Agent-Based Simulation of the COVID-19 Epidemic in Russia
Herald of the Russian Academy of Sciences, ISSN: 1555-6492, Vol: 92, Issue: 4, Page: 479-487
2022
- 9Citations
- 5Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations9
- Citation Indexes9
- Captures5
- Readers5
Article Description
Abstract: The COVID-19 pandemic has created a public health emergency in Russia and across the world. The wavelike spread of the new coronavirus infection, caused by newly emerging variants of the coronavirus, has led to a high incidence rate in all subjects of the Russian Federation. It is becoming extremely topical to get the opportunity to manage the development of the epidemic and assess the impact of certain regulatory measures on this process. This will help government agencies make informed decisions to control the burden on healthcare organizations. It is often impossible to obtain such assessments without using modern mathematical models.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85138105410&origin=inward; http://dx.doi.org/10.1134/s1019331622040219; http://www.ncbi.nlm.nih.gov/pubmed/36091848; https://link.springer.com/10.1134/S1019331622040219; https://dx.doi.org/10.1134/s1019331622040219; https://link.springer.com/article/10.1134/S1019331622040219
Pleiades Publishing Ltd
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