Estimating COVID-19 cases and reproduction number in Mexico
This work addresses the challenge of tracking the COVID-19 epidemic in Mexico, providing improved data for public health decision-making, but it is incremental as it applies an existing modeling approach to a specific regional context.
The authors tackled the problem of estimating COVID-19 infections and reproduction numbers in Mexico by fitting a semi-mechanistic Bayesian hierarchical model to death data, resulting in more accurate estimates compared to using reported case numbers.
In this report we fit a semi-mechanistic Bayesian hierarchical model to describe the Mexican COVID-19 epidemic. We obtain two epidemiological measures: the number of infections and the reproduction number. Estimations are based on death data. Hence, we expect our estimates to be more accurate than the attack rates estimated from the reported number of cases.