Oscillatory Patterns in the Amount of Demand for Dental Visits: An Agent Based Modeling Approach
Journal of Artificial Societies and Social Simulation
There are some empirical evidences indicating that there is a collective complex oscillatory pattern in the amount of demand for dental visit at society level. In order to find the source of the complex cyclic behavior, we develop an agent-based model of collective behavior of routine dental check-ups in a social network. Simulation results show that demand for routine dental check-ups can follow an oscillatory pattern and the pattern's characteristics are highly dependent upon the structure of
... on the structure of the social network of potential patients, the population, and the number of e ective contacts between individuals. Such a cyclic pattern has public health consequences for patients and economic consequences for providers. The amplitude of oscillations was analyzed under di erent scenarios and for di erent network topologies. This allows us to postulate a simulation-based theory for the likelihood observing and the magnitude of a cyclic demand. Results show in case of random networks, as the number of contacts increases, the oscillatory pattern reaches its maximum intensity, for any population size. In case of ringing lattice networks, the amplitude of oscillations reduces considerably, when compared to random networks, and the oscillation intensity is strongly dependent on population. The results for small world networks is a combination of random and ring lattice networks. In addition, the simulation results are compared to empirical data from Google Trends for oral health related search queries in di erent United States cities. The empirical data indicates an oscillatory behavior for the level of attention to dental and oral health care issues. Furthermore, the oscillation amplitude is correlated with town's population. The data fits the case of random networks when the number of e ective contacts is about -for each person. These results suggest that our model can be used for a fraction of people deeply involved in Internet activities like Web-based social networks and Google search.