Abstract

Due to the particularities of SARS-CoV-2, public health policies have played a crucial role in the control of the COVID-19 pandemic. Epidemiological parameters for assessing the stage of the outbreak, such as the Effective Reproduction Number (Rt), are not always straightforward to calculate, raising barriers between the scientific community and non-scientific decision-making actors. The combination of estimators of Rt with elaborated Machine Learning-based forecasting techniques provides a way to support decision-making when assessing governmental plans of action. In this work, we develop forecast models applying logistic growth strategies and auto-regression techniques based on Auto-Regressive Integrated Moving Average (ARIMA) models for each country that records information about the COVID-19 outbreak. Using the forecast for the main variables of the outbreak, namely the number of infected (I), recovered (R), and dead (D) individuals, we provide a real-time estimation of Rt and its temporal evolution within a timeframe. With such models, we evaluate Rt trends at the continental and country levels, providing a clear picture of the effect governmental actions have had on the spread. We expect this methodology of combining forecast models for raw data to calculate Rt to serve as valuable input to support decision-making related to controlling the spread of SARS-CoV-2.