COVID: To go or stay? A guide toward assessing risk

As COVID reached my family’s city, the US State Department issued dire warnings to its citizens and we saw dozens and dozens of ex-pats flee our city. We faced the agonizing decision whether to leave our home, family, and friends, or to stay. You may face that same decision–perhaps returning to your home country, perhaps going to a different part of the country. Such a decision is intensely personal and depends on a multitude of factors that are hard to sort through–made even harder by fear or forced pressure of making a decision before exit routes are cut off.

I know that fear and concern that you face and I want to give you a 4-part paradigm that can help you more comprehensively assess your risk–and in so doing, explain part of why we stayed even as many others fled.

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A basic paradigm of risk

Most commonly, we think of risk as containing two factors: the likelihood of something occurring and the consequences of it occurring. A kidnapping has significant consequences but may be low risk while a hurricane may have higher consequences and higher likelihood. This is a helpful paradigm, but doesn’t go far enough in helping you comprehensively evaluate your risk.

A more comprehensive paradigm

Instead, we need to think of risk as containing 4 factors and 4 axis: the likelihood of something occurring, the consequences of it occurring, the geographic proximity to danger, and the ethnographic similarity of the danger. Geographic proximity assesses how closer the danger is to you while ethnographic similarity considers whether the danger is happening to people similar to or different from you;

Fore example, there may be many lots of kidnappings in your country, but your overall risk is dramatically different if they exclusively kidnap Asian ex-pats (ethnographic) in your neighborhood (geographic) than if they kidnap mostly black government officials (ethnographic) in a city an hour away (geographic). Charting your risk across all 4 axis helps you more comprehensively assess your risk.

COVID case example

Part 1: The risk of staying

Here’s how we evaluated our risk of staying in our city as COVID came:

Consequences: Since my family is relatively young, the early data suggested that death was unlikely and recovery quite possible. However, we have an infant and, at that point, not many babies had contracted COVID and so the risk to our infant was relatively unknown. We considered the potential consequences fairly severe, 6/10, and only that low because it seemed that i

Likelihood: There were not many cases in our city, all known cases were quarantined, and we had not been to the locations where the individuals had traveled. Further, we had a one-month baby and so hadn’t gone out much as we recovered and so our risk was quite low. Also, we had sufficient food and water to hunker down, dramatically reducing our risk–so we assessed likelihood as fairly low, 3/10.

Geographic proximity: Our city was far away from the worst-hit cities, is a smaller town in our country, and our city immediately banned travelers from outside the city. However, COVID was in our city, no question, and it was unclear if human-to-human transmission had occurred in our city. Geographic proximity thus was 4/10

Ethnographic similarity: COVID doesn’t care if you’re American or Asian, though we considered that we could receive less quality care in a hospital if we were infected, not due to racism but due to our linguistic limitations. Still, relatively low risk: 1/10

Altogether, then, this chart shows how we assessed our overall risk from staying in our city as COVID broke out. Out of a total of 400 “points” in the chart, we registered just 40. Now, this isn’t a scientific number as each number was reached somewhat subjectively, but it is helpful to see the overall risk, especially when we were comparing the risk of staying with the risk of going.

Part 2: The risk of returning to the US (back in January)

When we assessed the risk of returning to the US at the end of January, this is what we saw:

Consequences: Perhaps slightly less of a hassle to get COVID in the US, but still generally the same risk. 6/10

Likelihood: My parents house is in a tiny rural village where their nearest neighbor is 1,000 feet away, so we felt the risk there was quite low: 1/10.

Geographic proximity: At that time, there were only one or two cases in the US, the US had started banning travelers from hard-hit countries, and there were no cases in my parent’s town. However, a flu pandemic can spread easily, so we assessed risk at 3/10.

Ethnographic proximity: COVID doesn’t care your ethnicity or socioeconomic status, but we have the linguistic skill to get better treatment in the US than in our city, so 0/10.

So, overall, that’s a risk threshold of just 9/400–and thus dramatically lower than staying where we were. That’s likely why many ex-pats fled.

We were strongly committed to staying for non-risk reasons (this is home!), but we there is a point where the risk of staying, however will it is motivated, is so much higher than the risk of going that the wise thing to do is leave. So why did we stay?

Part 3: The risk of flying to the US

If we could have magically transported from our city to my parents house, that would have been one thing. But traveling from our city to my parents would have meant a trip to the train station, a 2 hour train ride, travel through the destination train station, a taxi from the train station to the airport, a 4-5 hour flight, an airport layover probably 3 hours, a 12-14 hour flight to the US, another 2-3 hour airport layover, and then another 2 hour flight to my parent’s house. Here’s how we assessed that risk:

Consequences: Same. 6/10

Likelihood: Planes do have good air filters, all passengers were temperature-checked, and airpots were cleaned–but we’d still be in shared and sometimes crowded space with tens of thousands of people over the course of 30+ hours. With an infant who can’t be masked and two toddlers who can’t keep their hands off their face for 30+ hours straight. We thought the likelihood high, 6/10.

Geographic proximity: We would have to travel to the train station to a city hit worse than our city, then have a layover in a city hit far worse than our city, and then take the international flight, thus 6/10.

Ethnographic proximity: Half the flight would be in our country, so another slight risk of care in our country. 1/10

So, overall, we assessed the risk of traveling back to the US to be 84.

Now, the numbers aren’t scientific–was the risk of going twice twice as high as the risk of staying? I’m not sure the numbers are that precise because they rely on many assumptions–but was the risk of going higher than staying? Undoubtedly.

Your situation, of course, will be different than ours–your risk of staying, your risk of traveling, and your risk at your destination will all be different–and I hope this paradigm can help you more effectively evaluate all three so that you can make a wise decision informed by reality, not fear.

Of course, because the situation can change in a heartbeat, we still made plans to go even though we intended to stay. As I wrote in my tips to survive a COVID quarantine, making a plan for both scenarios helps you make this decision more rationally. As part of our plan, we talked about our “tipping point” when we would leave and made a plan to protect ourselves traveling if we needed to. Stay tuned to never miss material from The Prepared Expat!

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