Application of big data analytics in the COVID-19 pandemic: Selected problems

The coronavirus disease 2019 (COVID-19) pandemic enabled many governments around the globe to test and apply big data-based tracing technologies and various big data-driven tools to curb and monitor the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. The regular procedures of data and privacy protection were partially sacrificed to fight the pandemic. For the public health safety, all incidents of COVID-19 are considered of being hazardous, uncertain, and sudden. If a government can continuously and efficiently collect big data from various sources and apply suitable and efficient analytical methods, it might instantly respond to the public health threats by executing optimal decisions to slow the spread of the pandemic and for a fast return to normality. A specific framework is presented as a multidimensional recommendation for the efficient utilization of big data analytical technologies to control and prevent pandemics and epidemics. The recommendations and challenges with regard to employing big data for combatting COVID-19 are being discussed along with the background information. © 2023 Elsevier Inc. All rights reserved.

Three Mathematical Models for COVID-19 Prediction

The COVID-19 outbreak was a major event that greatly impacted the economy and the health systems around the world. Understanding the behavior of the virus and being able to perform long-term and short-term future predictions of the daily new cases is a working field for machine learning methods and mathematical models. This paper compares Verhulst's, Gompertz´s, and SIR models from the point of view of their efficiency to describe the behavior of COVID-19 in Spain. These mathematical models are used to predict the future of the pandemic by first solving the corresponding inverse problems to identify the model parameters in each wave separately, using as observed data the daily cases in the past. The posterior distributions of the model parameters are then inferred via the Metropolis–Hastings algorithm, comparing the robustness of each prediction model and making different representations to visualize the results obtained concerning the posterior distribution of the model parameters and their predictions. The knowledge acquired is used to perform predictions about the evolution of both the daily number of infected cases and the total number of cases during each wave. As a main conclusion, predictive models are incomplete without a corresponding uncertainty analysis of the corresponding inverse problem. The invariance of the output (posterior prediction) with respect to the forward predictive model that is used shows that the methodology shown in this paper can be used to adopt decisions in real practice (public health). © 2023 by the authors.