the other of science believers versus science deniers. Rather than succumbing to the naked partisanship that has infected the climate change debate, we should take practical steps to ensure that coronavirus models are transparent and based on the best available data.
The fact is models informing decision-making are inherently political. More than 70 years ago, the American researcher and health care philanthropist Charles Franklin Kettering observed that we must all be concerned about the future, because that is where we will be spending the rest of our lives. These high stakes explain why the science of prediction often becomes subject to controversy. We may be going into the future together, but we don’t always agree on where we’d like to end up, let alone the best route to take to get there. In democracies, we settle such disagreements through politics.
Without a doubt, the most successful application of scientific models to predict the future and inform decision-making is in weather forecasting. Of course, even the weather is not beyond politicization. Yet when the president used a Sharpie to alter a hurricane landfall forecast map to justify his earlier misstatement, few were fooled (if anyone). That such peccadillos should be so rare underscores the field’s success: The science of weather forecasting has proven to be widely trusted and effectively employed in a wide range of contexts, with little in the way of partisan or political disputes.
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Coronavirus forecasting, in contrast, has been drawn into a partisan divide over risk, behavior, and policy responses. Given President Donald Trump’s consistent efforts to downplay the threat posed by the coronavirus in January, February, and into March, it was easy to predict that the epidemiological models would become embroiled in the political debate. On April 8, Representative Chip Roy, a Republican from Texas, and a group of Republican colleagues sent a letter requesting congressional hearings on the “modeling information related to the coronavirus response efforts,” so as to ensure that “we are not making decisions based upon potentially flawed or misrepresentative information.”
The request by Roy and his colleagues in Congress reveals a huge difference between weather forecasting and the predictions being used in the pandemic. Weather forecasting has been performed by a US government agency since 1870, and is routinely evaluated, with predictions, underlying data, and methods always readily available. Pandemic forecasting has so far had no such home in the federal bureaucracy. According to computational epidemiologist Caitlin Rivers and colleagues at the Johns Hopkins Center for Health Security, the government is instead relying on “expert surge capacity” in academia to help formulate its policy responses.
In practice, this has meant that the federal government and US states have variously chosen, contracted, and created models in an ad hoc and opaque manner. Rivers, who has called for the creation of a “national infectious disease forecasting center” in the mold of the National Weather Service, observes that “Right now, there’s nobody responsible for really recording and archiving who said what—what did modelers say, and what happened. What prediction did they make today, and how does it change tomorrow, how does it change the next day, and how does that relate to what actually happened?”
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Weather modeling and epidemiological modeling have some important differences. One is that weather forecasts are made many times every day, for every location in the country. This allows for the rigorous evaluation of forecast skill. Pandemic forecasts are made rarely (thank goodness), meaning that we can’t know that much about their accuracy, even when we have to rely on them. Another important difference is that weather forecasts don’t change the weather, while forecasts of disease outbreaks may influence how people respond and behave, and thus alter the conditions being forecasted.
For these reasons, pandemic forecasting presents a much greater challenge than does weather forecasting. This is why we really don’t want to ever have to rely on pandemic forecasts. It will always be much better to stop a pandemic before it starts, which requires effective surveillance and strategies for quick intervention. That fact only makes the absence of the federal government in producing or evaluating coronavirus models more alarming.
In its place, we have a free-for-all. The plethora of pandemic models looks like a big bowl of cherries to political partisans, who can pick and choose whichever results seem most supportive of their favored policies or damaging to their opponents’ positions. We have seen similar dynamics in the climate debate, where scientific arguments can be just Trojan horses for views grounded in politics, economics, or culture.
Most notably, on April 1, Trump and the White House Coronavirus Task Force showed a figure indicating that successful social distancing would limit the number of US deaths to between 100,000 and 240,000. Those numbers have, in turn, been laid out by the administration as the metric of policy success in the pandemic: Any five-digit body count, no matter how high, shall thus be counted as salvation. Never mind the fact that these estimates have been widely criticized by experts as being unrealistically high in the first place, including by Trump’s own advisers.
