Explaining the Bomb-Like Dynamics of COVID-19 with Modeling and the Implications for Policy

Gary Lin; Alexandra T Strauss; Maxwell Pinz; Diego A Martinez; Katie K Tseng; Emily Schueller; Oliver Gatalo; Yupeng Yang; Simon A Levin; Eili Y Klein

Using Monte Carlo (MC) methods, we were able to generate 400 parameter samples from a prior distribution with acceptance-rejection sampling. In the figure, each set of sampled parameters of three countries, Italy, South Korea, and Spain, were generated which allow us to calculate the 0 , basic reproduction number, and display those values against the symptomatic rate ( ). The probability distribution for 0 and are displayed on the right and top, respectively. The size each point represents the relative inverse sum of square errors for each country, with larger sized points being a better fit. The ellipses encircle 95% of simulation runs for each scenario. The triangular points represent priors for that were bounded (constrained) between 0.01 -0.5, while the circular points represent priors (unconstrained) that were bounded between 0.01 -0.99.