PCovid: a multilevel model for prediction and aggravation of COVID-19 cases optimized for teleconsultations

Telemedicine is one of the smart cities’ main features, being essential to provide proactive health systems for monitoring risks to citizens’ health. During the COVID-19 pandemic, the use of teleconsultation systems was driven by social isolation recommendations and high demand for health services. In particular, during the pre-immunization phase of the disease, predictive models for diagnosing and aggravating COVID-19 were developed to assist in teleconsultation procedures. Models based on easily obtainable data, such as the patient's symptoms and comorbidities, present an alternative. In general, theses models were not integrated with the teleconsultation tools, leading to low adherence to these solutions. In this work, we present the PCovid model for prediction and aggravation of COVID-19 cases, optimized for online teleconsultations. The results demonstrated satisfaction in using the system with embedded-PCovid, its viability to provide better management, resource allocation and service flow control to combat COVID-19