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Objectives The purpose of this article is to describe and develop the predictive value of three models during the COVID-19 epidemic in Chile, providing knowledge for decision-making in health. *Autor de correspondencia mcanals@uchile.cl Citación Canals M, Cuadrado C, Canals A. COVID-19 in Chile: The usefulness of simple epidemic models in practice. Medwave 2021;21(01):e8119 Doi 10.5867/medwave.2021.01.8119 Fecha de envío 10/10/2020 Fecha de aceptación 7/1/2021 Fecha de publicación 12/02/2021 Origen No solicitado. Tipo de revisión Con revisión por pares externa, por tres árbitros a doble ciego. Palabras clave COVID, Models, Chile2 / 10 Methods We developed three models during the epidemic: a discrete model to predict the maximum burden on the health system in a short time frame—a basic SEIR (susceptible-exposed-infected-removed) model with discrete equations; a stochastic SEIR model with the Monte Carlo method; and a Gompertz-type model for metropolitan city of Santiago. Results The maximum potential burden model has been useful throughout the monitoring of the epidemic, providing an upper bound for the number of cases, intensive care unit occupancy, and deaths. Deterministic and stochastic SEIR models were very useful in predicting the rise of cases and the peak and onset of case decline; however, they lost utility in the current situation due to the asynchronous recruitment of cases in the regions and the persistence of a strong endemic. The Gompertz model had a better fit in the decline since it best captures the epidemic curve’s asymmetry in Santiago. Conclusions The models have shown great utility in monitoring the epidemic in Chile, with different objectives in different epidemic stages. They have complemented empirical indicators such as reported cases, fatality, deaths, and others, making it possible to predict situations of interest and visualization of the short and long-term local behavior of this pandemic. |
