PROCESS SYSTEMS ENGINEERING APPROACHES FOR INTERVENTION PLANNING DURING INFECTIOUS DISEASE OUTBREAKS

Abstract

Infectious disease outbreaks have occurred frequently throughout history. This research is aimed at designing dynamic, optimal health interventions during these events using systems engineering based approaches. Online sampling of weekly data from a realistic, calibrated `four-community agent-based model¿ was used to estimate the age-wise vaccine allocation ratios using the proposed Non-linear Model Predictive Control scheme. The control algorithm used a validated, 15 age-structured compartmental model to estimate the number of vaccines to be administered for each age-group for the subsequent week in the agent-based model. Meta-analysis from the simulation studies has been used to propose robust policies to handle future outbreaks. In addition, the list of possible US counties to be targeted for health related interventions during future events (modeled using decision trees) is also proposed. The models/resultant tools developed during this research can aid public health decision makers to design effective health interventions and respond swiftly to an infectious disease outbreak.