Diffusion and social networks : Revisiting Medical innovation with agents

In this paper, we reanalyze Medical Innovation, the classic study on diffusion of a new drug Tetracycline, by Coleman, Katz and Menzel (1966). Medical Innovation and the subsequent reanalyzes of the original data failed to capture the complexity of events involved in the diffusion process. In this paper, for the first time, we address this limitation by combining Agent-Based Modelling (ABM) and network analysis. Based on the findings of Coleman et. al. (1966) study, we develop a diffusion model, Gammanym, in which Doctors' adoption thresholds are decremented by information from their social and professional networks as well as pharmaceutical company supplying Tetracycline. The cumulative adoption curves are derived for three sets of initial conditions, based on which network topology and evolution of uptake are analyzed. Averaged over an ensemble of 100 runs, clustering coefficient and average shortest path length indicate that social networks depicted in Gammanym are random graphs. Evolution of uptake suggests that although the degree of external influence in terms of marketing strategies adopted by the pharmaceutical company does not have impact on the network structure, the speed of diffusion is largely determined by it.