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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 785752, 17 pages
Research Article

Computational Modeling of Interventions and Protective Thresholds to Prevent Disease Transmission in Deploying Populations

1MathEcology, Phoenix, AZ 85086, USA
2piTree Software, Metuchen, NJ 08840, USA
3Military Vaccine Agency (MILVAX), Defense Health Headquarters, Falls Church, VA 22042, USA

Received 28 February 2014; Revised 5 May 2014; Accepted 7 May 2014; Published 9 June 2014

Academic Editor: Thierry Busso

Copyright © 2014 Colleen Burgess 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.


Military personnel are deployed abroad for missions ranging from humanitarian relief efforts to combat actions; delay or interruption in these activities due to disease transmission can cause operational disruptions, significant economic loss, and stressed or exceeded military medical resources. Deployed troops function in environments favorable to the rapid and efficient transmission of many viruses particularly when levels of protection are suboptimal. When immunity among deployed military populations is low, the risk of vaccine-preventable disease outbreaks increases, impacting troop readiness and achievement of mission objectives. However, targeted vaccination and the optimization of preexisting immunity among deployed populations can decrease the threat of outbreaks among deployed troops. Here we describe methods for the computational modeling of disease transmission to explore how preexisting immunity compares with vaccination at the time of deployment as a means of preventing outbreaks and protecting troops and mission objectives during extended military deployment actions. These methods are illustrated with five modeling case studies for separate diseases common in many parts of the world, to show different approaches required in varying epidemiological settings.