Behavioral Epidemiology of Infectious Diseases: An Overview

Abstract

The focus of the growing discipline of behavioral epidemiology (BE) of infectious diseases is on individual behavior as a key determinant of infection trajectories. This overview departs from the central, but static, role of human behavior in traditional mathematical models of infection to motivate the importance of including behavior into epidemiological models. Our aim is threefold. First, we attempt to motivate the historical and cultural background underpinning the BE revolution, focusing on the issue of rational opposition to vaccines as a natural endpoint of the changed relation between man and disease in modern industrialized countries. Second, we review those contributions, from both mathematical epidemiology and economics, that forerun the current "epidemic" of studies on BE. Last, we offer a more detailed overview of the current epidemic phase of BE studies and, still motivated by the issue of immunization choices, introduce some baseline ideas and models.

Statistical physics of vaccination

“…It is this fine tuning of epidemiological models that eventually raised the key question of behavioral epidemiology: given that humans are not "particles", which is then the role they play for infection transmission and prevention? [80].…”

“…The second vein yielded to the literature on behavioral change triggered by the HIV/AIDS threat since the 1980s. As noted in [80] "the combination of a long incubation period, with difficult and costly treatment, and the lack of a vaccine, have made instilling preventive behavior through the dissemination of information on risky behavior with respect to sexual or intra-venous drug use the main control strategy, especially in poor resource settings." In this situation, where reliable data on individuals' responses to the spread of epidemics were mostly missing, mathematical modeling played a pioneering role in the understanding of the effects of behavior change on HIV dynamics, including the effect of prevalence-dependent switching to lower risk groups, reducing contact rates after screening or treatment, prevalence-dependent sexual mixing patterns, including the warning that availability of effective therapies and protective vaccine might increases disease severity by raising at-risk behavior [111,[482][483][484][485][486][487].…”

The economics of vaccination

“…models in which agents either behave in a boundedly rational manner, or in which information about diseases or the value of vaccination spreads gradually via word-of-mouth learning (see e.g. Medlock et al, 2009;d'Onofrio et al, 2013;Bauch et al, 2013;Fenichel and Wang, 2013). These types of approaches seem like the natural next step once the more stylized models of economic epidemiology have been well understood.…”

The impacts of simultaneous disease intervention decisions on epidemic outcomes

“…Coupled disease-behaviour models combine human decision making behaviour with traditional transmission dynamics, helping to capture an additional, and often important, aspect of disease spread (Funk et al, 2010;Bauch et al, 2013;Wang et al). Behaviourally based models that incorporate NPIs and social distancing during an outbreak show that these practices can lower the attack rate of a disease (Del Valle et al, 2005;Reluga;Funk et al, 2008;Rizzo et al, 2014;Bagnoli et al 2007;Fenichel et al;Poletti et al, 2009Poletti et al, , 2012.…”