To answer this question, letâ€™s run some SEIR simulations of a tiny community of 108 people split among 54 households (2 people per household). Everyone is social distancing, but households must periodically make trips to the store as they run out of supplies.
Some households (marked 1x
) have a 1x shopper rule that only one person goes
shopping, and other households (marked as 2x
), both people will go out to
shop and stick together in the store. All parameters at this footnote^{1}.
You can configure the percentage of 2x shopper households and observe how that affects disease spread.
Running this simulation tens of thousands of times, we see that a 1% increase in 2x-shopper households leads to a ~0.96% increase in people infected at the end of the simulations.
What happens if a 1x households sends out a second shopper?
Each additional shopper infects 1.9 more people:
1/54
increase in the number of 2x households0.96
% increase in infected people108
total people(1/54) * 0.96 * 108 = 1.92
The intuition behind this is that the additional shopper increases the chances that they get infected, increasing the chances their household gets infected, increasing the chances that someone in their household infects someone else, and so on, setting off an infection chain reaction. In epidemiology terms, additional shoppers increase the effective reproduction number.
Our decisions absolutely matter and matter beyond ourselves.
How does imposing a 1x-shopper rule in my household affect the risk of someone in my household getting the disease?
More details about this chart at this footnote^{2}.
Our decisions do not exist in a vacuum, and our rate of infection depends on others within our community. What is interesting here is that our decisions matter more when our community is more at risk - the gap between household infection rates increases with the percentage of 2x-shopper households community.
Safer decisions matter more when our community is more at risk.
The more people we see not taking precautions, the more we need take precautions ourselves.
The best way to combat this virus is to make data-driven policies and decisions. High quality simulations offer a fast and safe way to estimate the risk of our actions.
That being said, the virus modeled above is not Covid-19 and the tiny community does not capture real human behavior. We order delivery services, maintain distance in stores, self-quarantine if we are sick, and so on. Researchers are discovering more about Covid-19 every day, about how it spreads, symptoms it produces, how to treat it, and so on.
Future work will focus on incorporating the latest research and simulating more realistic human
behavior. These simulations are all open source.
You can reach out to me privately at covid-contact@simrnd.com
or on Twitter.
Please share these simulations if you found them informative - as the above data shows, we all need to work together to control the spread of the virus.
I built some #coronavirus simulations, exploring how the way we shop affects the infection rate. Check out the 60fps simulations at https://t.co/Qa2evarhM4. pic.twitter.com/fpGH25QzGM
— Jin Pan (@JinPan20) May 16, 2020
For more simulations, check out
Simulation parameters:
Infection by Household Type vs % 2x Shopper Households chart notes
25%
label represents the 25th percentile of infections among 1x shopper households. The 75%
label
represents the 75th percentile, and the 50%
label represents the median.