Estimating Active Case of Infectious Disease using Seird Model: Case of Covid-19 Diseases in Malaysia

Abstract

The global outbreak of the novel coronavirus disease (COVID-19) has unleashed widespread turmoil. The number of confirmed cases continues to rise and numerous countries exhibit a persistent upward trajectory in daily infections. In an effort to predict the trajectory of active cases, we employed the SEIRD mathematical model, which provides a visual representation of the ongoing spread of COVID-19. SEIRD models were used to estimate and visualize the trend of active cases, recovery and death during the infection period. Using the lmfit package in Python, the solution for Non-Linear Least-Square Minimization was provided and minimalizes the error for the curve-fitting, thus giving the optimal number for the coefficient for infection, recovery rate and death rate. It is important to estimate the current trend of an active case in Malaysia as early action can minimize the effect of COVID-19 infections.