Science
13 March 2015 vol 347, issue 6227, pages 1169-1284
http://www.sciencemag.org/current.dtl

Review
Modeling infectious disease dynamics in the complex landscape of global health
Hans Heesterbeek1,*, Roy M. Anderson2, Viggo Andreasen3, Shweta Bansal4, Daniela De Angelis5, Chris Dye6, Ken T. D. Eames7, W. John Edmunds7, Simon D. W. Frost8, Sebastian Funk4, T. Deirdre Hollingsworth9,10, Thomas House11, Valerie Isham12, Petra Klepac8, Justin Lessler13, James O. Lloyd-Smith14, C. Jessica E. Metcalf15, Denis Mollison16, Lorenzo Pellis11,
Juliet R. C. Pulliam17,18, Mick G. Roberts19, Cecile Viboud18, Isaac Newton Institute IDD Collaboration
Author Affiliations
1Faculty of Veterinary Medicine, University of Utrecht, Utrecht, Netherlands.
2School of Public Health, Imperial College, London, UK.
3Roskilde University, Roskilde, Denmark.
4Georgetown University, Washington, DC, USA.
5MRC Biostatistics Unit, Cambridge, UK.
6WHO, Geneva, Switzerland.
7Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene Tropical Medicine, London, UK.
8University of Cambridge, Cambridge, UK.
9School of Life Sciences, University of Warwick, UK.
10School of Tropical Medicine, University of Liverpool, UK.
11Warwick Mathematics Institute, University of Warwick, Coventry, UK.
12Department of Statistical Science, University College London, London, UK.
13Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
14Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA.
15Department of Zoology, University of Oxford, Oxford, UK, and Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
16Heriot-Watt University, Edinburgh, UK.
17Department of Biology–Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.
18Division of International Epidemiology and Population Studies, Fogarty International Center, NIH, Bethesda, MD, USA.
19Institute of Natural and Mathematical Sciences, Massey University, Auckland, New Zealand.
Abstract
BACKGROUND
Despite many notable successes in prevention and control, infectious diseases remain an enormous threat to human and animal health. The ecological and evolutionary dynamics of pathogens play out on a wide range of interconnected temporal, organizational, and spatial scales that span hours to months, cells to ecosystems, and local to global spread. Some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or persist in environmental reservoirs. Many factors, including increasing antimicrobial resistance, human connectivity, population growth, urbanization, environmental and land-use change, as well as changing human behavior, present global challenges for prevention and control. Faced with this complexity, mathematical models offer valuable tools for understanding epidemiological patterns and for developing and evaluating evidence for decision-making in global health.
ADVANCES
During the past 50 years, the study of infectious disease dynamics has matured into a rich interdisciplinary field at the intersection of mathematics, epidemiology, ecology, evolutionary biology, immunology, sociology, and public health. The practical challenges range from establishing appropriate data collection to managing increasingly large volumes of information. The theoretical challenges require fundamental study of many-layered, nonlinear systems in which infections evolve and spread and where key events can be governed by unpredictable pathogen biology or human behavior. In this Review, we start with an examination of real-time outbreak response using the West African Ebola epidemic as an example. Here, the challenges range from underreporting of cases and deaths, and missing information on the impact of control measures to understanding human responses. The possibility of future zoonoses tests our ability to detect anomalous outbreaks and to estimate human-to-human transmissibility against a backdrop of ongoing zoonotic spillover while also assessing the risk of more dangerous strains evolving. Increased understanding of the dynamics of infections in food webs and ecosystems where host and nonhost species interact is key. Simultaneous multispecies infections are increasingly recognized as a notable public health burden, yet our understanding of how different species of pathogens interact within hosts is rudimentary. Pathogen genomics has become an essential tool for drawing inferences about evolution and transmission and, here but also in general, heterogeneity is the major challenge. Methods that depart from simplistic assumptions about random mixing are yielding new insights into the dynamics of transmission and control. There is rapid growth in estimation of model parameters from mismatched or incomplete data, and in contrasting model output with real-world observations. New data streams on social connectivity and behavior are being used, and combining data collected from very different sources and scales presents important challenges.
All these mathematical endeavors have the potential to feed into public health policy and, indeed, an increasingly wide range of models is being used to support infectious disease control, elimination, and eradication efforts.
OUTLOOK
Mathematical modeling has the potential to probe the apparently intractable complexity of infectious disease dynamics. Coupled to continuous dialogue between decision-makers and the multidisciplinary infectious disease community, and by drawing on new data streams, mathematical models can lay bare mechanisms of transmission and indicate new approaches to prevention and control that help to shape national and international public health policy.