Abstract
The last decade saw the advent of increasingly realistic epidemic models that leverage on the availability of highly detailed census and human mobility data. Data-driven models aim at a granularity down to the level of households or single individuals. However, relatively little systematic work has been done to provide coupled behavior-disease models able to close the feedback loop between behavioral changes triggered in the population by an individual's perception of the disease spread and the actual disease spread itself. While models lacking this coupling can be extremely successful in mild epidemics, they obviously will be of limited use in situations where social disruption or behavioral alterations are induced in the population by knowledge of the disease. Here we propose a characterization of a set of prototypical mechanisms for self-initiated social distancing induced by local and non-local prevalence-based information available to individuals in the population. We characterize the effects of these mechanisms in the framework of a compartmental scheme that enlarges the basic SIR model by considering separate behavioral classes within the population. The transition of individuals in/out of behavioral classes is coupled with the spreading of the disease and provides a rich phase space with multiple epidemic peaks and tipping points. The class of models presented here can be used in the case of data-driven computational approaches to analyze scenarios of social adaptation and behavioral change.
Highlights
Understanding human behavior has long been recognized as one of the keys to understanding epidemic spreading [1,2], which has triggered intense research activity aimed at including social complexity in epidemiological models
Models based on social mobility and behavior [21,22] have shown to be valuable tools in the quantitative analysis of the unfolding of the recent H1N1 pandemic [21,22], but it has become clear that societal reactions coupling behavior and disease spreading can have substantial impact on epidemic spreading [2,23] defining limitations of most current modeling approaches [24]
In all cases we have a modification of the spreading process due to the change of mobility or contact patterns in the population. These behavioral changes may have a considerable impact on epidemic progression such as the reduction in epidemic size and delay of the epidemic peak
Summary
Understanding human behavior has long been recognized as one of the keys to understanding epidemic spreading [1,2], which has triggered intense research activity aimed at including social complexity in epidemiological models. We modify the classic susceptible-infectedrecovered (SIR) model [41] by introducing a class of individuals, SF , that represents susceptible people who self-initiate behavioral changes that lead to a reduction in the transmissibility of the infectious disease. We show that in the presence of belief-based propagation mechanisms the population may acquire a collective ‘memory’ of the fear of the disease that makes the population more resilient to future outbreaks This abundance of different dynamical behaviors clearly shows the importance of the behavior-disease perspective in the study of realistic progressions of infectious diseases and provides a chart for future studies and scenario analyses in data-driven epidemic models
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