Dynamic Data-Driven Algorithm to Predict the Cumulative COVID-19 Infected Cases Using Susceptible-Infected-Susceptible Model

In recent times, researchers have used Susceptible-Infected-Susceptible (SIS) model to understand the spread of pandemic COVID-19. The SIS model has two compartments, susceptible and infected. In this model, the interest is to determine the number of infected people at a given time point. However, it is also essential to know the cumulative number of infected people at a given time point, which is not directly available from the SIS model’s present structure. In this work, we propose a modified structure of the SIS model to determine the cumulative number of infected people at a given time point. We develop a dynamic data-driven algorithm to estimate the model parameters based on an optimally chosen training phase to predict the same. We demonstrate the proposed algorithm’s prediction performance using COVID-19 data from Delhi, India’s capital city.