Description
The interplay of social networks and infectious diseases poses a highly complex problem. On the one hand, the presence of an infectious disease affects the social networking behavior of individual persons (e.g., people temporarily break ties to cure a disease or to minimize the risk of an infection). Further, a major factor for health-related network decisions is the individual perception of infection risks. On the other hand, each network decision (i.e., create, maintain, break a tie) modifies the structure of the corresponding social networks, and therefore the potential transmission routes of a disease. To find effective social network-based interventions (e.g., closure of public places, information campaigns using strategic points within the network), we need to have a better understanding of how social networks and infectious diseases co-evolve. Prior research on network epidemiology commonly relies on two simplifications (among others). First, social networks are considered static, neglecting the ongoing changes of social connections. Second, theories of social networking behavior consider only infected actors, neglecting the behavioral differences (e.g., risk behavior) between all individuals involved.To resolve the two aforementioned simplifications, we present an integrated model of social networking behavior, depending on individual risk behavior, and disease transmission dynamics. To this end, we make two assumptions. First, different social network structures provide different utilities. Second, individuals seek to maximize their social well-being, while minimizing physical harm, considering the information available and their limited cognitive abilities. We hypothesize that in the presence of an infectious disease different perceptions of risks lead to different networking decisions. With the use of an agent-based computer simulation we investigate the likelihood of getting infected with an infectious disease depending on the risk behavior of individuals, and the time a disease remains in the network depending on the composition of risk behaviors within the network.
In our presentation, we show the results of the aforementioned simulation. Our findings contribute to the social-behavioral basis for a bigger project to understand and formalize the co-evolution of social networks and infectious diseases. This knowledge and the corresponding formal models will allow describing and test the effectivity of social network-based interventions, providing valuable contributions to health policymaking and the scientific field of network epidemiology.
| Period | 29 Jun 2018 |
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| Event title | INSNA Sunbelt Conference |
| Event type | Conference |
| Location | Utrecht, NetherlandsShow on map |
| Degree of Recognition | International |