Impact of control interventions on COVID-19 population dynamics in Malaysia: a mathematical study

Abstract

Coronavirus disease 2019 (COVID-19) pandemic has posed a serious threat to both the human health and economy of the affected nations. Despite several control efforts invested in breaking the transmission chain of the disease, there is a rise in the number of reported infected and death cases around the world. Hence, there is the need for a mathematical model that can reliably describe the real nature of the transmission behaviour and control of the disease. This study presents an appropriately developed deterministic compartmental model to investigate the effect of different pharmaceutical (treatment therapies) and non-pharmaceutical (particularly, human personal protection and contact tracing and testing on the exposed individuals) control measures on COVID-19 population dynamics in Malaysia. The data from daily reported cases of COVID-19 between 3 March and 31 December 2020 are used to parameterize the model. The basic reproduction number of the model is estimated. Numerical simulations are carried out to demonstrate the effect of various control combination strategies involving the use of personal protection, contact tracing and testing, and treatment control measures on the disease spread. Numerical simulations reveal that the implementation of each strategy analysed can significantly reduce COVID-19 incidence and prevalence in the population. However, the results of effectiveness analysis suggest that a strategy that combines both the pharmaceutical and non-pharmaceutical control measures averts the highest number of infections in the population.

Optimal control analysis of COVID-19 vaccine epidemic model: a case study

“…The authors in [ 15 ] studied the optimal control measures coupled with cost-effective analysis to curtail COVID-19 in the selected region. A similar mathematical analysis with a case study of Malaysia and Nigeria is conducted by Abidemi et al and Olaniyi et al in [ 16 , 17 ], respectively. The authors in [ 16 ] considered the actual data reported in Malaysia from 3 March to 31 December of 2020 and parameterized the proposed model.…”

Direct Statistical Modeling of Spread of Epidemic Based On a Stage-Dependent Stochastic Model

“…где ξ j -случайная величина c экспоненциальным распределением, параметр которого R(X(t j )) приведен в формуле (22). Отметим, что изменения компонент X(t j ) могут приводить к изменениям некоторых компонент Ω(t j ) (см.…”

“…На первом шаге для фиксированных j и t j вычисляем константу R(X(t j )) по формуле (22). Находим величину τ j по формуле (23), генерируя экспоненциально распределенную случайную величину ξ j c параметром R(X(t j )).…”

Estimating a continuously varying offset between multivariate time series with application to COVID-19 in the United States

“…First, many papers based on existing mathematical models, such as the susceptible-infected-recovered (SIR) model and the (effective) reproductive ratio [ 16 ], have been proposed and systematically collated by researchers [ 17 , 18 ]. Next, nonlinear dynamics researchers have proposed several sophisticated extensions to the classical predictive SIR model, including finding analytical solutions [ 19 , 20 ], modifications with additional variables [ 21 – 26 ], incorporation of Hamiltonian dynamics [ 27 ] or network models [ 28 ], and a closer analysis of uncertainty in the SIR equations [ 29 ]. Other mathematical approaches to prediction and analysis include power-law models [ 30 – 32 ], forecasting models [ 33 ], fractal curves [ 34 ], Bayesian methods [ 35 ], regression models and feature selection [ 36 , 37 ], Markov chain Monte Carlo models [ 38 ], distance analysis [ 39 , 40 ], network models [ 41 – 43 ], analyses of the dynamics of transmission and contact [ 44 , 45 ], clustering [ 46 , 47 ] and many others [ 48 – 53 ].…”