Epidemiological Situation on Plague in 2020. Forecast of Episootic Activity of Natural Plague Foci in the Russian Federation and Other CIS Countries for 2021
Problemy Osobo Opasnykh Infektsii(2021)
Russian Research Anti-Plague Institute “Microbe” | Irkutsk Research Anti-Plague Institute of Siberia and Far East | Masgut Aikimbaev National Scientific Center for Especially Dangerous Infections of the Ministry of Healthcare of the Republic of Kazakhstan | Republican Center for Quarantine and Particularly Dangerous Infections of the Ministry of Health of the Kyrgyz Republic | Plague Control Center | Stavropol Research Anti-Plague Institute
Abstract
The aim of the work was to substantiate the forecast of the epidemiological and epizootiological situation in natural foci of plague in the Russian Federation, countries of the near and far abroad for the year of 2021. Characteristics of the distribution of Yersinia pestis strains of the main subspecies (subspecies pestis) of medieval and antique biovars, Caucasian (ssp. caucasica) and central Asian (ssp. central asiatica) subspecies by 45 natural foci of the CIS countries are presented in the paper. The persistence of a multidirectional trend in the dynamics of epizootic activity of natural foci of the CIS countries with the circulation of Y. pestis pestis strains of the medieval biovar of the 2.MED1 phylogenetic branch and the antique biovar of the 0.ANT5, 4.ANT phylogenetic branches in the current decade has been outlined. For the Russian Federation, the development of epizootics is predicted in the Gorno-Altai highland and Tuva mountain natural foci caused by the circulation of Y. pestis pestis strains of the antique biovar 4.ANT and Y. pestis of the Altai biovar of the Central Asian subspecies 0.PE4a in 2021. For the Republic of Kazakhstan, there is a high probability of preserving epizootic activity in the North Aral, Aral-Karakum, Balkhash, Mojynkum, Taukum desert and Ili intermountain natural foci with the circulation of Y. pestis pestis strains of the medieval biovar of the phylogenetic branch 2.MED1. For the Kyrgyz Republic, the forecast for the development of plague epizootics caused by Y. pestis pestis strains of the antique biovar 0.ANT5 phylogenetic branch in the Sarydzhaz and Upper Naryn high-mountain natural foci has been substantiated. A high epidemic risk of epizootic manifestations caused by highly virulent strains of Y. pestis pestis of antique biovars 0.ANT5, 4.ANT and medieval biovar 2.MED1 for the entire territory of the CIS countries is noted. The relevance of implementing forecasts of the epidemiological situation into practice, taking into account the molecular-genetic and epidemiological characteristics of Y. pestis strains circulating in areas of expected epizootic manifestations of plague, is highlighted.
MoreTranslated text
Key words
natural foci of plague,epizootic activity,strains,morbidity,forecast,preventive measures
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
Microbiology Resource Announcements 2022
被引用4
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest