Demand Forecasting in Hospital and ICUs using a Modified Propagation Dynamic Model: A novel GSEIR approach

Compartmentalized mathematical modeling of infectious diseases is essential in understanding the epidemic's spread in the population. It helps to identify the best strategy for control and prevention. In this paper, we propose an epidemiological model to estimate disease burden statistics of infected, recovered, and deceased cases as a function of time, including two sensitive compartments, namely hospitalization and intensive care unit admissions. To formulate and solve the flow mutation from one compartment to another, we use a non-linear least squares algorithm. Our main goal is to forecast infection rates’ outcome trends to alert health authorities of potential risks of hospital overload. The approach developed has been validated using COVID-19 data. The results obtained demonstrate the effectiveness and robustness of the model. The model can also be applied as a benchmark for assessing disease burden in the hospital environment.