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Modeling Infectious Disease Dynamics in the Complex Landscape of Global Health Hans Heesterbeek Et Al

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Modeling Infectious Disease Dynamics in the Complex Landscape of Global Health Hans Heesterbeek Et Al RESEARCH ◥ REVIEW SUMMARY linear systems in which infections evolve and spread and where key events can be governed by unpredictable pathogen biology or human EPIDEMIOLOGY behavior. In this Review, we start with an ex- amination of real-time outbreak response using the West African Ebola epidemic as an Modeling infectious disease dynamics example. Here, the challenges range from un- ◥ derreporting of cases and in the complex landscape of ON OUR WEB SITE deaths, and missing infor- Read the full article mation on the impact of at http://dx.doi. control measures to under- global health org/10.1126/ standing human responses. science.aaa4339 The possibility of future .................................................. Hans Heesterbeek,* Roy M. Anderson, Viggo Andreasen, Shweta Bansal, zoonoses tests our ability Daniela De Angelis, Chris Dye, Ken T. D. Eames, W. John Edmunds, to detect anomalous outbreaks and to esti- Simon D. W. Frost, Sebastian Funk, T. Deirdre Hollingsworth, Thomas House, mate human-to-human transmissibility against Valerie Isham, Petra Klepac, Justin Lessler, James O. Lloyd-Smith, C. Jessica E. Metcalf, a backdrop of ongoing zoonotic spillover while Denis Mollison, Lorenzo Pellis, Juliet R. C. Pulliam, Mick G. Roberts, also assessing the risk of more dangerous Cecile Viboud, Isaac Newton Institute IDD Collaboration strains evolving. Increased understanding of the dynamics of infections in food webs and BACKGROUND: Despitemanynotablesuc- lenges for prevention and control. Faced with ecosystems where host and nonhost species cesses in prevention and control, infectious this complexity, mathematical models offer interact is key. Simultaneous multispecies diseases remain an enormous threat to human valuable tools for understanding epidemio- infections are increasingly recognized as a and animal health. The ecological and evolu- logical patterns and for developing and eval- notable public health burden, yet our under- tionary dynamics of pathogens play out on a uating evidence for decision-making in global standing of how different species of pathogens wide range of interconnected temporal, orga- health. interact within hosts is rudimentary. Patho- on March 12, 2015 nizational, and spatial scales that span hours gen genomics has become an essential tool for to months, cells to ecosystems, and local to glob- ADVANCES: During the past 50 years, the drawing inferences about evolution and trans- alspread.Somepathogensaredirectlytrans- study of infectious disease dynamics has ma- mission and, here but also in general, hetero- mitted between individuals of a single species, tured into a rich interdisciplinary field at the geneity is the major challenge. Methods that whereas others circulate among multiple hosts, intersection of mathematics, epidemiology, ecol- depart from simplistic assumptions about ran- need arthropod vectors, or persist in environ- ogy, evolutionary biology, immunology, sociol- dom mixing are yielding new insights into the mental reservoirs. Many factors, including ogy, and public health. The practical challenges dynamics of transmission and control. There increasing antimicrobial resistance, human con- range from establishing appropriate data col- is rapid growth in estimation of model param- nectivity, population growth, urbanization, en- lection to managing increasingly large volumes eters from mismatched or incomplete data, www.sciencemag.org vironmental and land-use change, as well as of information. The theoretical challenges re- and in contrasting model output with real- changing human behavior, present global chal- quire fundamental study of many-layered, non- world observations. New data streams on so- cial connectivity and behavior are being used, and combining data collected from very dif- ferent sources and scales presents important challenges. Available data All these mathematical endeavors have the potential to feed into public health policy and, Downloaded from Policy uild Scientific indeed, an increasingly wide range of models questions b understanding is being used to support infectious disease control, elimination, and eradication efforts. OUTLOOK: Mathematical modeling has the t potential to probe the apparently intractable p a complexity of infectious disease dynamics. Cou- d a fi pled to continuous dialogue between decision- t makers and the multidisciplinary infectious disease community, and by drawing on new Policy advice Scientific insights data streams, mathematical models can lay bare mechanisms of transmission and indicate Data collection new approaches to prevention and control that help to shape national and international pub- lic health policy.