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  1. Article: Explicit formulae for the peak time of an epidemic from the SIR model.

    Turkyilmazoglu, Mustafa

    Physica D. Nonlinear phenomena

    2021  Volume 422, Page(s) 132902

    Abstract: ... with favourable performance of the presented formulae for assessing the peak time occurrence of an epidemic ... with a straightforward computation of peak time of outbreak. Initially, the time scale from the relevant autonomous SIR ... Reducing the peak time of an epidemic disease in order for slowing down the eventual dynamics and ...

    Abstract Reducing the peak time of an epidemic disease in order for slowing down the eventual dynamics and getting prepared for the unavoidable epidemic wave is utmost significant to fight against the risks of a contagious epidemic disease. To serve to this purpose, the well-documented infection model of SIR is examined in the current research to propose an analytical approach for providing an explicit formula associated with a straightforward computation of peak time of outbreak. Initially, the time scale from the relevant autonomous SIR epidemic model is formulated analytically via an integral based on the fractions of susceptible and infected compartments. Afterwards, through a series expansion of the logarithmic term of the resultant integrand, the peak time is shown to rely upon the fraction of susceptible, the infectious ratio as well as the initial fractions of ill and susceptible individuals. The approximate expression is shown to rigorously capable of capturing the time threshold of illness for an epidemic from the semi-time SIR epidemiology. Otherwise, it is also successful to predict the peak time from a past history of a disease when all-time epidemic model is adopted. Accuracy of the derived expressions are initially confirmed by direct comparisons with recently reported approximate formulas in the literature. Several other epidemic disease samples including the COVID-19 often studied in the recent literature are eventually attacked with favourable performance of the presented formulae for assessing the peak time occurrence of an epidemic. A quick evaluation of the peak time of a disease certainly enables the governments to take early effective epidemic precautions.
    Language English
    Publishing date 2021-03-26
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1466587-6
    ISSN 1872-8022 ; 0167-2789
    ISSN (online) 1872-8022
    ISSN 0167-2789
    DOI 10.1016/j.physd.2021.132902
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Explicit formulae for the peak time of an epidemic from the SIR model. Which approximant to use?

    Kröger, Martin / Turkyilmazoglu, Mustafa / Schlickeiser, Reinhard

    Physica D. Nonlinear phenomena

    2021  Volume 425, Page(s) 132981

    Abstract: ... solutions (SK-I, SK-II, and CG) of the semi-time SIR model in its reduced formulation that allow one to come ... epidemic precautions. Recently, an explicit analytic, approximate expression (MT) for the peak time ... up with different explicit expressions for the peak time of the infected compartment (Schlickeiser ...

    Abstract An analytic evaluation of the peak time of a disease allows for the installment of effective epidemic precautions. Recently, an explicit analytic, approximate expression (MT) for the peak time of the fraction of infected persons during an outbreak within the susceptible-infectious-recovered/removed (SIR) model had been presented and discussed (Turkyilmazoglu, 2021). There are three existing approximate solutions (SK-I, SK-II, and CG) of the semi-time SIR model in its reduced formulation that allow one to come up with different explicit expressions for the peak time of the infected compartment (Schlickeiser and Kröger, 2021; Carvalho and Gonçalves, 2021). Here we compare the four expressions for any choice of SIR model parameters and find that SK-I, SK-II and CG are more accurate than MT as long as the amount of population to which the SIR model is applied exceeds hundred by far (countries, ss, cities). For small populations with less than hundreds of individuals (families, small towns), however, the approximant MT outperforms the other approximants. To be able to compare the various approaches, we clarify the equivalence between the four-parametric dimensional SIR equations and their two-dimensional dimensionless analogue. Using Covid-19 data from various countries and sources we identify the relevant regime within the parameter space of the SIR model.
    Language English
    Publishing date 2021-06-24
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1466587-6
    ISSN 1872-8022 ; 0167-2789
    ISSN (online) 1872-8022
    ISSN 0167-2789
    DOI 10.1016/j.physd.2021.132981
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: An explicit formula for minimizing the infected peak in an SIR epidemic model when using a fixed number of complete lockdowns

    Sontag, Eduardo

    medRxiv

    Abstract: ... complete-lockdowns of prespecified lengths (such as two weeks) so as to minimize the peak of the infective ... compartment. Perhaps surprisingly, it is possible to give an explicit and easily computable rule ... to high "second waves" of infections of COVID-19. This paper asks what should be the timing of a set of k ...

    Abstract A too-early start of NPIs (non-pharmaceutical interventions) such as social distancing may lead to high "second waves" of infections of COVID-19. This paper asks what should be the timing of a set of k complete-lockdowns of prespecified lengths (such as two weeks) so as to minimize the peak of the infective compartment. Perhaps surprisingly, it is possible to give an explicit and easily computable rule for when each lockdown should commence. Simulations are used to show that the rule remains fairly accurate even if lockdowns are not perfect.
    Keywords covid19
    Language English
    Publishing date 2021-04-18
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2021.04.11.21255289
    Database COVID19

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