Description
In an increasingly globalized world, we face risks of outbreaks of infectious diseases, such as AIDS, Ebola, or new versions of the flu. Human behavior is known to have fundamental influence on infectious disease dynamics. This can come in a variety of forms, such as preventive behaviors (e.g., handwashing, mask wearing, vaccinations), avoidant behaviors (e.g., avoiding public transport or work), and management of disease behaviors (e.g., consulting medical experts). Especially avoidant behaviors, that is distancing oneself from a disease within a network, may affect the structure of social networks, resulting in different pathological pathways. Empirical evidence supporting such behavior can be found for different scenarios. People in Hong Kong and Singapore, for example, massively reduced their social contacts and travels during the 2003 SARS epidemic. In addition, individuals infected with H1N1 (swine flu) reported that they temporarily reduced their social contacts and broke weak ties for the duration of their infection.Past studies show an increasing interest in this interplay of social networks and infectious diseases in both sociological and epidemiological scholarship. Predictive models in epidemiology, for example, increasingly consider structural characteristics of underlying transmission networks. A common shortcoming of these models, however, is that network structure either remains static or is altered stochastically. Both of these approaches are problematic, as they neglect the fact that people adjust their behavior according to deliberate and emotional decision-making processes. From the literature we know that two main drivers for the adjustment of social behavior are the perceived probability of getting infected and the perceived severity of a disease. Thus, depending on individual risk perception, a risk-afraid person might prefer to avoid social contacts during a wave of influenza, while a risk-ignorant person might not alter social behavior
at all.
In our study presented here, we argue that social networking in the context of infectious disease is a trade-off between the well-being contacts create, the costs to maintain the social ties, and the physical harm an infected connection may cause. Further, we present a general model that allows to describe the complex interplay of behavioral reactions caused by (objective and subjective) threats of infectious diseases on network structure, and thus on the overall course of epidemics. By integrating widely accepted theories from epidemiology, sociology, and health psychology, the model describes how desirable network positions are dependent on social connections and their disease states. Using a simple yet relevant instance of our model within an agent-based simulation, we show that even minor structural changes in the network (due to distancing of agents from a disease) have tremendous effects on the attack rate and the duration of epidemics. These results show that the neglect of avoidant behaviors due to infectious diseases must lead to a skewed picture of current network-based models of infectious diseases. Finally, we describe how our model can be modified to fit to a large variety of infectious diseases and social scenarios. Further, we describe how network interventions can be tested either using single instances of our model, or with the use of multi-layer networks that integrate the role of health-relevant information and/or opinions.
| Period | 1 Jun 2019 |
|---|---|
| Event title | INAS: International Conference of Analytical Sociologists 2019 |
| Event type | Conference |
| Location | Saint Petersburg, Russian FederationShow on map |
| Degree of Recognition | International |