A multi-agent model for adaptive vaccination during infectious disease outbreaks

Infectious disease outbreaks are a huge burden on healthcare, causing hospitalizations, deaths and rigorously impairing the economy. In this work, an age-structured multi-agent model has been developed to simulate an epidemic spread among the US population and strategize adaptive vaccination planning to control the spread. The population was split into six distinct groups of agents depending upo on their age. In addition, the calibration of the multi-agent model parameters for H1N1 2009 pandemic, validation of the model using H1N1 2009 pandemic data from Centers for Disease Control and Prevention (CDC, US) was carried out. Using these data, the model was calibrated such that the H1N1 deaths predicted by the model was comparable to that of the deaths reported by CDC, while the H1N1 hospitalizations predicted were within the 95% confidence interval. A series of hypothetical simulations of a H1N1 like pandemic outbreak among the US population to illustrate the effectiveness of various adaptive strategies proposed in the literature will be presented. Each set of simulation was replicated 100 times so as to average the stochastic effects of parameter(s) uncertainty. The multi-agent model developed in this work can be used as a decision support system to systematically gauge the effectiveness of various interventions so as to aid healthcare policy makers to design dynamic, optimal health interventions to counter disease outbreaks.