Abstract
This research shows how a limited supply of antivirals can be distributed optimally between the hospitals and the market so that the attack rate is minimized and enough revenue is generated to recover the cost of the antivirals. Results using an individual based model find that prevalence elastic demand behavior delays the epidemic and change in the social contact network induced by isolation reduces the peak of the epidemic significantly. A microeconomic analysis methodology combining behavioral economics and agent-based simulation is a major contribution of this work. In this paper we apply this methodology to analyze the fairness of the stockpile distribution, and the response of human behavior to disease prevalence level and its interaction with the market.
We thank our external collaborators and members of the Network Dynamics and Simulation Science Laboratory (NDSSL) for their suggestions and comments. This work has been partially supported by NSF Nets Grant CNS-0626964, NSF HSD Grant SES-0729441, CDC Center of Excellence in Public Health Informatics Grant 2506055-01, NIH-NIGMS MIDAS project 5U01GM070694-05, NIH MIDAS project 2U01GM070694-07, NSF PetaApps Grant OCI-0904844, DTRA R&D Grant HDTRA1-0901-0017, DTRA CNIMS Grant HDTRA1-07-C-0113 and NSF NETS CNS-0831633.
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Chen, J., Marathe, A., Marathe, M. (2010). Coevolution of Epidemics, Social Networks, and Individual Behavior: A Case Study. In: Chai, SK., Salerno, J.J., Mabry, P.L. (eds) Advances in Social Computing. SBP 2010. Lecture Notes in Computer Science, vol 6007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12079-4_28
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