Therefore, in modeling Covid-19 and its particular medication-related hospitalisation results, a shift from the knowledge-intensive methods paradigm towards the data-intensive a person is needed. The existing paper is dedicated to the structure of ProME, a data-intensive system for forecasting the Covid-19 and choice making support necessary to mitigate the pandemics effects. The machine happens to be built to address the mentioned difficulties also to enable further relatively easy adaptations into the dynamically switching situation. The device is mainly according to open-source solutions so may be reproduced whenever similar challenges occur.The COVID-19 pandemic outbreak caused numerous negative effects on both the global and national economies. To make usage of effective guidelines to mitigate the negative effect of a pandemic, it is necessary to spot especially vulnerable areas. The objective of this paper is always to rank the EU nations with regards to the level of vulnerability of these economies towards the effect of the pandemic. For this specific purpose, the COVID-19 Economic Vulnerability Index (CEVI) had been built. It replaces the 15-dimensional group of qualities regarding the countries with one aggregate, artificial signal determined for 27 EU user states. When you look at the study multivariate analytical practices Fasciola hepatica , including agglomerative clustering and multi-attribute methods of object evaluation were utilized to analyse the results of the pandemic. The research shows that EU countries have various levels of financial vulnerability to the effect regarding the COVID-19 pandemic. The south European countries (Spain, Croatia, Greece and Italy), where in actuality the tourism industry plays an important role in GDP composition buy NSC 167409 , would be the most delicate. Germany plus the Scandinavian countries proved to be the smallest amount of sensitive to the negative influence of this pandemic. The CEVI may be a significant part associated with the decision help system. It allows the recognition of countries that show better vulnerability towards the financial effect associated with the COVID-19 pandemic and may even help support countries that need assist the many. The suggested index additionally suggests certain areas in the nation’s economic climate that make it more susceptible. The CEVI in combination with various other instruments can be a very helpful tool to improve the economic climate’s strength and help it recuperate faster in the event of a pandemic surprise.At the termination of 2019 a new coronavirus appeared, turning out to be a world pandemic. This new coronavirus is named COVID-19. Different countries managed the pandemic differently and our main focus in this article is on Poland. For much better counteracting and managing the specific situation a model for forecasting the characteristics of the pandemic is necessary. In this essay we provide a model for simulating future infections considering various preventive measures and areas in Poland. We based the design on a two-dimensional mobile automata, with spatial dependencies between areas, various populace and measurements of simulated regions.We present a prototype system, OptiLoc, that is aimed at analysing effects of medical ability limitations under the Covid-19 regime as well as at supplying medical experts and decision manufacturers with surgical treatment relocation plans for a given location in a given time frame. This really is accomplished by first forecasting the demand of medical procedures of different kinds at the selected geographical granularity level and then finding relocations that are optimal based on a well defined objective function that takes into account treatment and relocation costs, under limitations imposed by Covid-19 restrictions and medical center capacities. Allocation programs are visualized in a user friendly web-based application. We illustrate the effectiveness of the evolved system in the information on urological procedures from Poland.Robust method of temporary forecast of Covid-19 epidemic in tiny administrative units (districts) is suggested. By pinpointing similar parts of epidemic evolutions in the past you can obtain short-term forecast of epidemic in given district. Samples of one and two-weeks forecasts for three towns and cities in Poland during third epidemic revolution (March and April 2021) are shown. Difference between epidemic evolutions in third wave and past waves brought on by Covid B.1.1.7 British variant is observed. Proposed algorithm permits one to manage epidemic locally by entering or releasing anti-Covid constraints in categories of small administrative units.The article involves the detection of outliers in rule-based understanding bases containing data on Covid 19 instances. The authors move from the automated generation of a rule-based understanding base from resource information by clustering rules within the knowledge base to enhance inference processes also to finding strange rules enabling the suitable structure of rule groups. The report provides a two-phase procedure, wherein in the first period, we try to find the suitable construction of guideline clusters when there are outlier rules in the understanding base. In the second phase, we identify outliers in the principles utilising the LOF (regional Outlier element) algorithm. Then we get rid of the unusual principles from the database and look if the selected cluster quality measures are answered definitely to the elimination of outliers, which will indicate that the principles were rightly considered outliers. The performed studies confirmed the effectiveness of the LOF algorithm and chosen cluster high quality measures within the framework of detecting atypical rules.