Enlarge /. Deborah Birx, Coronavirus Response Coordinator, speaks during a Coronavirus Task Force press conference.
Bloomberg / Getty Images
On Thursday, the United States passed a grim milestone: it surpassed China as the country with the largest number of confirmed coronavirus cases. The milestone was reached during an apparently growing public tug-of-war between high-ranking Trump administration officials who want the restrictions removed as soon as possible and public health experts who claim that they are clearly still needed for the time being. This tension could be reflected in the implementation of a new plan that is being developed to guide states through their response to the pandemic.
A victim in this struggle: the work of epidemiologists. As these researchers continue to test the effects of different restrictions on the spread of infections, their models necessarily produce different numbers. These differences are now being drawn into the intense political debate over how best to respond to the pandemic.
According to the Johns Hopkins Coronavirus Resource Center, the world has now seen a little less than 560,000 confirmed cases of SARS-CoV-2. Of these, 86,000 (about 15 percent) are in the United States. In China, where the pandemic was triggered, there are almost 82,000 cases, followed by Italy with just over 80,000 cases. With the exception of Iran, all other countries with over 10,000 confirmed cases are Europeans.
China claims that the majority of its cases are now secondary reintroduction: the virus has largely stopped circulating in the population, but travelers from countries with uncontrolled distribution are now bringing it with them.
China has been able to track new infections by imposing significant restrictions on its population and then strengthening the tests so that new transmission cases can be identified quickly. Despite the warning of the spread of SARS-CoV-2, the virus was able to establish itself in many regions of the country because the USA could not start the tests. As a result, many states have been forced to severely restrict their population to limit the rapidly spreading infections.
Part of the goal of these public health restrictions is to reproduce what China is said to have achieved: a combination of limited infections and rapid tests that allow the United States to identify and isolate recently infected people, and to track down everyone with whom they interacted. (It's also about limiting the number of serious cases so that they can remain in the capacity of our health system.)
Although this government has not clearly communicated this goal, there are signs that are gradually changing. On Thursday, Trump sent a letter to the U.S. governors outlining a rough outline of a plan to contain the virus. The plan includes extensive testing to determine the state of the virus at the county level. Counties are divided into different risk categories based on the results of these tests, and governors are given a number of restrictions that should be put in place to reduce risk.
This approach is necessary in the United States because the government structure places state authority over health issues. There is simply no mechanism by which the President can directly force local authorities to implement these types of public health restrictions.
Unfortunately, this structure could undermine this program. Trump has generally tended to downplay the risk posed by SARS-CoV-2 and increase the prospect of returning to normal activities, despite public health experts opposing it, both inside and outside the administration. While this tension is likely to come into play when drafting guidelines for this new policy, some Republican governors have chosen to take an approach that Trump's wishes and have refused to impose restrictions.
Everything is political
While such political decisions are the natural domain of partisan politics, partisanship has now reached a rather unexpected area: epidemiology. A key factor in deciding to severely limit public interactions was the creation of epidemiological models that examined how the virus could spread and what this could mean for our public health systems. These models necessarily require a number of assumptions about a disease about which we have only partial information. However, as we mentioned in our first reporting, the key model has attempted to base these assumptions on empirical data as much as possible.
Of course, this did not prevent epidemiologists – including some of those involved in the previous study – from trying alternative assumptions. These included assumptions that have really little basis in reality, for example the assumption that a very large number of those infected are essentially symptom-free. But it was also about modeling what would happen if countries introduced specific restrictions at various points in the growth curve of their national virus outbreak – something that happens in the real world.
Of course, these different epidemiological models lead to different results – that's the whole point of running multiple models. None of them are necessarily wrong or right, but the hope is that some of them will be useful. The problem arises when these different results are published without this context and armed by partisan politics.
For example, let's take the idea that there could be a large number of symptom-free infections. In the resulting model, herd immunity to SARS-CoV-2 could be established quickly and without the strain on the medical system by a large number of people with severe symptoms. Which sounds great, of course, aside from the lack of data that suggests this is happening in the real world. Unfortunately, the Financial Times published a story about this model without mentioning this significant limitation or asking outside experts to comment on the subject. This quickly forced the release back when criticism came up.
Unfortunately, this situation has been poorly explained by Trump administration officials. Deborah Birx, the coordinator of the Coronavirus Task Force, discussed yesterday how many of the models do not match the empirical data we get from different countries. It turns out there is a good reason for this: Harvard epidemiologist Marc Lipsitch said his team had been specifically asked by the CDC to model dozens of different scenarios to get a more complete picture of what the United States was doing under different conditions Circumstances.
Of course, this statement was picked up by the press as Birx, who warned of "inaccurate models" (although her statement was more of a warning not to trust every single model). Every nuance in Birx’s statement was lost when criticism reached the partisan editors of Fox News and the Wall Street Journal.
All of this prompted epidemiologist Carl Bergstrom to post a long lawsuit on Twitter. We'll quote part of it below:
As epidemiologists for infectious diseases, biomedical researchers and health professionals in a broader sense, we are fighting the biggest crisis in decades. But we are also fighting on a second front, which we did not expect, and we are fighting against misinformation and disinformation in a non-partisan environment where our predictions and recommendations for responding to the pandemic are deeply politicized. Every turn that the pandemic takes is taken by one side or the other to claim that some of us are incompetent, if not lying.
Researchers are denounced because they update their beliefs based on new information. In this environment, unexpected facts – such as higher than expected (infectivity) – are used to discredit scientists who have drawn correct conclusions based on the data available at the time.
To understand this pandemic, it is up to a scientist to produce numbers and projections, although we know in advance that some of them will be wrong. Being wrong doesn't mean that the models are useless or that the scientists were wrong. And that doesn't mean that scientists are confused or know little about what they're modeling. These numbers are generated because they show the contrast between our actions and doing nothing – or what can happen if the virus turns out to behave differently than it seems.
This is a case where misinterpreting why these numbers exist for partisan purposes can directly cost lives in the next few months.