Multi-Coaffiliation Networks and Public Health Applications

Abstract

Infectious diseases are a global concern. The challenge for public health bodies relies upon optimizing the distribution of scarce or costly control measures to maximize their impact on the outbreak dynamics. Risk identification has focused on schools and child-care centers mainly because they represent dense masses of highly immunologically naive hosts for the pathogens. To advance the design of mitigation strategies, epidemiology researchers have broaden their perspective through the use of computational tools designed to provide decision support for multiple scenarios. To identify at-risk populations, we propose a computational algorithm that recreates a realistic social model of the school system of a selected study place. It is a known fact that childhood diseases are spread through the social contacts that occur in the classrooms while schools are in session. Through synthetic reconstruction, the algorithm generates a synthesized population database. The demographic simulations are created at the level of individuals, households and schools. Then a school to school network is built as a representation of the social model. The algorithm outputs a new graph B′, representing the Multi- Coaffiliation Network (MCN) with number of vertices of order O(S), where S represents the number of schools. The resulting weighted network includes a value associated with each school as a possible intervention location. The riskevaluation of the schools in the network can be derived in a wide range of applications in both research and public policy analysis.