Blog ·
COVID-19 super-spreaders - a data-driven analysis (2020)
Public analysis arguing super-spreader events break SEIR models; a physicist's data-driven take.
An April 2020 public analysis (as Dr. János Karancsi) of why most COVID-19 simulations failed: data-driven work across ECDC, testing, WPP age-distribution, and hospital datasets arguing that super-spreader events in vulnerable settings, plus using deaths under age 60 scaled by the infection-fatality rate to estimate true infection levels, explain outliers that SEIR models miss. A demonstration of independent, large-scale data analysis and clear science communication outside the physics domain. Analysis code: https://github.com/jkarancs/CoronavirusModelling