Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases

1. Front Matter

   Pages i-ix

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2. A Reality of Its Own

   • Richard Rothenberg

   Pages 1-4

3. Modeling the Impact of Behavior Change on the Spread of Ebola

   • Jessica R. Conrad, Ling Xue, Jeremy Dewar, James M. Hyman

   Pages 5-23

4. A Model for Coupled Outbreaks Contained by Behavior Change

   • John M. Drake, Andrew W. Park

   Pages 25-37

5. Real-Time Assessment of the International Spreading Risk Associated with the 2014 West African Ebola Outbreak

   • Ana Pastore-Piontti, Qian Zhang, Marcelo F. C. Gomes, Luca Rossi, Chiara Poletto, Vittoria Colizza et al.

   Pages 39-56

6. Modeling the Case of Early Detection of Ebola Virus Disease

   • Diego Chowell, Muntaser Safan, Carlos Castillo-Chavez

   Pages 57-70

7. Modeling Ring-Vaccination Strategies to Control Ebola Virus Disease Epidemics

   • Gerardo Chowell, Maria Kiskowski

   Pages 71-87

8. Evaluating the Number of Sickbeds During Ebola Epidemics Using Optimal Control Theory

   • Eunok Jung, Jonggul Lee, Gerardo Chowell

   Pages 89-101

9. Inverse Problems and Ebola Virus Disease Using an Age of Infection Model

   • Alexandra Smirnova, Linda DeCamp, Hui Liu

   Pages 103-121

10. Assessing the Efficiency of Movement Restriction as a Control Strategy of Ebola

    • Baltazar Espinoza, Victor Moreno, Derdei Bichara, Carlos Castillo-Chavez

    Pages 123-145

11. Patch Models of EVD Transmission Dynamics

    • Bruce Pell, Javier Baez, Tin Phan, Daozhou Gao, Gerardo Chowell, Yang Kuang

    Pages 147-167

12. From Bee Species Aggregation to Models of Disease Avoidance: The Ben-Hur effect

    • K. E. Yong, E. Díaz Herrera, C. Castillo-Chavez

    Pages 169-185

13. Designing Public Health Policies to Mitigate the Adverse Consequences of Rural-Urban Migration via Meta-Population Modeling

    • Zhilan Feng, Yiqiang Zheng, Nancy Hernandez-Ceron, Henry Zhao

    Pages 187-206

14. Age of Infection Epidemic Models

    • Fred Brauer

    Pages 207-220

15. Optimal Control of Vaccination in an Age-Structured Cholera Model

    • K. Renee Fister, Holly Gaff, Suzanne Lenhart, Eric Numfor, Elsa Schaefer, Jin Wang

    Pages 221-248

16. A Multi-risk Model for Understanding the Spread of Chlamydia

    • Asma Azizi, Ling Xue, James M. Hyman

    Pages 249-268

17. The 1997 Measles Outbreak in Metropolitan São Paulo, Brazil: Strategic Implications of Increasing Urbanization

    • José Cassio de Moraes, Maria Claudia Corrêa Camargo, Maria Lúcia Rocha de Mello, Bradley S. Hersh, John W. Glasser

    Pages 269-289

18. Methods to Determine the End of an Infectious Disease Epidemic: A Short Review

    • Hiroshi Nishiura

    Pages 291-301

19. Statistical Considerations in Infectious Disease Randomized Controlled Trials

    • Matthew J. Hayat

    Pages 303-312

20. Epidemic Models With and Without Mortality: When Does It Matter?

    • Lisa Sattenspiel, Erin Miller, Jessica Dimka, Carolyn Orbann, Amy Warren

    Pages 313-327

The contributions by epidemic modeling experts describe how mathematical models and statistical forecasting are created to capture the most important aspects of an emerging epidemic.Readers will discover a broad range of approaches to address questions, such as

• Can we control Ebola via ring vaccination strategies?
  
• How quickly should we detect Ebola cases to ensure epidemic control?
  
•  What is the likelihood that an Ebola epidemic in West Africa leads to secondary outbreaks in other parts of the world?  
• When does it matter to incorporate the role of disease-induced mortality on epidemic models?
•  What is the role of behavior changes on Ebola dynamics? 
• How can we better understand the control of cholera or Ebola using optimal control theory?
• How should a population be structured in order to mimic the transmission dynamics of diseases such as chlamydia, Ebola, or cholera?
• How can weobjectively determine the end of an epidemic?
• How can we use metapopulation models to understand the role of movement restrictions and migration patterns on the spread of infectious diseases?
  
• How can we capture the impact of household transmission using compartmental epidemic models?
  
• How could behavior-dependent vaccination affect the dynamical outcomes of epidemic models? 
  

The derivation and analysis of the mathematical models addressing these questions provides a wide-ranging overview of the new approaches being created to better forecast and mitigate emerging epidemics. 

This book will be of interest to researchers in the field of mathematical epidemiology, as well as public health workers.

• Emerging infectious diseases
• Epidemic
• Epidemic forecasting
• Infectious disease dynamics
• Mathematical modeling
• Statistical inference
• infectious diseases

“This book focuses on mathematical and statistical modeling to capture the important aspects of emerging epidemics that can help public health workers and researchers to better understand the spread of infections and reduce the uncertainty of the estimates of disease prevalence, as well as to help evaluate the potential effectiveness of different approaches for bringing an epidemic under control. … recommended to researchers in the field of mathematical epidemiology and public health workers who are involved in epidemic disease control.” (Hemang B. Panchal, Doody's Book Reviews, November, 2016)

• School of Public Health, Georgia State University, Atlanta, USA

  Gerardo Chowell

• Department of Mathematics, Tulane University, New Orleans, USA

  James M. Hyman

Gerardo Chowell is an associate professor and a Second Century Initiative Scholar (2CI) in the School of Public Health at Georgia State University in Atlanta. His research program includes the development and application of quantitative approaches for understanding the transmission dynamics and control of infectious diseases including influenza, Ebola, and dengue fever. His work has appeared in high-impact journals including The New England Journal of Medicine, PLOS Medicine, and BMC Medicine, and has been cited by major media outlets including the Washington Post and TIME magazine.

James (Mac) Hyman has developed and analyzed mathematical models for the transmission of HIV/AIDs, influenza, malaria, dengue fever, chikungunya, and infections.  His current focus is to identify approaches where these models can help public health workers be more effective in mitigating the impact of emerging diseases.  He was aresearch scientist at Los Alamos National Laboratory for over thirty years, is a past president of the Society for Industrial and Applied Mathematics (SIAM),  and now holds the Phillips Distinguished Chair in Mathematics at Tulane University.

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