Artificial intelligence approach toward analysis of COVID-19 development—Personalized and epidemiological model

Although ML has been examined for a variety of epidemiological and clinical concerns, as well as for COVID-19 survival prediction, there is a notable lack of research dealing with ML utilization in predicting disease severity changes during the course of the disease. This chapter encompasses two approaches in predicting COVID-19 spread—personalized model for predicting disease development in infected individual patients and an epidemiological model for predicting disease spread in population. Personalized model uses XGboost for the classification of infected individuals into four different groups based on the values of blood biomarkers analyzed by Gradient boosting regressor and chosen as biomarkers with the highest effect on the classification of COVID-19 patients. The epidemiological model includes two proposed methods—differential equation-based SEIRD model and an LSTM deep learning model. Proposed models can be used as tools useful in the research and control of infectious illnesses and in reducing the burden on the health system. © 2022 Elsevier Inc. All rights reserved.

Epidemiological forecasting of COVID-19 infection using deep learning approach

Since the novel SARS-CoV-2 virus appeared, interest in developing epidemiological mechanisms that would help in prevention of its spread has increased. Epidemiological models are the most important mechanisms for examining the spread of the virus. For that purpose, we propose deep learning approach, LSTM neural network model. LSTM is a special kind of neural network structure capable of learning long-term dependencies in sequence prediction problems. The model was fed with official statistical data available online for Belgium in the period of March 15th, 2020 to March 15th, 2021. Results show that LSTM is capable of predicting in long-term manner with the low values of RMSE and MAE. Higher values of RMSE and MAE are observed in the infected cases (RMSE was 397.23 and MAE was 315.35) which is expected due to thousands of infected people per day in Belgium. In future studies, we will include more phenomena, especially medical intervention and asymptomatic infection, in order to better describe the COVID-19 spread and development.

Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review

COVID-19 is one of the greatest challenges humanity has faced recently, forcing a change in the daily lives of billions of people worldwide. Therefore, many efforts have been made by researchers across the globe in the attempt of determining the models of COVID-19 spread. The objectives of this review are to analyze some of the open-access datasets mostly used in research in the field of COVID-19 regression modeling as well as present current literature based on Artificial Intelligence (AI) methods for regression tasks, like disease spread. Moreover, we discuss the applicability of Machine Learning (ML) and Evolutionary Computing (EC) methods that have focused on regressing epidemiology curves of COVID-19, and provide an overview of the usefulness of existing models in specific areas. An electronic literature search of the various databases was conducted to develop a comprehensive review of the latest AI-based approaches for modeling the spread of COVID-19. Finally, a conclusion is drawn from the observation of reviewed papers that AI-based algorithms have a clear application in COVID-19 epidemiological spread modeling and may be a crucial tool in the combat against coming pandemics.