Estimation of the epidemiological parameter for the COVID-19 outbreak

COVID 19 disease has spread worldwide, disrupting the economy, society, education, and health. It became crucial to understand the transmission curve and identify the best action in combating this highly transmissible epidemic. Epidemiological parameters of the deterministic Susceptible-Infected-Removed (SIR) model are widely used in explaining the characteristics and behavior of the disease spreading. Disease like COVID-19 is highly influenced by the uncontrolled factors of randomness, hence in this paper, the deterministic susceptible-infected-recovered-death (SIRD) model is extended to the stochastic SIRD (SSIRD) model. SSIRD model takes into consideration the noisy behavior of the process which explains the uncontrolled effects of the COVID-19 outbreak. The epidemiological parameter of the model changes throughout the epidemic due to external factors such as enforcement and public obedience to the control measures as well as the changes in healthcare facilities. These parameters need to be estimated. This paper estimates the epidemiological parameters of the SSIRD model using the Markov Chain Monte Carlo (MCMC) method of the Metropolis Hasting algorithm. The COVID-19 data from Malaysia and Indonesia are used and the dynamical behavior of the COVID-19 outbreak in both countries is simulated. The results show the parameter changing due to the uncontrolled factors influencing the trend of the pandemic curve.