ABSTRACT
The Instituto Mexicano del Seguro Social (IMSS) developed and implemented epidemic monitoring and modeling tools to support the organization and planning of an adequate and timely response to the COVID-19 health emergency. The aim of this article is to describe the methodology and results of the early outbreak detection tool called COVID-19 Alert. An early warning traffic light was developed that uses time series analysis and a Bayesian method of early detection of outbreaks from electronic records on COVID-19 for suspected cases, confirmed cases, disabilities, hospitalizations, and deaths. Through Alerta COVID-19, the beginning of the fifth wave of COVID-19 in the IMSS was detected in a timely manner, three weeks before the official declaration. The proposed method is aimed at generating early warnings before the start of a new wave of COVID-19, monitoring the serious phase of the epidemic, and supporting decision-making within the institution;unlike other tools that have an approach aimed at communicating risks to the community. We can conclude that the Alerta COVID-19 is an agile tool that incorporates robust methods for the early detection of outbreaks. Copyright © 2023 Revista Medica del Instituto Mexicano del Seguro Social.