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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 952381, 16 pages
http://dx.doi.org/10.1155/2014/952381
Research Article

Conflicts of Interest during Contact Investigations: A Game-Theoretic Analysis

1Francis I. Proctor Foundation for Research in Ophthalmology, University of California, San Francisco, Box 0412, San Francisco, CA 94143-0412, USA
2Department of Epidemiology and Biostatistics, University of California, San Francisco, Box 0412, San Francisco, CA 94143-0412, USA
3Department of Ophthalmology, University of California, San Francisco, Box 0412, San Francisco, CA 94143-0412, USA

Received 22 November 2013; Revised 4 February 2014; Accepted 6 March 2014; Published 14 April 2014

Academic Editor: Chris Bauch

Copyright © 2014 Nicolas Sippl-Swezey et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The goal of contact tracing is to reduce the likelihood of transmission, particularly to individuals who are at greatest risk for developing complications of infection, as well as identifying individuals who are in need of medical treatment of other interventions. In this paper, we develop a simple mathematical model of contact investigations among a small group of individuals and apply game theory to explore conflicts of interest that may arise in the context of perceived costs of disclosure. Using analytic Kolmogorov equations, we determine whether or not it is possible for individual incentives to drive noncooperation, even though cooperation would yield a better group outcome. We found that if all individuals have a cost of disclosure, then the optimal individual decision is to simply not disclose each other. With further analysis of (1) completely offsetting the costs of disclosure and (2) partially offsetting the costs of disclosure, we found that all individuals disclose all contacts, resulting in a smaller basic reproductive number and an alignment of individual and group optimality. More data are needed to understand decision making during outbreak investigations and what the real and perceived costs are.