Analysis of Coronavirus Dynamics Using the Generalized Logistic Model

Kokoulina, M.V.; Epifanova, A.S.; Pelinovsky, Efim; Kurkina, Oxana; Kurkin, Andrey

doi:10.46960/1816-210x_2020_3_28

Cited by 5 publications

(1 citation statement)

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“…The recent coronavirus epidemic, which has affected all countries, arouse a great deal of interest in mathematical models that allow describing the dynamics of the growth of new cases and predicting pandemic development. To the surprise of many, it turned out that even the simplest logistic model based on the first order ordinary differential equation provided reasonable accuracy in describing the number of cases in different countries during the first wave of the epidemic [ 1 , 2 , 3 , 4 , 5 , 6 ]. The classical logistic equation was proposed by Verhulst in 1838 [7] and has the following form:

where N is the current number of infected persons, r is the infection rate, and N ∞ is the total number of infected persons in the considered wave in the region.…”

Section: Introductionmentioning

confidence: 99%

Gompertz model in COVID-19 spreading simulation

Pelinovsky

Kokoulina

Epifanova

et al. 2022

Chaos, Solitons & Fractals

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The paper reports on application of the Gompertz model to describe the growth dynamics of COVID-19 cases during the first wave of the pandemic in different countries. Modeling has been performed for 23 countries: Australia, Austria, Belgium, Brazil, Great Britain, Germany, Denmark, Ireland, Spain, Italy, Canada, China, the Netherlands, Norway, Serbia, Turkey, France, Czech Republic, Switzerland, South Korea, USA, Mexico, and Japan. The model parameters are determined by regression analysis based on official World Health Organization data available for these countries. The comparison of the predictions given by the Gompertz model and the simple logistic model (i.e., Verhulst model) is performed allowing to conclude on the higher accuracy of the Gompertz model.

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where N is the current number of infected persons, r is the infection rate, and N ∞ is the total number of infected persons in the considered wave in the region.…”

Section: Introductionmentioning

confidence: 99%

Gompertz model in COVID-19 spreading simulation

Pelinovsky

Kokoulina

Epifanova

et al. 2022

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

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Generalization of Non-elementary Linear Regressions

Bazilevskiy

2023

Modelling and Data Analysis

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<p>Earlier, the author developed a non-elementary linear regression consisting of a linear part and all possible combinations of min and max binary operations. This article is devoted to its generalization. For the first time a non-elementary linear regression with a linear part and all possible combinations of binary, ternary, ..., l-ary operations min and max has been introduced. The proposed model generalizes both linear regression and the Leontief function, and can be effectively used both for predicting and for interpreting the study object functioning. An estimation algorithm was developed using the method of least squares for non-elementary linear regressions without a linear part and with an l-ary operation min (max), i.e. regressions with specification in the form of a Leontief function. The essence of the algorithm is to form a set of possible values of slope coefficients, from which a point is selected with the minimum value of the residual sum of squares. A system of linear inequalities is identified that makes it possible to form such a set. Using the algorithm, a model of the gross regional product of the Irkutsk region was construct and its interpretation was given.</p>

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Analysis of the readiness of Russian regions for the COVID-19 pandemic based on demographic sustainability and development of the healthcare system

Rudneva

Sokolov

2023

Population

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Assessment of the readiness of regions for the occurrence of critical situations is atopical area of research in modern conditions. The problem of high mortality in Russia determines the importance of studying the factors that shape the current state of the socio-demographic system of the population and the prerequisites for its change in the future or in the present under the influence of negative processes. To form an objective picture of the current situation, two areas are identified: potential (the state of the region's healthcare system) and demographic stability (risks of population decline), on the basis of which complex integral indices are calculated and regions are ranked. The specificity of the author's approach to the analysis consists in the complex use of demographic indicators, morbidity indicators and the functioning of the healthcare system. As a result of the analysis, it was found out that an ambiguous situation has developed in the regions. According to the assessment of demographic sustainability, the republics of the North Caucasus have become the best, but there is also the lowest level of development of the healthcare system. The opposite situation is observed in remote and sparsely populated regions of the Far East — Sakhalin Oblast, the Jewish Autonomous Oblast and Chukotka Autonomous Okrug — with high indicators of the potential of the healthcare system there are high demographic risks. The depth of the consequences of the pandemic was reflected in the form of excess mortality. In the first year, the regions of the North Caucasus became leaders in terms of mortality growth — Chechnya (by 44%), Dagestan (by 32%) and Ingushetia (by 27%).The most stable were Adygea, Sevastopol and Buryatia — excess mortality did not exceed 6%. In the second year, actively increasing mortality has already affected all regions, with the exception of the Republic of Tyva and Chukotka Autonomous Okrug having the lowest proportion of the population over working age. The proposed concept of a two-way study of regions will make it possible to trace the main problem areas in dynamics and give an opportunity to prevent the consequences of other possible force majeure.

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Analysis of Coronavirus Dynamics Using the Generalized Logistic Model

Cited by 5 publications

References 0 publications

Gompertz model in COVID-19 spreading simulation

Gompertz model in COVID-19 spreading simulation

Generalization of Non-elementary Linear Regressions

Analysis of the readiness of Russian regions for the COVID-19 pandemic based on demographic sustainability and development of the healthcare system

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