Back

Final sizes and durations of new COVID-19 pandemic waves in Ukraine and around the world predicted by generalized SIR model

Nesteruk, I.

2021-11-24 epidemiology
10.1101/2021.11.22.21266683 medRxiv
Show abstract

New waves of the COVID-19 pandemic in Ukraine, which began in the summer of 2021, and after holidays in the middle of October 2021, were characterized by almost exponential growth of smoothed daily numbers of new cases. This is a matter of great concern and the need to immediately predict the epidemic dynamics in order to assess the possible maximum values of new cases, the risk of infection and the number of deaths. The generalized SIR-model and corresponding parameter identification procedure was used to simulate and predict the dynamics of two new epidemic waves in Ukraine and one in the whole world. Results of calculations show that new cases in Ukraine will not stop to appear before November 2022. If the global situation with vaccination, testing and treatment will not change, the pandemic could continue for another ten years.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 6%
23.4%
Similar papers in this journal
2
Scientific Reports
○ 3612 papers in training set
Top 4%
10.2%
Similar papers in this journal
3
Infectious Disease Modelling
○ 54 papers in training set
Top 0.2%
6.5%
Similar papers in this journal
4
Mathematical Biosciences and Engineering
● 23 papers in training set
Top 0.2%
4.2%
Similar papers in this journal
5
International Journal of Environmental Research and Public Health
○ 128 papers in training set
Top 1%
3.4%
Similar papers in this journal
6
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.2%
3.3%
50% of probability mass above
7
The European Physical Journal Plus
○ 13 papers in training set
Top 0.1%
2.6%
8
Nonlinear Dynamics
○ 10 papers in training set
Top 0.1%
2.5%
9
Journal of Clinical Medicine
○ 97 papers in training set
Top 2%
2.2%
10
Frontiers in Applied Mathematics and Statistics
○ 11 papers in training set
Top 0.2%
2.2%
11
Mathematics
○ 11 papers in training set
Top 0.1%
1.8%
12
Frontiers in Public Health
○ 148 papers in training set
Top 3%
1.8%
13
Viruses
○ 332 papers in training set
Top 3%
1.6%
14
Mathematical Biosciences
○ 49 papers in training set
Top 0.7%
1.5%
15
Royal Society Open Science
● 214 papers in training set
Top 4%
1.5%
16
Frontiers in Physics
○ 21 papers in training set
Top 0.1%
1.4%
17
JMIRx Med
◐ 32 papers in training set
Top 1%
1.4%
18
Biomedical Signal Processing and Control
○ 22 papers in training set
Top 0.4%
1.2%
19
PLOS Computational Biology
● 1863 papers in training set
Top 16%
1.2%
20
Epidemiology and Infection
◐ 89 papers in training set
Top 2%
1.2%
21
Applied Sciences
○ 25 papers in training set
Top 0.5%
1.1%
22
Informatics in Medicine Unlocked
○ 22 papers in training set
Top 1%
1.0%
23
Bulletin of Mathematical Biology
○ 92 papers in training set
Top 1%
0.9%
24
Frontiers in Artificial Intelligence
○ 20 papers in training set
Top 0.6%
0.9%
25
Heliyon
○ 152 papers in training set
Top 7%
0.9%
26
BMC Infectious Diseases
○ 133 papers in training set
Top 4%
0.6%
27
Biology
○ 45 papers in training set
Top 1%
0.6%
28
Chaos: An Interdisciplinary Journal of Nonlinear Science
● 17 papers in training set
Top 0.3%
0.6%
29
Computer Methods in Biomechanics and Biomedical Engineering
○ 10 papers in training set
Top 0.3%
0.6%
30
COVID
○ 14 papers in training set
Top 0.5%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.