Between March 5 and July 25, 2020, the total number of SARS-CoV-2 confirmed cases in Bosnia and Herzegovina (BH) was 10 090 corresponding to a cumulative incidence rate of 285.7 per 100 000 population. Demographic and clinical information on all the cases along with exposure and contact information was collected using a standardized case report form. In suspected SARS-CoV-2 cases, respiratory specimens were collected and tested by real-time reverse-transcriptase polymerase chain reaction (rRT-PCR) assay. The dynamic of the outbreak was summarized using epidemiological curves, instantaneous reproduction number Rt and interactive choropleth maps for geographical distribution and spread. The rate of hospitalization was 14.0 % (790/5646) in Federation of Bosnia and Herzegovina (FBH) and 6.2 % (267/4299) in Republic of Srpska (RS). The death rate was 2.2% (122/5646) in FBH and 3.6% in the RS (155/4299). After the authorities lifted mandatory quarantine restrictions, the basic reproduction number increased from 1.13 on May, the 20th to 1.72 on May the 31st. The outbreak concerns both entities, Federation of Bosnia and Herzegovina and Republika Srpska, and it is more pronounced in those aged 20-44 years. It is important to develop the communication and emergency plan for the SARS-CoV-2 outbreak in BH, including the mechanisms to allow the ongoing notification and updates at the national level.
Organizations can improve efficiency of process execution through a correct resource allocation, as well as increase income, improve client satisfaction, and so on. This work presents a novel approach for solving problems of resource allocation in business processes which combines process mining, statistical techniques, and metaheuristic algorithms for optimization. In order to get more reliable results of the simulation, in this paper, we use process mining analysis and statistical techniques for building a simulation model. For finding optimal human resource allocation in business processes, we use the improved differential evolution algorithm with population adaptation. Because of the use of a stochastic simulation model, noise appears in the output of the model. The differential evolution algorithm is modified in order to include uncertainty in the fitness function. In the end, validation of the model was done on three different data sets in order to demonstrate the generality of the approach, and the comparison with the standard approach from the literature was done. The results have shown that this novel approach gives solutions which are better than the existing model from literature.
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