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Modeling Approaches Toward Understanding Infectious Disease Transmission

  • Laura A. Skrip
  • Jeffrey P. TownsendEmail author
Chapter

Abstract

Long-standing neglected diseases continue to challenge our global health infrastructure, and emerging pathogens pose new threats worldwide. To inform prevention and response efforts, mathematical models of infectious disease dynamics are being increasingly applied. Here we explain how models can be developed to enhance our understanding and predictive power over population-level disease trends, by capturing both fundamental aspects of transmission and also the effects of medical and behavioral interventions. We review advances in transdisciplinary approaches of disease modeling and illustrate these advances with applications including community-based initiatives undertaken during the Ebola epidemic in West Africa and age-targeting of influenza vaccination in the USA. We further discuss how modern statistical inference facilitates the incorporation of data from behavioral sciences and epidemiology into models, highlighting how data-driven models can constitute powerful tools to inform and improve public health strategies.

Keywords

Mathematical modeling Disease transmission Infection Infectiousness Behavior Intervention Public health policy Population heterogeneity Infectious disease Model structure Disease dynamics Data Parameterization Epidemiology Epidemic Surveillance Distributions Statistics Statistical analysis Probabilistic analysis Uncertainty analysis Variables Parameters 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Epidemiology of Microbial DiseaseYale School of Public HealthNew HavenUSA
  2. 2.Department of BiostatisticsYale School of Public Health, Yale UniversityNew HavenUSA

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