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US finally has plans for the pandemic it now leads in infections, Ars Technica

US finally has plans for the pandemic it now leads in infections, Ars Technica

      Why us? –


Meanwhile, epidemiologists facing pushback for doing science.



China is indicating that the majority of its cases are now due to secondary re-introduction: the virus has largely stopped circulating within the populace, but travelers from countries with unchecked spread are now bringing it back with them.

China is in a position to track new infections because it imposed significant restrictions on its population and then ramped up testing so that new instances of transmission could be identified quickly. Despite having advanced warning of the spread of SARS-CoV-2, the US’s failure to ramp up testing allowed the virus to establish itself in many areas of the country. As a result, many states have been compelled to place severe restrictions on their populace in the hope of limiting the rapidly spreading infections.

The public health goal of these restrictions is partly to reproduce what China claims to have achieved: a combination of limited infections and rapid testing that allow the US to identify and isolate recently infected individuals and trace anyone they interacted with. (It’s also to limit the number of severe cases so they can remain within the capacity of our health care system.)

by this administration, there are indications that’s starting to change. On Thursday, Trump

sent a letter to US governors that contains a rough outline of a plan for containing the virus. The plan will involve extensive testing to identify the state of the virus on a per-county level. Counties will be placed in different risk categories according to the results of that testing, and the governors will be given a set of restrictions that should be put in place to help bring the risk down.

This approach is necessary in the US because the structure of government places authority for health issues in the hands of states. There is simply no mechanism by which the President can directly compel local authorities to implement these sorts of public health restrictions.

Take, for example, the idea that there might be a large number of symptom-free infections. In the model that resulted, this allowed herd immunity to SARS-CoV-2 to be established quickly, and without the strain on the medical system caused by large numbers of people with severe symptoms. Which, of course, sounds great, other than the lack of data that suggests this is what’s happening in the real world. Unfortunately, the Financial Times ran a story on that model without mentioning that significant caveat or asking for outside experts to comment on the issue. That forced the publication backtrack quickly as criticism poured in .

Unfortunately, this situation has been explained poorly by Trump Administration officials. Deborah Birx, who is the coordinator of the Coronavirus Task Force, discussed yesterday how many of the models don’t match the empirical data we’re getting from various countries. It turns out there’s a good reason for that: Harvard epidemiologist Marc Lipsitch says that his team was specifically asked to model dozens of different scenarios by the CDC in order to give a broader picture of what the US might see under different circumstances.

Naturally, this statement was picked up by the press as Birx (cautioning against “inaccurate models” ( though her statement was more a caution about not trusting every single model). Any nuance in Birx’s statement was lost by the time criticisms reached the partisan editorialists at Fox News and the Wall Street Journal. 6469 As infectious disease epidemiologists, biomedical researchers, and health professionals more broadly, we’re fighting a battle against the biggest crisis in decades. But we are also fighting on a second front that we did not anticipate, fighting a battle against misinformation and disinformation in a hyper-partisan environment where our predictions and recommendations about the pandemic response are deeply politicized. Every twist and turn that the pandemic takes is seized upon by one side or other to claim that some fraction of us are incompetent if not outright mendacious.

Researchers are pilloried for updating their beliefs based on new information. In this environment, when unexpected facts come to light — a higher than anticipated [infectivity], for example — they are used to discredit scientists who made correct inferences given the data that they had available at the time.

To understand this pandemic, it’s part of a scientist’s job to produce numbers and projections, even though we know in advance that some of those will be wrong. Being wrong does not mean the models are useless or that the scientists were wrong. And it doesn’t mean that the scientists are confused or know little about what they’re modeling. These numbers are produced because they show the contrast between our actions and doing nothing — or what might happen if the virus turns out to behave differently than it appears to be.

This is a case where misinterpreting why those numbers exist for partisan purposes may directly cost lives within the next few months.


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