Affiliations 

  • 1 Centre for Mathematical Sciences, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Gambang. norazaliza@ump.edu.my
  • 2 Centre for Mathematical Sciences, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Gambang. norhayati@ump.edu.my
  • 3 Centre for Mathematical Sciences, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Gambang. noryanti@ump.edu.my
J Public Health Res, 2021 Sep 24;11(1).
PMID: 34558879 DOI: 10.4081/jphr.2021.2130

Abstract

BACKGROUND: This research aimed to model the outbreak of COVID-19 in Malaysia and develop a GUI-based model.

DESIGN AND METHODS: The model is an improvement of the susceptible, infected, recovery, and death (SIRD) compartmental model.  The epidemiological parameters of the infection, recovery, and death rates were formulated as time dependent piecewise functions by incorporating the control measures of lockdown, social distancing, quarantine, lockdown lifting time and the percentage of people who abide by the rules. An improved SIRD model was solved via the 4th order Runge-Kutta (RK4) method and 14 unknown parameters were estimated by using Nelder-Mead algorithm and pattern-search technique. The publicly available data for COVID-19 outbreak in Malaysia was used to validate the performance of the model. The GUI-based SIRD model was developed to simulate the number of active cases of COVID-19 over time by considering movement control order (MCO) lifted date and the percentage of people who abide the rules.

RESULTS: The simulator showed that the improved SIRD model adequately fitted Malaysia COVID-19 data indicated by low values of root mean square error (RMSE) as compared to other existing models. The higher the percentage of people following the SOP, the lower the spread of disease. Another key point is that the later the lifting time after the lockdown, the lower the spread of disease.

CONCLUSIONS: These findings highlight the importance of the society to obey the intervention measures in preventing the spread of the COVID-19 disease.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.