@incollection{bernardo_gois_predictive_2021, title = {Predictive models to the {COVID}-19}, isbn = {978-0-12-824536-1}, url = {https://www.sciencedirect.com/science/article/pii/B978012824536100023X}, abstract = {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.}, language = {en}, urldate = {2021-05-26}, booktitle = {Data {Science} for {COVID}-19}, publisher = {Academic Press}, author = {Bernardo Gois, Francisco Nauber and Lima, Alex and Santos, Khennedy and Oliveira, Ramses and Santiago, Valdir and Melo, Saulo and Costa, Rafael and Oliveira, Marcelo and Henrique, Francisco das Chagas Douglas Marques and Neto, José Xavier and Martins Rodrigues Sobrinho, Carlos Roberto and Lôbo Marques, João Alexandre}, editor = {Kose, Utku and Gupta, Deepak and de Albuquerque, Victor Hugo C. and Khanna, Ashish}, month = jan, year = {2021}, doi = {10.1016/B978-0-12-824536-1.00023-X}, keywords = {COVID-19, Forecast, Holt Winters, Kalman filter, Machine learning, Prophet, SEIR}, pages = {1--24}, }