Jump-Drop Adjusted Prediction of Cumulative Infected Cases using the Modified SIS Model

Accurate prediction of cumulative COVID-19 infected cases is essential for effectively managing the limited healthcare resources, specifically in India. Historically, epidemiological models have helped control such epidemics. Those models need accurate past data to predict for future. However, for various reasons, we observe sudden drops and jumps in the number of daily reported COVID-19 infected cases on some days, not aligned with the overall trend. If we incorporate those observations in the training data, the model's prediction accuracy may worsen, as they do not capture the correct trend in the training data. But it is not straightforward for the existing epidemiological models to decide a specific day as a sudden drop or jump. We propose an algorithm that automatically determines any drop or jump days in this work. Then, based on the overall trend in the data, we adjust the number of daily infected cases on those days and decide on the training data based on the adjusted observations. We have applied the proposed algorithm in a recently proposed modified Susceptible-Infected-Susceptible (SIS) to show that adjusted training data gives better prediction accuracy when jump and drop exist in the training data.