▪ Modeling for public health. Policy questions define the model’s purpose. Initial model design is based on current scientific understanding and the available relevant data. Model validation and fit to The list of author affiliations is available in the full article disease data may require further adaptation; sensitivity and uncertainty analysis can point to online. *Corresponding author. E-mail: [email protected] requirements for collection of additional specific data. Cycles of model testing and analysis thus lead Cite this article as H. Heesterbeek et al., Science 347, to policy advice and improved scientific understanding. aaa4339 (2015). DOI: 10.1126/science.aaa4339 1216 13 MARCH 2015 • VOL 347 ISSUE 6227 sciencemag.org SCIENCE RESEARCH ◥ nized at least 250 years ago when, in 1766, Daniel REVIEW Bernoulli published a mathematical analysis of the benefits of smallpox inoculation (then called variolation) (12). In the past 50 years, the study EPIDEMIOLOGY of infectious disease dynamics has grown into a rich interdisciplinary field. For example, decision- making for vaccination strategies increasingly Modeling infectious disease depends on model analyses in which infection dynamics are combined with cost data (Box 2, Influenza: prevention and control). In recent dynamics in the complex landscape decades, responses to major infectious disease outbreaks, including HIV, bovine spongiform of global health encephalopathy (BSE), foot-and-mouth disease (FMD), SARS, and pandemic and avian influ- Hans Heesterbeek,1*† Roy M. Anderson,2 Viggo Andreasen,3 Shweta Bansal,4 enza, have shown both the need for and capa- Daniela De Angelis,5 Chris Dye,6 Ken T. D. Eames,7 W. John Edmunds,7 bilities of models (Box 3, HIV: Test and treat Simon D. W. Frost,8 Sebastian Funk,4 T. Deirdre Hollingsworth,9,10 Thomas House,11 strategy). Model-based analysis of such outbreaks Valerie Isham,12 Petra Klepac,8 Justin Lessler,13 James O. Lloyd-Smith,14 also continually brings improvements in meth- C. Jessica E. Metcalf,15 Denis Mollison,16 Lorenzo Pellis,11 Juliet R. C. Pulliam,17,18 odology and data, emerging from the compari- Mick G. Roberts,19 Cecile Viboud,18 Isaac Newton Institute IDD Collaboration‡§ son of model prediction with observed patterns. For infectious agents important to public health, Despite some notable successes in the control of infectious diseases, transmissible a series of principles has emerged for modeling pathogens still pose an enormous threat to human and animal health. The ecological and infection dynamics (Table 1 and Box 4). The basic R evolutionary dynamics of infections play out on a wide range of interconnected temporal, reproduction number 0,forexample,isacentral organizational, and spatial scales, which span hours to months, cells to ecosystems, concept characterizing the average number of and local to global spread. Moreover, some pathogens are directly transmitted between secondary cases generated by one primary case individuals of a single species, whereas others circulate among multiple hosts, need in a susceptible population. This concept high- arthropod vectors, or can survive in environmental reservoirs. Many factors, including lights what must be measured to interpret observed increasing antimicrobial resistance, increased human connectivity and changeable human disease patterns and to quantify the impact of behavior, elevate prevention and control from matters of national policy to international selected control strategies (Fig. 1). challenge. In the face of this complexity, mathematical models offer valuable tools for Two fundamental properties of the world that synthesizing information to understand epidemiological patterns, and for developing shape infectious disease dynamics make com- quantitative evidence for decision-making in global health. putational tools key for understanding reality. The world is essentially a stochastic and highly 9 10 hirty-five years ago, it was believed that of an outbreak ( , ). Phylogenetic data shed 1 11 Faculty of Veterinary Medicine, University of Utrecht, the health burden of infectious diseases light on an additional layer of complexity ( ), as Utrecht, Netherlands. 2School of Public Health, Imperial was close to becoming insignificant as hy- will increased understanding of the human ge- College, London, UK. 3Roskilde University, Roskilde, giene, improved nutrition, drugs, and vac- nome in relation to susceptibility, infectiousness, Denmark. 4Georgetown University, Washington, DC, USA. T 5MRC Biostatistics Unit, Cambridge, UK. 6WHO, Geneva, cines brought about a steady decline in and its duration. At the same time, the develop- 7 1 Switzerland. Centre for the Mathematical Modelling of overall mortality ( ). In recent decades, however, ment of effective new vaccines remains a difficult Infectious Diseases, London School of Hygiene Tropical it has become clear that the
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