CBRR Model for Predicting the Dynamics of the COVID-19 Epidemic in Real Time

Abstract

Because of the lack of reliable information on the spread parameters of COVID-19, there is an increasing demand for new approaches to efficiently predict the dynamics of new virus spread under uncertainty. The study presented in this paper is based on the Case-Based Reasoning method used in statistical analysis, forecasting and decision making in the field of public health and epidemiology. A new mathematical Case-Based Rate Reasoning model (CBRR) has been built for the short-term forecasting of coronavirus spread dynamics under uncertainty. The model allows for predicting future values of the increase in the percentage of new cases for a period of 2–3 weeks. Information on the dynamics of the total number of infected people in previous periods in Italy, Spain, France, and the United Kingdom was used. Simulation results confirmed the possibility of using the proposed approach for constructing short-term forecasts of coronavirus spread dynamics. The main finding of this study is that using the proposed approach for Russia showed that the deviation of the predicted total number of confirmed cases from the actual one was within 0.3%. For the USA, the deviation was 0.23%.

Adapting supply chain operations in anticipation of and during the COVID-19 pandemic

“…Another important research stream has been focused on developing methods for forecasting the pandemic dynamics with consideration of control measures (see e.g., [ 45 , 48 , 49 , 71 , 101 ]). Robust optimization methods have been frequently used in combination with methods based on regression analysis, e.g., using non-parametric regression models like variations of MARS (multivariate adaptive regression splines) [21] for assessing the process dynamics and forecasting.…”

Understanding systemic disruption from the Covid-19-induced semiconductor shortage for the auto industry

“…The idea is to develop capabilities for a fragile supply chain to mitigate the disruptions caused by the pandemic: inventory reservation; back-up and emergency inventory at the distribution center; reserve capacity [86] and multi-level commons [15] have been suggested as some supply chain resilience strategies in the recent Covid motivated supply chain literature. There is also empirical work in identifying the impact of supply chain resilience strategies [e.g [45] , [51] , [52] , [100] ]. Ivanov and Dolgui [41] also discuss the need to adapt supply chains to better prepare against future pandemics with a view to making supply chains more viable in the long-term [40] .…”

Forecasting and XAI for Applications Usage in OS

“…In the currently existing work on the measurement of forecast models, XAI techniques take an alternative view of the results generated using XAI methods (importance of features) that can help users to intuitively understand the results generated by forecast models [17][18][19]. For example, XAI can be applied to medically assisted diagnosis, so that this has important implications for the doctor's diagnosis, prompting black-box models and doctors to make more beneficial decisions for patients [20][21][22][23][24], and XAI can also be applied to automated driving, when automated systems make decisions or recommendations, for practical factors and socio-legal reasons, to users, developers, and regulators is essential to provide explanations. In the face of the rapid development of information technology, there is an increasing interest in machine learning and deep learning, which are called "black box models" because most of the algorithms in machine learning and deep learning are not intuitively understandable [25][26][27][28][29][30].…”