A Novel Framework for Complex Networks and Chronic Diseases

Abstract

Complex networks have provided a wealth of information regarding infectious diseases, for example by understanding how the network structure impacts the basic reproduction number or immunization strategies. However, researchers have struggled to translate this knowledge to chronic diseases, where social networks are at play but broad societal factors also have an important role. This translation is becoming urgent given the increasing prevalence, and the escalating healthcare costs, of conditions such as obesity. In this paper,we provide a mathematical framework that enables researchers to represent both the network and societal aspects of chronic disease, thereby facilitating this translation effort. Our framework uses Complex Networks to represent the population,where influences between neighboring nodes are modelled through Fuzzy Cognitive Maps that account for societal effects. Applying our framework to real-world cases, possibly through processes such as Group Model Building, may facilitate the better direction of policy towards the management of chronic diseases.

Research funded by the Canadian Institutes of Health Research (MT-10574). We are indebted to D.T. Finegood, D. Grace and V.K. Mago for having shared ideas that were to later shape this work. We thank R. Dorrell for his thorough feedback, and D. Grace for his suggestions.