Systematic description of COVID-19 pandemic using exact SIR solutions and Gumbel distributions

被引:4
作者
Amaro, J. E. [1 ,2 ]
机构
[1] Univ Granada, Dept Fis Atom Mol & Nucl, Granada 18071, Spain
[2] Univ Granada, Inst Carlos I Fis Teor & Computac, Granada 18071, Spain
关键词
COVID-19; coronavirus; SIR model; Differential equations; Gumbel distribution; A-PRIORI PATHOMETRY; MODEL; PROBABILITIES;
D O I
10.1007/s11071-022-07907-4
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
An epidemiological study is carried out in several countries analyzing the first wave of the COVID-19 pandemic using the SIR model and Gumbel distribution. The equations of the SIR model are solved exactly using the proper time as a parameter. The physical time is obtained by integration of the inverse of the infected function over proper time. Some properties of the solutions of the SIR model are studied such as time scaling and the asymmetry, which allows to obtain the basic reproduction number from the data. Approximations to the solutions of the SIR model are studied using Gumbel distributions by least squares fit or by adjusting the maximum of the infected function. Finally, the parameters of the SIR model and the Gumbel function are extracted from the death data and compared for the different countries. It is found that ten of the selected countries are very well described by the solutions of the SIR model, with a basic reproduction number between 3 and 8.
引用
收藏
页码:1947 / 1969
页数:23
相关论文
共 50 条
[1]   Numerical simulation and stability analysis of a novel reaction-diffusion COVID-19 model [J].
Ahmed, Nauman ;
Elsonbaty, Amr ;
Raza, Ali ;
Rafiq, Muhammad ;
Adel, Waleed .
NONLINEAR DYNAMICS, 2021, 106 (02) :1293-1310
[2]   An introduction to stochastic epidemic models [J].
Allen, Linda J. S. .
MATHEMATICAL EPIDEMIOLOGY, 2008, 1945 :81-130
[3]  
Amaro, 2020, ARXIV
[4]   Monte Carlo simulation of COVID-19 pandemic using Planck's probability distribution [J].
Amaro, Jose Enrique ;
Orce, Jose Nicolas .
BIOSYSTEMS, 2022, 218
[5]   Global analysis of the COVID-19 pandemic using simple epidemiological models [J].
Amaro, Jose Enrique ;
Dudouet, Jeremie ;
Orce, Jose Nicolas .
APPLIED MATHEMATICAL MODELLING, 2021, 90 (90) :995-1008
[6]  
Andersson H., 2000, Stochastic epidemic models and their statistical analysis, DOI DOI 10.1007/978-1-4612-1158-7
[7]  
[Anonymous], WORLD INF COR
[8]  
[Anonymous], COVID19 WHO INT
[9]  
Bartlett M.S., 1956, PROC 3 BERKELEY S MA, V4, P81, DOI DOI 10.2307/2342553
[10]   MEASLES PERIODICITY AND COMMUNITY SIZE [J].
BARTLETT, MS .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-GENERAL, 1957, 120 (01) :48-70