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Using posterior predictive distributions to analyse epidemic models: COVID-19 in Mexico City

Scientists and public health officials have relied on a variety of epidemiological models to forecast the trajectory of the coronavirus pandemic, and to derive guidance for policies aiming to avoid overloading health facilities. All such models contain parameters, and forecasting tools tend to choose these to provide a best fit to available observations.

Modeling the second wave of COVID-19 infections in France and Italy via a stochastic SEIR model

Late in the spring of this year, many nations around the world, especially in Europe, faced public health crises, as coronavirus infections threatened to overwhelm their intensive care facilities. Authorities responded with drastic measures to reduce social contacts, closing businesses and schools, restricting public transport and banning large social events.