A hierarchical Bayesian network-based semi-mechanistic model for handling data variabilities in dynamical process systems

Abstract

This work presents a hierarchical Bayesian network-based model for handling dynamic nature, data variabilities, and information fusion in a dynamical process system. The overall dynamics has been captured by physics-based models while uncertainty and local variabilities in the model parameters have been accounted for by using a hierarchical Bayesian network formalism. The resulting semi-mechanistic model has been investigated in two cases, a simulated case of an enzymatic reaction system and a real-world scenario of pandemic risk forecasting. The first case study on the enzymatic reaction system shows effectiveness of the proposed semi-mechanistic framework in a closed system with limited data from different sources. The second case illustrates scope of the proposed semi-mechanistic framework to open systems by investigating transmission of disease under evolving conditions of the advent of multiple waves of a pandemic outbreak. The findings have been presented in terms of the most probable values and credible intervals that account for data variabilities in different scenarios. The model is benchmarked and validated for the fatality forecast of the Coronavirus Disease of 2019 by the observed data and benchmark models reported by the Center for Disease Prevention and Control of the United States [https://covid.cdc.gov/covid-data-tracker/#datatracker-home].