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.

Fig. 1

Flow diagram of COVID-19 model (1), where $\lambda =\beta (I+\eta A+\rho H)$

Fig. 2

Graphical illustration of the fitted cumulative number of reported COVID-19 cases

Fig. 3

PRCC values for COVID-19 model (1) using ${\mathcal{R}}_0$ as the response function with respect to the parameter range and baseline values given in Table 2

Fig. 4

Contour plots of ${\mathcal{R}}_c$

Fig. 5

Simulations of model (2) with Strategy A and without control

Fig. 6

Simulations of model (2) with strategy B and without control

Fig. 7

Simulations of model (2) with strategy C and without control

Fig. 8

Simulations of model (2) with strategy D and without control

Fig. 9

Simulations of model (2) with strategy E and without control