Why Predictive Modeling is Critical in the Fight against COVID-19 / ¿Por qué los modelos predictivos son cruciales en la lucha contra la COVID-19?

ABSTRACT

A number of predictive models and forecasting exercises have been developed by various organizations, such as research groups, academic institutions, hospitals, and consulting companies, with the main aim to support health systems in with COVID-19 strategic decision making, planning, and health policy formulation that help in the fight against COVID19. Predictive models are helpful for estimating the number of COVID-19 cases and deaths; the resources required, e.g., such as hospital patient beds and ICU beds; and the demand for supplies, such as personal protective equipment (PPE). Because predictive models for COVID-19 must rely on a rapidly changing situation and underlying data, they produce results that may change repeatedly as data areas data is updated and revised. Nevertheless, the predictive models are meaningful and can offer crucial insights to policymakers. It is important that we understand the strengths and weaknesses of predictive models in order to use them judiciously as support and reference tools for COVID-19 planning and action.

RESUMEN

Los modelos predictivos son útiles para estimar el número de casos y de muertes por la COVID-19; los recursos necesarios, como las camas de hospital y de UCI; y la demanda de suministros, como la de equipos de protección personal (EPP). Dado que los modelos predictivos para la COVID-19 deben basarse en situaciones y datos subyacentes que cambian rápidamente, los resultados que producen pueden cambiar repetidamente a medida que se actualizan y revisan los datos. No obstante, los modelos predictivos tienen interés y pueden aportar perspectivas que son cruciales para los responsables de las políticas. Es importante que conozcamos los puntos fuertes y las limitaciones de los modelos predictivos para usarlos de forma juiciosa como elementos de apoyo y herramientas de referencia para la planificación y la actuación en torno a la COVID-19.