As we rush to embrace an “obey the experts” technocracy in the fight against COVID, media and commentators often assume that the epidemiological data justifying proposed draconian policies — such as lockdowns — are clear. But that’s not true. And that should give our policymakers pause before attempting to impose policies that are likely, in some cases, to be popularly resisted.

This cogent point was made recently by the editor of a professional epidemiology journal. From, “Data Versus Truth in the Midst of a Pandemic,” by Jim Rohrer:

Uncounted scientific articles have been published about the pandemic. Missing from all this information and analysis is frank recognition of the uncertainty in the assumptions upon which data analysis and forecasts are based.

State mitigation strategies are based partly on guidance from the Centers for Disease Control and Prevention (CDC) and partly on local politics. The effectiveness of different mitigation strategies is not strongly supported by population-based evidence, yet television news programs constantly bring out ‘experts’ who insist that if we only did this or that, pandemic deaths would have been avoided.

Rohrer points a finger at media outlets use of talking heads to opine about the pandemic who may be unqualified or too impacted by conditions on the ground to see the bigger picture:

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Perhaps the most serious source of bias in the media is reliance on practicing clinicians as experts even if they lack any special expertise in population epidemiology. These experts typically are working in hospitals and they are over-burdened with acute COVID-19 cases. They see the most biased sample possible and this influences their opinions.

Rohrer ends with a call for recognizing uncertainties.

As researchers, we should be concerned with the false and misleading interpretations of the available data that fill the airwaves. Instead of freely admitting uncertainties about the effectiveness of government policies, we hear arguments that the national failure to follow certain policies has caused many deaths.

We do not know to what extent that assertion is true; we cannot say how much the harms of lockdowns counter the benefits. Let me suggest that responsible researchers will challenge the assertions based on weak data by saying we do not yet know how much effect different policies would have had in the US.

This paper may seem arcane, but it is crucial to our judging the wisdom of increasing calls for government-imposed and private-sector-enforced mandates, ranging from masks to the use of contact-tracing apps.

This much seems true: “The experts” need a little more humility in their policy prognostications. Perhaps more important, the media and social platforms need to be less censorious of heterodox opinions and policy advocacy about COVID. Otherwise, crucial democratic deliberation about the advisability of proposed policies will be shortchanged and trust eroded.