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Mathematical Studies on Human Disease Dynamics: Emerging Paradigms and Challenges
Edited by: Abba Gumel, Editor-in-Chief, University of Manitoba, Winnipeg, MB, Canada, Carlos Castillo-Chavez, Arizona State University, Tempe, AZ, Ronald E. Mickens, Clark Atlanta University, GA, and Dominic P. Clemence, North Carolina A&T State University, Greensboro, NC

Contemporary Mathematics
2006; 389 pp; softcover
Volume: 410
ISBN-10: 0-8218-3775-3
ISBN-13: 978-0-8218-3775-7
List Price: US$105
Member Price: US$84
Order Code: CONM/410
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This volume contains the proceedings of the AMS-IMS-SIAM Joint Summer Research Conference on Modeling the Dynamics of Human Diseases: Emerging Paradigms and Challenges, held in Snowbird, Utah, July 17-21, 2005.

The goal of the conference was to bring together leading and upcoming researchers to discuss the latest advances and challenges associated with the modeling of the dynamics of emerging and re-emerging diseases, and to explore various control strategies. The articles included in this book are devoted to some of the significant recent advances, trends, and challenges associated with the mathematical modeling and analysis of the dynamics and control of some diseases of public health importance. In addition to illustrating many of the diverse prevailing epidemiological challenges, together with the diversity of mathematical approaches needed to address them, this book provides insights on a number of topical modeling issues such as the modeling and control of mosquito-borne diseases, respiratory diseases, animal diseases (such as foot-and-mouth disease), cancer and tumor growth modeling, influenza, HIV, HPV, rotavirus, etc. This book also touches upon other important topics such as the use of modeling in homeland security and some review and new results on various modeling paradigms including network, stochastic and deterministic formulations together with the use of optimal control and related methods for evaluating control strategies.


Graduate students and research mathematicians interested in mathematical models of disease dynamics.

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