Dynamic data-driven algorithm to predict cumulative COVID-19 infected cases using susceptible-infected-susceptible model

Abstract

Abstract Objectives In recent times, researchers have used Susceptible-Infected-Susceptible (SIS) model to understand the spread of the COVID-19 pandemic. The SIS model has two compartments, susceptible and infected. In this model, the interest is to determine the number of infected cases at a given time point. However, it is also essential to know the cumulative number of infected cases at a given time point, which is not directly available from the SIS model's present structure. The objective is to provide a modified SIS model to address that gap. Methods In this work, we propose a modified structure of the SIS model to determine the cumulative number of infected cases 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 number of cumulative infected cases. Results We demonstrate the proposed algorithm's prediction performance using COVID-19 data from Delhi, India's capital city. Considering different time periods, we observed the proposed algorithm’s performance using the modified SIS model is well to predict the cumulative infected cases with two different prediction periods 30 and 40. Our study supports the idea of estimating the modified SIS model's parameters based on the optimal training phase instead of the entire history as the training phase. Conclusions Here, we have provided a modified SIS model that accounts for deaths due to disease and predicts cumulative infected cases based on an optimally chosen training phase. The proposed estimation process is beneficial when the disease under study changes its spreading pattern over time. We have developed the modified SIS model considering COVID-19 as the disease under focus. However, the model and algorithms can be applied to predict the cumulative cases of other infectious diseases.