In this instance, the White House’s use of coronavirus forecasts appears to be for purposes of evading accountability for poor decisions or justifying decisions already made, in addition to whatever role they had informing policy. Unsurprisingly, the White House has not released details on its projections; and task force member Deborah Birx only referred, in presenting them, to “five or six international and domestic modelers from Harvard, from Columbia, from Northeastern, from Imperial [College London].” The model from the University of Washington also has been frequently cited by the task force, but another with less aggressive projections, which would be far less favorable to evaluating the White House response, was dismissed as an outlier.
The almost complete lack of transparency from the White House is like gasoline poured on a hot fire of politicized science. As a consequence, both supporters and critics of the Trump administration’s policies cite bits and pieces of research to support their arguments, but the absence of a broader scientific context for interpreting the pandemic forecasts means that everyone lacks a rigorous, authoritative basis for their views. This is convenient for political battles, but fatal to effective policy development or evaluation.
It is true that the widely cited University of Washington model has been shown to produce deeply flawed projections. Of course, we should expect that any newly developed and untested model deployed in a completely novel context will produce poor forecasts. To expect otherwise is to misunderstand the difficulty of such modeling. That is precisely why it is important to compare side-by-side forecasts from all available models. Looking at a diversity of models can help us to characterize areas of agreement and uncertainty.
But models have been pushed out into the ether without this important context. As one group of medical researchers explained, “Major policy decisions need model input, but models are valuable only to the extent that outputs are transparent, are valid, are based on accurate documented sources, are rigorously evaluated, and yield robust and reliable projections.”
The pandemic will be with us for a while, and we may experience multiple waves of outbreaks. That means it is not too late to get our house in order on this issue.
A top priority is to create a clearinghouse of models and their forecasts, especially those being used by the federal government and across the states to inform decisions about pandemic restrictions. This would support both their contextualized use as well as rigorous evaluation of both the forecasts and policy actions as events unfold.
Plus: What it means to “flatten the curve,” and everything else you need to know about the coronavirus.
Models feed on data, and their projections depend on accurate numbers. So a parallel top priority is to create an open-source database of the most crucial information. The good news is that epidemiological modeling is a well-developed science and the requirements for effective modeling are well understood. For instance, in 2018 an advisory committee to the British government published a list of data needed to support real-time epidemiological modeling.
Who should create such a clearinghouse? The obvious answer, following the recommendations of Rivers and colleagues at Johns Hopkins, and broader experience, is the US government. However, the Centers for Disease Control and Prevention has been notably silent during the pandemic, despite being a rare institution that is widely trusted by the public with no partisan slant.
In February, the CDC did present its internal forecasts for the coronavirus outbreak to government officials, but these were never made public. Back in 2014, the agency developed a public outbreak model in support of decision-making related to Ebola. For reasons that may only become known in the fullness of time, the CDC does not now appear to be a candidate for coordinating a clearinghouse of coronavirus modeling and data.
Another candidate would be the National Academy of Sciences, which has created a coronavirus expert advisory committee at the request of the White House Office of Science and Technology Policy and the Department of Health and Human Services. Yet like the CDC, this committee has said little and appears to have been infrequently called upon. At some point, we will learn if the Trump administration’s reluctance to using its experts reflects an aversion to inconvenient science, incompetent leadership, or both.
Meantime, it appears certain that in the near term any pandemic modeling and data clearinghouse will have to come from outside the government, perhaps led by an organization such as the Bill & Melinda Gates Foundation. A group of academics led by Nicholas Reich at the University of Massachusetts has initiated such an effort, but not at the pace or scale that is needed. It is not the first time during the pandemic that institutions beyond the federal government have stepped in to fill a leadership vacuum.
Pandemic modeling is crucially important to inform ongoing policy responses. But right now, this area of science sits on a precipice. We can collectively take steps to ensure that we have transparent science supported by robust data, to inform the many decisions that will have to be made in the coming months and perhaps years. Or else we can continue to allow pandemic science to be used for scoring points in partisan battles. We are all going into the future together, so let’s choose to do so wisely.
Photographs: George Gojkovich/Getty Images; Drew Angerer/Getty Images; Library of Congress
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