CuraZone: The tool to care for populated areas[Formula presented]

Abstract

The COVID-19 pandemic highlighted the inadequate readiness of numerous nations to address diseases that could potentially evolve into epidemics or pandemics, posing risks to health systems and supply chains. Statistical analysis and predictive models were developed to manage COVID-19 and other diseases that harm public health. However, few public-policy decision-support tools are documented in the literature, although several governments have created them. In line with the previous developments, this tool uses socioeconomic features to model the COVID-19 province's mortality rates. This paper presents a tool to predict the mortality rate of a province using supervised learning techniques, named CuraZone. This tool was validated using 196 provinces in Peru for training and considering 31 characteristics. The tool displays the dataset's most essential characteristics, shows the country's mean square error (MSE), and predicts a province's mortality rate. In addition, the tool contributes to the field of Explainable AI (XAI), as it shows the importance of each feature. Highlighted contributions of this work include the support for the decision-making of governments or stakeholders in epidemics, providing the source code in an open and reproducible way, and the estimated mortality rate for specific populations of a neighborhood, city, or country.