This paper presents a mathematical model and a parameter estimation technique for predicting the evolution of COVID-19 pandemic . First, a modified SEIR (susceptible, exposed, infectious, and recovered) is introduced. This modified model can take into account the time-dependent characteristic of the parameters, which is an important factor of this epidemic. Based on the real data, the parameters of the model are estimated . In this paper, the trust-region-reflective algorithm is applied for parameter estimation. After that, based on these calculated parameters, the prediction is made under three scenarios of the control measures and one scenario without any control measures. Unlike the previous models in which the effects of control action is not quantified, the proposed model can do this in order to better predict the evolution of this pandemic. The case study considers Kazakhstan, a badly affected country by this pandemic but do not receive enough considerations .
This paper presents the assessment and forecasts of the transmission dynamics of COVID-19 with the effects of time-dependent control actions using the data in Kazakhstan as a case study . First, a modified SEIRD (susceptible, exposed, infectious, recovered, and death) model is introduced. The SEIRD model is developed to take into account the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measurements used to describe the transmission dynamics of this epidemic. The reproduction number alongside other key parameters of the model can be estimated by fitting the model to real-world data using numerical optimisation techniques . In this paper, the trust-region-reflective (TTR) algorithm is used to fit the model to estimate both time-invariant and time-varying (with bounded contsraints) parameters. The model is verified using a case study based on the data in Kazakhstan, which is a country badly affected by this pandemic but has not been receiving much attention by the research community thus far. Finally, some forecasts are made using five scenarios with time-dependent control measures such that the effects of the control actions onto the transmission dynamics of the pandemic can be quantified and better understood .
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a mathematical model and a parameter estimation technique for predicting the evolution
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recovered, and death) model
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This modified model can
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characteristic of the parameters, which is an important factor of
characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measurements used to describe the transmission dynamics of
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Based on the real data, the
The reproduction number alongside other key
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are estimated
can be estimated by fitting the model to real-world data using numerical optimisation techniques
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algorithm is applied for parameter estimation. After that, based on these calculated parameters, the prediction is made under three scenarios of the control measures and one scenario without any control measures. Unlike the previous models in which the effects of control action is not quantified, the proposed model can do this in order to better predict the evolution of this pandemic. The case study considers Kazakhstan, a badly affected country
(TTR) algorithm is used to fit the model to estimate both time-invariant and time-varying (with bounded contsraints) parameters. The model is verified using a case study based on the data in Kazakhstan, which is a country badly affected
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do not receive enough considerations
has not been receiving much attention by the research community thus far. Finally, some forecasts are made using five scenarios with time-dependent control measures such that the effects of the control actions onto the transmission dynamics of the pandemic can be quantified and better understood