In this post, we’ll juxtapose two articles about the science of coronavirus, one from Dan Sarewitz, who is a scientist who studies the interface between science, technology, and policy, and one a journalist at the WaPo. Plus we’ll also link to an essay by a law professor about Federalism and pandemic responses, and finally go to an article in the journal Science about pandemic modeling. Apologies for the length of this post, but I’ve tried to point you to some interesting takes on the same issue and also relate it to science and our standard TWS policy disputes.
Here are excerpts from Dan’s essay (worth reading in its entirety):
The facts, that is, are being made authoritative not through scientists telling us what to believe about an invisible virus, but by occurrences in the real world, visible for all to see. If a researcher claims that a certain chemical in the environment, such as the glyphosate in Roundup, will cause a certain number of increased cancer deaths per year or that a particular economic policy will lead to a certain number of new jobs, in most cases no one will ever be able to confirm that prediction. Even if the mechanism by which the chemical causes some variety of cancer is clear in lab rats, it is likely to have many plausible causes in humans. Even if the new jobs do appear, the cause might be trade decisions made by other countries, or the expansion of new industries. In the years that might be necessary to test such claims (though usually they cannot be tested), other researchers may come up with entirely new explanations. No wonder scientific and political debates about such matters never seem to end. But for COVID-19, the basic scientific inferences quickly play out—through changing incidence of the disease and its consequences—in ways that allow both scientists and the public to assess the current level of scientific understanding and the facts on the ground.
For many problems at the intersection of science and policy, scientists use mathematical models to make inferences about the future, for time periods ranging from decades to centuries or more: How can new energy technologies best be deployed to reduce greenhouse gas emissions? How will nuclear waste behave in a geological repository over coming millennia? How much will economic productivity increase if more investments are made in research? But such questions always involve enormous uncertainties, and the models used to try to answer them are laden with assumptions about more basic questions that are themselves unanswerable: How will the price of solar panels change in the coming decades? How many centuries will it take for groundwater to corrode the nuclear waste storage vessels? How efficiently do universities create economically valuable knowledge? Different assumptions about these sorts of questions allow models to fuzz the boundary between science and politics by providing competing views of the future, in support of competing political agendas.
While epidemiological models used for predicting the future of COVID-19 are also assumption-laden and highly uncertain, they can be constantly tested and refined based on data that is emerging on a daily basis, to accomplish what everyone agrees must be done. For the most part models are being used to help put boundaries around the range of plausible futures that we face, and we can see different versions of these futures unfold as different countries implement different policies at different speeds. The models are valuable because they allow us to test our assumptions about both the behavior of the virus and the impacts of different policy approaches, in real time. They are not crystal balls deployed to make the case for one preferred future or another, but navigation charts that help us narrow the plausible pathways to the future that we all hope for.
But when it comes to fighting COVID itself, rather than fixing the economy, the combination of shared values and clear chains of causation makes it tough to import second-order political agendas into debates about what actions to take—despite the ongoing and acknowledged uncertainties. Politicians as ideologically distinct as New York’s Mayor Bill de Blasio, a liberal Democrat, and Ohio’s Governor Mike DeWine, a conservative Republican, are implementing essentially equivalent strategies for addressing the pandemic. While President Donald Trump is at the moment threatening to loosen up social distancing rules, his spasmodic approach to pandemic policies isn’t turning out to be significantly different from that of many other national political leaders. For this crisis, the things that unite us are outranking those that divide us; pandering and opportunism, while never absent from politics, are being brought to heel by the pincer combination of shared values and facts on the ground.
Now, let’s take a brief aside to Federalism and the coronavirus response from Dan Farber posted on Legal Planet (also worth reading in its entirety, the Constitution is only one part of the discussion):
These constitutional rules reinforce the statutory and practical reasons why states have been doing so much of the heavy lifting during this viral outbreak. The federal government could do a lot more than it has so far, but its powers are not unbounded. Don’t get me wrong, the role of the federal government in addressing the pandemic is vitally important. The Feds have resources and funding the states can’t match. But the way our system of government is designed, states and cities are inevitably going to be on the front lines.
Finally, let’s check back with the WaPo.. It’w worth reading the whole thing thinking about “what does the author mean by “politicization”? What evidence does the author use to support that claim?
This is why epidemiology exists. Its practitioners use math and scientific principles to understand disease, project its consequences, and figure out ways to survive and overcome it. Their models are not meant to be crystal balls predicting exact numbers or dates. They forecast how diseases will spread under different conditions. And their models allow policymakers to foresee challenges, understand trend lines and make the best decisions for the public good.
But one factor many modelers failed to predict was how politicized their work would become in the era of President Trump, and how that in turn could affect their models.
I don’t find the WaPo’s evidence more convincing that “what do do” has “become politicized” than Sarewitz’s. Some people disagree about models (including modelers, I’m sure) and President Trump issues statements that don’t make sense (same old, same old). I guess having one’s models “politicized” is bad, but models being used in policy is necessary. Which goes back to our old forest discussion about what is the role of elected political leaders (legitimate?) or is “politics” really bad when making decisions? What is the bad part- values of elected officials or only those you happen to disagree with? Or is the bad part of politics only when the decision is solely based on tribal loyalties (party politics) or light or dark money, or ???
If you want to dive into the detail of some of the models without going too far into the weeds, this Science article “Mathematics of life and death: How disease models shape national shutdowns and other pandemic policies” seems to cover it.
Here’s a quote about models from that piece:
Policymakers have relied too heavily on COVID-19 models, says Devi Sridhar, a global health expert at the University of Edinburgh. “I’m not really sure whether the theoretical models will play out in real life.” And it’s dangerous for politicians to trust models that claim to show how a little-studied virus can be kept in check, says Harvard University epidemiologist William Hanage. “It’s like, you’ve decided you’ve got to ride a tiger,” he says, “except you don’t know where the tiger is, how big it is, or how many tigers there actually are.”
Models are at their most useful when they identify something that is not obvious, Kucharski says. One valuable function, he says, was to flag that temperature screening at airports will miss most coronavirus-infected people.
There’s also a lot that models don’t capture. They cannot anticipate, say, the development of a faster, easier test to identify and isolate infected people or an effective antiviral that reduces the need for hospital beds. “That’s the nature of modeling: We put in what we know,” says Ira Longini, a modeler at the University of Florida. Nor do most models factor in the anguish of social distancing, or whether the public obeys orders to stay home. Recent data from Hong Kong and Singapore suggest extreme social distancing is hard to keep up, says Gabriel Leung, a modeler at the University of Hong Kong. Both cities are seeing an uptick in cases that he thinks stem at least in part from “response fatigue.” “We were the poster children because we started early. And we went quite heavy,” Leung says. Now, “It’s 2 months already, and people are really getting very tired.” He thinks both cities may be on the brink of a “major sustained local outbreak”.