Information index augmented eRG to model vaccination behaviour: A case study of COVID-19 in the US

Recent pandemics triggered the development of a number of mathematical models and computational tools apt at curbing the socio-economic impact of these and future pandemics. The need to acquire solid estimates from the data led to the introduction of effective approaches such as the \emph{epidemiological Renormalization Group} (eRG). A recognized relevant factor impacting the evolution of pandemics is the feedback stemming from individuals' choices. The latter can be taken into account via the \textit{information index} which accommodates the information--induced perception regarding the status of the disease and the memory of past spread. We, therefore, show how to augment the eRG by means of the information index. We first develop the {\it behavioural} version of the eRG and then test it against the US vaccination campaign for COVID-19. We find that the behavioural augmented eRG improves the description of the pandemic dynamics of the US divisions for which the epidemic peak occurs after the start of the vaccination campaign. Our results strengthen the relevance of taking into account the human behaviour component when modelling pandemic evolution. To inform public health policies, the model can be readily employed to investigate the socio-epidemiological dynamics, including vaccination campaigns, for other regions of the world.