The COVID-19 pandemic is an example of complexity in action. From transmission networks to robustness to market collapse, scientists who study complex systems are hard at work both monitoring and modeling the epidemic, and also seeking to project the socio-economic impact of the disease, and plot paths to recovery.
To share some of their insights from the world of complexity science, researchers are posting short, blog-style transmissions.
Recent posts address some common misconceptions around transmission and testing, such as:
- Why, if you test positive for COVID-19 and the test is 90% accurate, that does NOT mean you are 90% likely to have the disease. (David Wolpert on 'Why good math can't fix bad data')
- How our current approach to testing can create a "false" flat curve. (Van Savage on 'The informational pitfalls of selective testing')
- How surprisingly large outbreaks can result from even a "subcritical" infection rate (R0<1). (Cristopher Moore on 'The heavy tail of outbreaks')
For the full series, visit santafe.edu/COVID19. New posts and new experts are added each Monday.
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