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Description: Given your direct and indirect experiences with Covid-19 and its impact on the world overt the past 6 months you are something of an expert on the subject, right?  Well, here is a pop quiz on that subject. Imagine there are a large number of people who regularly interact closely without masks or what is now considered appropriate social distancing, indoors. Assume we start with just one person who is infected with Covid-19 and is contagious. Off the top of your head, how many people will be infected with Covid-19 by the end of one month? Six weeks? Seven weeks? Eight weeks? When you have your estimates in mind (maybe write them down). Have a look down below the picture below to see how your estimates match what epidemiology tells us is the potential reality and then have a look at the article linked below to see why your estimates (or maybe those of folks who are resistant to mask wearing) are off.

Source: Exponential Growth Bias: The numerical error behind Covid-19, David Robson, Future, BBC.

Date: August 12, 2020

Photo Credit:  Image by _Alicja_ from Pixabay

Article Link:

After 1 month just over 1000 people would be infected; after 6 weeks nearly 33,000; after 7 weeks over 130,000 and after 8 weeks over 1 million people would be infected. Infection rates are exponential not linear so when you think just a little bit beyond you and your immediate contacts, well, you should wear a mask and practice social distancing right? We are NOT alone, oh my no we are most certainly now alone and we need to stop acting like we are.

Questions for Discussion:

  1. Why might it be that many people do not do a good job estimating viral infection rates?
  2. How might the errors in estimation noted in your answer to the previous question impact things like mask wearing and social distancing practice adherence?
  3. How might we use the information provided by the linked article to increase conscious commitment to social distancing and mask usage in the general population?

References (Read Further):

Siegel, Ethan (2020) Why ‘Exponential Growth’ Is So Scary For The Covid-19 Coronavirus, Forbes, Link

Lammers, J., Crusius, J., & Gast, A. (2020). Correcting misperceptions of exponential coronavirus growth increases support for social distancing. Proceedings of the National Academy of Sciences, 117(28), 16264-16266. Link

Banerjee, R., Bhattacharya, J., & Majumdar, P. (2020). Exponential-growth prediction bias and compliance with safety measures in the times of COVID-19. arXiv preprint arXiv:2005.01273. Link

Schonger, M., & Sele, D. (2020). How to better communicate exponential growth of infectious diseases. medRxiv. Link

Robson, D. (2019). The intelligence trap: Why smart people make dumb mistakes. WW Norton & Company. Link