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ASSESSING THE EFFECTS OF TIME-DEPENDENT RESTRICTIONS AND CONTROL ACTIONS TO FLATTEN THE CURVE OF COVID-19 IN KAZAKHSTAN

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dc.contributor.author Do, Ton Duc
dc.contributor.author Gui, Meei Mei
dc.contributor.author Ng, Kok Yew
dc.date.accessioned 2021-05-11T10:16:50Z
dc.date.available 2021-05-11T10:16:50Z
dc.date.issued 2021-02
dc.identifier.citation Do, T. D., Gui, M. M., & Ng, K. Y. (2021). Assessing the effects of time-dependent restrictions and control actions to flatten the curve of COVID-19 in Kazakhstan. PeerJ, 9, e10806. https://doi.org/10.7717/peerj.10806 en_US
dc.identifier.issn 2167-8359
dc.identifier.uri https://peerj.com/articles/10806/#
dc.identifier.uri https://doi.org/10.7717/peerj.10806
dc.identifier.uri http://nur.nu.edu.kz/handle/123456789/5376
dc.description.abstract This article presents the assessment of time-dependent national-level restrictions and control actions and their effects in fighting the COVID-19 pandemic. By analysing the transmission dynamics during the first wave of COVID-19 in the country, the effectiveness of the various levels of control actions taken to flatten the curve can be better quantified and understood. This in turn can help the relevant authorities to better plan for and control the subsequent waves of the pandemic. To achieve this, a deterministic population model for the pandemic is firstly developed to take into consideration the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measures used to describe the transmission dynamics of this pandemic. The reproduction number alongside other key parameters of the model can then be estimated by fitting the model to real-world data using numerical optimisation techniques or by inducing ad-hoc control actions as recorded in the news platforms. In this article, the model is verified using a case study based on the data from the first wave of COVID-19 in the Republic of Kazakhstan. The model is fitted to provide estimates for two settings in simulations; time-invariant and time-varying (with bounded constraints) parameters. Finally, some forecasts are made using four scenarios with time-dependent control measures so as to determine which would reflect on the actual situations better. en_US
dc.language.iso en en_US
dc.publisher PeerJ en_US
dc.relation.ispartofseries PeerJ;9, e10806
dc.rights Attribution-NonCommercial-ShareAlike 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/us/ *
dc.subject Computational Biology en_US
dc.subject Mathematical Biology en_US
dc.subject Epidemiology en_US
dc.subject Global Health en_US
dc.subject Infectious Diseases en_US
dc.subject COVID-19 en_US
dc.subject Coronavirus en_US
dc.subject Modelling en_US
dc.subject SEIRD en_US
dc.subject Time-dependent analysis en_US
dc.title ASSESSING THE EFFECTS OF TIME-DEPENDENT RESTRICTIONS AND CONTROL ACTIONS TO FLATTEN THE CURVE OF COVID-19 IN KAZAKHSTAN en_US
dc.type Article en_US
workflow.import.source science


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