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COVID-19 has become the largest pandemic in recent history to sweep the world. This study is devoted to developing and investigating three models of the COVID-19 epidemic process based on statistical machine learning and the evaluation of the results of their forecasting. The models developed are based on Random Forest, K-Nearest Neighbors, and Gradient Boosting methods. The models were studied for the adequacy and accuracy of predictive incidence for 3, 7, 10, 14, 21, and 30 days. The study used data on new cases of COVID-19 in Germany, Japan, South Korea, and Ukraine. These countries are selected because they have different dynamics of the COVID-19 epidemic process, and their governments have applied various control measures to contain the pandemic. The simulation results showed sufficient accuracy for practical use in the K-Nearest Neighbors and Gradient Boosting models. Public health agencies can use the models and their predictions to address various pandemic containment challenges. Such challenges are investigated depending on the duration of the constructed forecast.

The complete genome of Salmonella enterica subsp. enterica serovar Kottbus strain Kharkiv (serogroup C2-C3), which was isolated from a commercial pork production facility in Kharkiv, Ukraine, was assembled using long-read Nanopore sequences. A single circular contig (4,799,045 bp) comprised a complete chromosome encoding antibiotic resistance, highlighting the risk of cross-species livestock and human infection.

This study has evaluated the correlation between different carbapenemases detection methods on carbapenem non-susceptible Klebsiella pneumoniae strains from Northern and Eastern Europe; 31 institutions in 9 countries participated in the research project, namely Finland, Estonia, Latvia, Lithuania, Russia, St. Petersburg, Poland, Belarus, Ukraine, and Georgia. During the research program, a total of 5,001 clinical K. pneumoniae isolates were screened for any carbapenem non-susceptibility by the disk diffusion method, Vitek 2 or Phoenix system following the EUCAST guideline on detection of resistance mechanisms, version 1.0. Strains isolated from outpatients and hospitalized patients from April 2015 to June 2015 were included. All types of samples (blood, pus, urine, etc.) excluding fecal screening or fecal colonization samples have been represented. In total, 171 carbapenemase screening-positive K. pneumoniae isolates (3.42%) were found and characterized. Several methods were used for detection of carbapenemases production, including Luminex assay (PCR and hybridization), whole genome sequencing, MALDI-TOF based Imipenem degradation assay, and immunochromatography testing. Minimal inhibitory concentration determination for Meropenem by agar-based gradient method was also used. Finally, 83 K. pneumoniae strains were carbapenemase negative by all confirmation methods (49.4% of all screening-positive ones), 74 – positive by three methods (44.0%), 8 – positive by two methods (4.8%) and 3 – positive by only one method (1.8%). The sensitivity of the tests was 96.3% for Whole genome sequencing and MALDI-TOF assay (both three undetected cases), and 95.1% for Luminex-Carba (4 undetected cases). The most commonly detected carbapenemases were NDM ( n = 54) and OXA-48 ( n = 26), followed by KPC-2, VIM-5, and OXA-72 (one case of each). Our results showed that different types of carbapenemases can be detected in the countries involved in the project. The sensitivity of our methods for carbapenemase detection (including screening as a first step and further confirmation tests) was >95%, but we would recommend using different methods to increase the sensitivity of detection and make it more precise.

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