DOI: 10.5176/2251-3833_GHC12.56
Authors: Olivia Loza, Iris Gomez-Lopez and Armin R. Mikler
Abstract: Infectious diseases are a global concern. Thechallenge for public health bodies relies upon optimizing thedistribution of scarce or costly control measures to maximizetheir impact on the outbreak dynamics. Risk identification hasfocused on schools and child-care centers mainly because theyrepresent dense masses of highly immunologically naive hostsfor the pathogens. To advance the design of mitigation strategies,epidemiology researchers have broaden their perspectivethrough the use of computational tools designed to providedecision support for multiple scenarios.To identify at-risk populations, we propose a computationalalgorithm that recreates a realistic social model of the schoolsystem of a selected study place. It is a known fact thatchildhood diseases are spread through the social contacts thatoccur in the classrooms while schools are in session. Throughsynthetic reconstruction, the algorithm generates a synthesizedpopulation database. The demographic simulations are createdat the level of individuals, households and schools. Then aschool to school network is built as a representation of thesocial model. The algorithm outputs a network B0 with numberof vertices of order O(S), where S represents the numberof schools. The resulting weighted network includes a valueassociated with each school as a possible intervention location.The risk-evaluation of the schools in the network can beleveraged in a wide range of applications in both researchand public policy analysis.
Keywords: Computational Epidemiology; Algorithms; AffiliationNetworks
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