Covid and the parable of bear island

Imagine you are a member of a large stone age tribe on a large island. The island is inhabited by 6 bears. You go hunting every day. About once a year, you hear that one person was killed by a bear. Yet the people continue to hunt every day.

In April, you hear that 2 bears have swum over from the mainland. The tribe is terrified. It is decided to not go hunting and just gather berries near each cave. In May you hear that one bear swam back to the mainland. Still there is one unfamiliar bear and the people continue to avoid hunting out of fear.

Does these people seem silly to you? They should. And yet this is a pretty good analogy for Covid. For every 6 deaths from all causes among people over 35 in April 2019, there were 8 in April 2020. The extra 2 were from Covid. Now the death rate is lower. For every 6 deaths from all causes in July 2019, there were 7 in July 2020. These ratios are pretty consistent across age groups over 35 (younger people are much less affected by Covid).

So why so much panic about the 7th bear, but not the other 6? You might expect that people accept their mortality and the risks of everyday living. But if that were true, they would quickly accept the 7th bear. Apparently it is not acceptance, but denial. If we live in denial of the 6 bears, a bear swimming over from the mainland could indeed be terrifying.

To get a feel for this effect, it helps to imagine the island contains only people over 85. In that case the island population is only 5. In other words, 1 in 5 people over 85 die each year. That's pretty scary indeed. Yet in normal years, they continue to go about their lives without fear despite the 6 bears.

Not one of us will live forever. If we can come to acceptance that death is part of life, maybe we can avoid futile attempts to escape death at any cost.

Cost effectiveness of Covid-19 response

Shutting down major sectors of the economy was a drastic measure to try and prevent the spread of Covid-19. But was it worth the expense in terms of lives saved? Let's do an admittedly crude comparison. The leading causes of death this year, in rough decreasing order, will be heart disease, cancer, accidents, lung disease, Covid, stroke, and Alzheimers. For a fair comparison, we need to know the expected number of deaths and the cost to society of measures taken to prevent statistical deaths. We do not include individual treatments, as most of those costs are borne by the afflicted families after the fact, or through insurance payments, and are not as relevant for public policy. For most of the leading causes, it is difficult to estimate the costs, but we have a nice metric for cancer and Alzheimers, where we can use annual research and deaths from previous years. For Covid, an accurate measure of lost productivity due to the lockdown would be the loss of GDP. This has been estimated at $15T or more, but it is hard to pin down. We will be extra conservative and use the $2T stimulus for a rough order of magnitude.

Cancer deaths: 600k
Cancer research expense: $50B (private big pharma)
Cancer research expense: $5B (federal research)
Cost per death: $90k

Alzheimer's deaths 120k
Alzheimers research expense $3B
Cost per death: $25k

Covid deaths 145k (to date)
Covid response expense $2T
Cost per death: $13M

Here it is in a chart: