TY - CHAP TI - Predictive models to the COVID-19 AU - Bernardo Gois, Francisco Nauber AU - Lima, Alex AU - Santos, Khennedy AU - Oliveira, Ramses AU - Santiago, Valdir AU - Melo, Saulo AU - Costa, Rafael AU - Oliveira, Marcelo AU - Henrique, Francisco das Chagas Douglas Marques AU - Neto, José Xavier AU - Martins Rodrigues Sobrinho, Carlos Roberto AU - Lôbo Marques, João Alexandre T2 - Data Science for COVID-19 A2 - Kose, Utku A2 - Gupta, Deepak A2 - de Albuquerque, Victor Hugo C. A2 - Khanna, Ashish AB - Following the World Health Organization proclaims a pandemic due to a disease that originated in China and advances rapidly across the globe, studies to predict the behavior of epidemics have become increasingly popular, mainly related to COVID-19. The critical point of these studies is to discuss the disease's behavior and the progression of the virus's natural course. However, the prediction of the actual number of infected people has proved to be a difficult task, due to a wide range of factors, such as mass testing, social isolation, underreporting of cases, among others. Therefore, the objective of this work is to understand the behavior of COVID-19 in the state of Ceará to forecast the total number of infected people and to aid in government decisions to control the outbreak of the virus and minimize social impacts and economics caused by the pandemic. So, to understand the behavior of COVID-19, this work discusses some forecast techniques using machine learning, logistic regression, filters, and epidemiologic models. Also, this work brings a new approach to the problem, bringing together data from Ceará with those from China, generating a hybrid dataset, and providing promising results. Finally, this work still compares the different approaches and techniques presented, opening opportunities for future discussions on the topic. The study obtains predictions with R2 score of 0.99 to short-term predictions and 0.93 to long-term predictions. DA - 2021/01/01/ PY - 2021 SP - 1 EP - 24 LA - en PB - Academic Press SN - 978-0-12-824536-1 UR - https://www.sciencedirect.com/science/article/pii/B978012824536100023X Y2 - 2021/05/26/08:26:26 KW - COVID-19 KW - Forecast KW - Holt Winters KW - Kalman filter KW - Machine learning KW - Prophet KW - SEIR ER -