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%.
Highlights
The prediction of the novel coronavirus COVID-19 dynamics is inevitably associated with the lack of statistics from previous years and the need to adequately use the currently available information on the developing epidemic parameters, for which the degree of uncertainty is extremely high.Many research groups in the USA, China, and Europe are working on the development of effective models and methods for predicting the spread of the new virus in the short term [1,2,3,4,5]
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%
The authors of the study [16] evaluated the performance of a dynamic Bayesian network in infectious disease surveillance
Summary
The prediction of the novel coronavirus COVID-19 dynamics is inevitably associated with the lack of statistics from previous years and the need to adequately use the currently available information on the developing epidemic parameters, for which the degree of uncertainty is extremely high.Many research groups in the USA, China, and Europe are working on the development of effective models and methods for predicting the spread of the new virus in the short term [1,2,3,4,5]. The prediction of the novel coronavirus COVID-19 dynamics is inevitably associated with the lack of statistics from previous years and the need to adequately use the currently available information on the developing epidemic parameters, for which the degree of uncertainty is extremely high. Susceptible-Exposed-Infected-Recovered (SEIR) type [11,12,13] are built based on the mechanisms of virus spread from individual to individual. They use epidemiological parameter estimates of known viruses, which is hardly suitable for modeling a new type of viral epidemic. In the article [17], a dynamic neural network model for predicting the risk of Zika virus in real time is developed.
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