On sourcing credible pandemic news
I responded to a question on Facebook with a much longer answer than the person was likely expecting, but I think it's worth reposting: Where is a good place to get credible news? Seems like all we see, hear, or read is conflicting.
Me, just now, after realizing I'd spent nearly twenty minutes hammering out an answer: "I really should capture this and post it on my own website." So here we are! Suggestions welcome.
Oh, hey, a topic I feel strongly about. First up, my answer about general news.
Take a look at this chart: https://www.adfontesmedia.com/static-mbc/ It ranks news sources on a couple of different axes: left/centrist/right, and fact-based/fabrication-based. While I can't speak to all of the items on this chart, I've found that a synthesis of Reuters + NYT + Washington Post + Economist + NPR gives me a clear centrist perception of the issues.
Next: ask yourself a question about the sources of news you're consuming. Is its revenue model based on clicks and views, or does it have some element of subscription involved? News websites with revenue models based on clicks and views have every incentive to publish news canted in a manner to encourage more reading, more clicking, more outrage, more sharing, more social media outrage. What this tells you, in blunt terms, is whether you are the consumer, or the product. If your clicks and views are what are being bought and sold, get another news source.
What this comes down to: identify solid, measured, thoughtful reporting, and pay for it. Outrage fodder is cheap to write. Journalism with fact-checking and editorial oversight is expensive. (I speak as a technical editor. I am, bluntly, expensive.)
Next up: coronavirus-centric news. In broad terms, my eyeballs and time are put toward people who have spent their professional careers studying some aspect of the fields of virology or epidemiology. Their professional goals are to advance the studies in their field, and to acknowledge when previous best guesses were incorrect. Those goals are frequently in conflict with political goals.
My husband and I were in Europe when the pandemic spun up, and we had to make an informed judgment call on whether to stay in Europe (high immediate cost, but functioning health care systems) or attempt to evacuate home to the states (lower immediate cost, but a hellish health care system). I started reading a LOT about epidemiology. (See our decision to still travel, some thoughts on risk assessment, and when we decided it was time to come home.)
What you want to listen for is subtle: scientific mindset and an openness to uncertainty. Scientists generally fare poorly in the political spotlight because politicians prefer all-or-nothing statements, while science - especially epidemiology in a fast-moving outbreak! - rarely provides an all-or-nothing statement. In science, theories hold until they are disproven by evidence, and then they are discarded in favor of better theories. Sometimes theories turn out to be wrong. Scientists acknowledge this readily and without shame. Politicians do not. FiveThirtyEight provides a link to current predictions from different models. (https://projects.fivethirtyeight.com/covid-forecasts/) Models take different approaches to predicting viral spread based on their assumptions of how humans will behave. Familiarize yourself with the major models that are out there, ask how their predictions have fared (and changed!) over time.
Learn what R0 (R-naught) and Rt mean. People talking about those are generally more focused on the science.
Some people that I've found useful to keep an eye on:
- Trevor Bedford - https://twitter.com/trvrb/ - was one of the virologists at the Seattle Flu Study who started sequencing the DNA of the virus at the beginning of the outbreak, and tried to sound the alarm for containment.
- Helen Branswell - https://twitter.com/HelenBranswell - a reporter covering infectious diseases for StatNews. She's publishing thoughtful, science-minded updates (she's covered Ebola multiple times in the past) and is good about presenting uncertainty fairly.
- Youyang Gu - https://twitter.com/youyanggu - a data scientist behind one of the models (the Youyang Gu model, heh) that's done pretty well in predicting spread.
- [A qualified recommendation of] Ed Yong - https://twitter.com/edyong209 - a science writer for The Atlantic. I've been following him for a few days, so I can't speak to his long-term trajectory of information, but what I've seen so far looks right and sane.
Also: https://www.covidexitstrategy.org/ and https://covidactnow.org/ are good for seeing several different types of data, pulled together from all states, and presented in clear, comprehensible formats.
The tl;dr: ask yourself if you're the consumer, or the product. Familiarize yourself with the scientific terms used to describe an epidemic. (R0, or R-nought, is INCREDIBLY important.) Look for journalism that focuses on science.
And … here's the thing. The reason it's hard is because this epidemiology is a tough subject. In science, multivariant problems are hardest to solve, because there are multiple factors at play. Not only are we dealing with a new-and-unknown entity (a virus) but we're also dealing with human behavior and political directives. The virus has traits that are becoming understood (like the length of the incubation period) but predicting how the populace will help or hinder the spread is tougher. The reading that's real and informative and helpful will not be the splashiest stuff on a news site. It will have data, and acknowledgment of uncertainty.
Andy pointed out something I thought worth dropping in as an addendum:
There's one factor that I think is worth bringing out clearly whenever we talk about this: there is so much that we just don't know about this virus.
All the experts can reasonably do is make recommendations and decisions based on what we know to be true and what we think is probably true. Some of those assumptions are going to be wrong -- as you point out, that's the nature of science, and adapting to changing information is a sign of maturity as well as of a good scientist.
Beware black-and-white, all-or-nothing assertions. The epidemic isn't that simple. If you see a headline that makes you outraged, consider that a warning flag -- think carefully about who's writing it, why, and what they're trying to convey and accomplish.