Inverse Problems and Ebola Virus Disease Using an Age of Infection Model
Parameter estimation problems in ordinary and partial differential equations constitute a large class of models described by ill-posed operator equations. A considerable number of such problems come from epidemiology and infectious disease modeling, with
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hematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases
Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases
Gerardo Chowell James M. Hyman •
Editors
Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases
123
Editors Gerardo Chowell School of Public Health Georgia State University Atlanta, GA USA
ISBN 978-3-319-40411-0 DOI 10.1007/978-3-319-40413-4
James M. Hyman Department of Mathematics Tulane University New Orleans, LA USA
ISBN 978-3-319-40413-4
(eBook)
Library of Congress Control Number: 2016942897 © Springer International Publishing Switzerland 2016 Chapter 17 was created within the capacity of an US governmental employment. US copyright protection does not apply. This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG Switzerland
Preface
Mathematical modelers are joining with biological, epidemiological, behavioral, and social science studies to produce better projections and better understanding of the transmission dynamics of infectious diseases. They are working with public health workers to create new tools for devising effective strategies to minimize the emergence, impact, and spread of epidemics. For these tools to be useful and used, the decision-makers must fully understand the assumptions, such as any behavior changes of the population during an epidemic, used in defining the model and how sensitive the model predictions, such as the number of people infected, depend upon these assumptions. That is, a clear description of the model formulation and sensitivity analysis of the predictions are both necessary to quantify the uncertainty in the model forecasts. This collection of articles by epidemic modeling experts describe how these models are created to capture the most important aspects of an emerging epidemic. It provides
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