Trump’s fight over Covid-19 numbers shows how the hollowing out of expertise can be dangerous for democracy

As in any emergency or disaster, institutional agreement over the statistics of the Covid-19 pandemic is incredibly important. During the crisis, President Trump has questioned federally requested research around the spread of the pandemic and the amount of equipment needed to tackle it. Philip Rocco writes on how Trump’s efforts to undermine a common understanding of the numbers around the crisis can be a threat to democracy itself.

White House Press Briefing. Picture:  The White House / Public Domain.

240,000. That’s the number of people the White House claims could die from Covid-19.

Where that number came from, however, no one seems to know. White House officials apparently have refused to discuss their methodology for arriving at it. Epidemiologists, including those whose research the Trump administration used, are also apparently mystified by the estimate.

The administration produced these projections after a week of numerical conflict on coronavirus. In an interview on Sean Hannity’s Fox News program, President Donald Trump got into the weeds on public health infrastructure. Referring to an emergency request from New York, Trump said: ‘I don’t believe you need 40,000 or 30,000 ventilators. You know, you go into major hospitals sometimes and they’ll have two ventilators. Now all of a sudden they’re saying, “Can we order 30,000 ventilators?”’

It is at this point that observers of the Trump administration might be experiencing some deja vu. The last four years have been defined, as I argue in a new edited volume, by conflict over the official statistics that help to constitute political reality. Trump has called unemployment rates published by the Bureau of Labor Statistics ‘totally fiction’. In 2018, he falsely suggested that ‘3,000 people did not die’ from two hurricanes that hit Puerto Rico.

Observers of the Trump administration have treated these rhetorical acts as the product of ignorance, a dangerous disdain for truth, or some form of personality disorder. Yet, however alluring, these rationalisations fail to capture broader historical developments that have led to conflict over the numbers federal officials used to make public policy.

Because pandemics are complex and uncertain events, health officials often leverage images or phrases to facilitate public understanding. In recent weeks, the most prominent of these devices has been the goal of ‘flattening the curve’ of infections, a phrase accompanied by a simple visual representation. In practice, however, it is difficult to know whether we have reached the peak rate of infection. Moreover, the image of a single curve conceals the subtler underlying truth: there are multiple curves which may peak at different times in different regions. Because of the weak testing regime in the United States, most of this movement will only be visible retrospectively.

Such uncertainty makes it imperative for governments to preserve their capacity to explain what is happening in real time. This is precisely why epidemiologists employ models that forecast how diseases might spread under sets of conditions. These models make a variety of assumptions about the policies governments will enact and the way that human beings will respond to those policies. Rather than projecting a single image of the future, their purpose is to help policymakers consider the consequences of various actions they might take.

Yet while disease models always contain conditional predictions, the fragmented character of knowledge production in the United States may be allowing White House officials to choose estimates that are politically expedient. According to Deborah Birx – coordinator of the president’s coronavirus task force – officials reviewed the work of 12 models developed by teams of researchers at universities around the country. The team appears to have leaned most heavily, however, on the projections of one research group at the University of Washington’s Institute for Health Metrics and Evaluation (IHME). That model assumes that every state will  impose stay at home orders (two have yet to do this) until the summer. Nevertheless, Trump’s own restrictions extend only until April 30. The IHME model also fits the curve of deaths from China to emerging death data in American cities and counties. Again, this would assume a far more aggressive approaching to testing and treatment than the one currently taken in the United States.

The high level of political control over public numbers is not merely an artefact of the Trump administration. As my research shows, Trump has accelerated a pre-existing trend of hollowing out federal research capacity across the whole of government. Even before Trump took office, relationships between federal agencies and outside researchers for disease modelling developed on an ad hoc basis. In 2017, a federal policy change restricted funding for public health response within the NIH’s Modelling Infectious Disease Agent Study Network. This environment has further strained relations between academic modellers and public health officials, and has prompted some to call for the creation of a new national centre for disease forecasting, which would help to further integrate and consolidate knowledge about outbreaks like Covid-19.

Trump has not always been successful in his efforts to reduce the ability of federal statistical agencies to analyse problems like Covid-19. Congressional patrons of scientific capacity have been essential in preserving the integrity of statistical agencies and policy-analysis units. Nevertheless, the fragmented and ad hoc information regime creates an atmosphere of uncertainty and ambiguity which make it harder for government to respond to crises. Crises themselves can exacerbate the problem. As a result of Covid–19, key social and economic surveys conducted by federal agencies could be interrupted for the first time in 50 years.

The survival of complex democracies depends in part on our ability to establish a stable, common understanding of extreme events. In the absence of this, threats to public health can become threats to democracy itself.

This article gives the views of the author, and not the position of Democratic Audit. It was first published on LSE USAPP – American Politics and Policy blog.

About the author

Philip Rocco (@philiprocco) is an assistant professor in the Department of Political Science at Marquette University. He is the co-author of Obamacare Wars: Federalism, State Politics, and the Affordable Care Act (University Press of Kansas, 2018) and the co-editor of American Political Development and the Trump Presidency (University of Pennsylvania Press, 2020). His work has been featured in, among other venues, Journal of Health Politics, Policy, and Law, Political Science Quarterly, Publius: The Journal of Federalism, Journal of Aging and Social Policy, and Policy Studies Journal.

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