Improving Input Parameter Estimation in Online Pandemic Simulation

Abstract

Simulation models are suitable tools to represent the complexity and randomness of hospital systems. To be used as forecasting tools during pandemic waves, it is necessary an accurate estimation, by using real-time data, of all input parameters that define the patient pathway and length of stay in the hospital. We propose an estimation method based on an expectation-maximization algorithm that uses data from all patients admitted to the hospital to date. By simulating different pandemic waves, the performance of this method is compared with other two statistical estimators that use only complete data. Results collected to measure the accuracy in the parameters estimation and its influence in the forecasting of necessary resources to provide healthcare to pandemic patients show the better performance of the new estimation method. We also propose a new parameterization of the Gompertz growth model that eases the creation of patient arrival scenarios in the pandemic simulation.