Modelling and predicting the spread of COVID-19 cases depending on restriction policy based on mined recommendation rules

Math Biosci Eng. 2021 Mar 24;18(3):2789-2812. doi: 10.3934/mbe.2021142.


This paper is an extended and supplemented version of the paper "Recommendation Rules Mining for Reducing the Spread of COVID-19 Cases", presented by the authors at the 3rd International Conference on Informatics & Data-Driven Medicine in November 2020. The paper examines the impact of government restrictive measures on the spread and effects of COVID-19. The work is devoted to the improvement of recommendation rules based on novel ensemble of machine learning methods such as regression tree and clustering. The dynamics of migration between countries in clusters, and their relationship with the number of confirmed cases and the percentage of deaths caused by COVID-19, were studied on the example of Poland, Italy and Germany. It is shown that there is a clear relationship between the cluster number and the number of new cases of diseases and death. It has also been shown that different countries' policies to prevent the disease, in particular the timing of restrictive measures, correlate with the dynamics of the spread of COVID-19 and the consequences of the disease. For example, the results show a clear proactive tactic of restrictive measures by example of Germany, and catching up on the spread of the disease by example of Italy. A regression tree and guidelines about influence of features on the spreading of COVID-19 and mortality due to this infection have been constructed. The paper predicts the number of deaths due to COVID-19 on a 21-day interval using the obtained guidelines on the example of Sweden. Such forecasting was carried out for two potential government action options: with existing precautionary actions and the same precautionary actions, if they had been taken 20 days earlier (following the example of Germany). The RMSE of the mortality forecast does not exceed 4.2, which shows a good prognostic ability of the developed model. At the same time, the simulation based on the strategy of anticipatory introduction of restrictions gives 2-6% lower values of the forecast of the number of new cases. Thus, the results of this study provide an opportunity to assess the impact of decisions about restrictive measures and predict, simulate the consequences of restrictions policy.

Keywords: COVID-19; classification; clustering; machine learning; prognostic data.