This week saw a spike in COVID-19 cases in the U.S., and with it come the finger-pointing and blame game. First, fingers are pointed at other countries who made the wrong calls and let their cases spike, and possibly exporting the virus here. Then, blame falls on which state governor or mayor took the information seriously or not.
The reality is that each governer or mayor is faced with a very real challenge: the choice of action or waiting when there is very limited, and unreliable data in front of her/him.
I'm reminded of the following diagram, from Chapter 2 of my book Numbers Rule Your World (link). Half of this chapter looks at epidemiologists who monitor disease outbreaks around the country. They are the ones who sound the alarm when there is e-coli in our greens, which frequently lead to wholesale recalls of produce. Every week there are a few cases of disease but most of the time, they don't lead to large-scale outbreaks.
The left column shows what the analyst was looking at up to August or September in different years. In 2005 and 2006, she found a spike in cases. Each week, she had to decide whether to sound the alarm or wait another week. In 2006 (top row), if she didn't sound the alarm by late August, she would have been late to the outbreak, and the media would blame the CDC for underestimating the outbreak.
In 2005 (middle row), if she had sounded the alarm with the spike in September, she would have made a false alarm. The media would criticize the CDC for destroying the produce industry for six months or longer.
The point is, it is a tough call when you have limited data. It's pointless to slam an earlier decision after more data came in. Too much delay always looks bad if things get worse. Overly drastic action always looks bad if the worst did not materialize.
The most scary part of the situation - if you're the decision-maker - is the daily dread. Every day, new data arrive to judge your previous actions. As long as cases continue unabated, you'll be branded a failure. It's a situation that rewards careful interpretation of the data.
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That's the lesson of Chapter 2 of Numbers Rule Your World (link), and it's here globally in an unprecedented scale.
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