An Improved Data Driven Dynamic SIRD Model for Predictive Monitoring of COVID-19

Abstract

COVID-19 pandemic spreaded across the world in early 2020. It forced many countries to impose lockdown to pre-vent surge in the number of infected cases. There has been a huge impact on social and economic activities worldwide. In this work, we carry out the functional modeling of COVID-19 infection trends using two models: the Gaussian mixture model (GMM) and the composite logistic growth model (CLGM). Unlike the traditional SIRD models that use numerical data fitting, we utilize the best data-fitted curves employing GMM and/or CLGM to construct the Susceptible-Infected-Recovered-Dead (SIRD) pandemic model. Further, we derive the explicit expressions of time-varying parameters of the SIRD model unlike most works that consider static parameters without any closed form solution. The proposed parameterized dynamic SIRD model is generically applicable to any pandemic, can capture the day-to-day dynamics of the pandemic and can assist the governing bodies in devising efficient action plans to deal with the prevailing pandemic.