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4 - The Nature of Plagues 2013–14: A Year of Living Dangerously
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- By Angela McLean, All Souls College in Oxford
- Edited by Jonathan L. Heeney, University of Cambridge, Sven Friedemann, University of Bristol
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- Plagues
- Published online:
- 24 March 2017
- Print publication:
- 09 February 2017, pp 92-113
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Summary
The 12 months from June 2013 to May 2014 were, in many ways, typical in the emerging infectious disease events that occurred. There were no huge shocks, no massive outbreaks nor new pandemics, but every month there were important events and together the year's events form a good illustration of what is a ‘normal’ rhythm of events for emerging infectious diseases. However, after May 2014 the Ebola epidemic in West Africa (described, in its infancy, under ‘March’ in this chapter) rapidly expanded to become a very large epidemic, illustrating how quickly small outbreaks can become very large problems given circumstances that favour human to human transmission and rapid spread.
Whilst many people think of ‘emerging infections’ as only the brand new infections like SARS and HIV, the definition of emerging infections is broader and includes five types of infections that are in some sense ‘new’. Table 4.1 describes those five types and gives examples of each from the past.
In England, Public Health England (an agency of the Department of Health) routinely gathers up evidence about new infectious disease both nationally and internationally. This ‘horizon scanning’ activity is an important part of identifying new infectious hazards that may pose a risk to public health. Each month Public Health England, along with other government bodies, publishes a two-page summary of notable events of public health significance. These summaries are widely circulated in government and academia and are publically available. They form both an excellent warning of current events and a record of how events unfold over months and years.
In this article I have picked one event from each of the past twelve months to illustrate the ‘normal’ rhythm of incidents. Those events have been chosen to illustrate the five types of emerging infectious disease events. They include the three events of 2013–14 that are most likely to trigger substantial, global problems in the future: the ongoing MERS-coronavirus outbreak in the Middle East (July 2013), the ongoing zoonotic cases of Avian Influenza in China (February 2014) and the re-emergence of Polio in early 2014 (May 2014). Despite the ongoing fears about a devastating influenza pandemic, the biggest realised threat from emerging infections continues to be the evolution of antimicrobial resistance. This is a slow, chronic problem that is happening everywhere all the time and therefore never triggers a single ‘event’.
24 - Evolution of Vaccine-resistant Strains of Infectious Agents
- Edited by Ulf Dieckmann, International Institute for Applied Systems Analysis, Austria, Johan A. J. Metz, Universiteit Leiden, Maurice W. Sabelis, Universiteit van Amsterdam, Karl Sigmund, Universität Wien, Austria
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- Book:
- Adaptive Dynamics of Infectious Diseases
- Published online:
- 15 January 2010
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- 11 April 2002, pp 339-346
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Summary
Introduction
Vaccination is one of the most notable successes of modern medicine. Smallpox has been eradicated, and many serious infectious diseases of childhood have been brought under control, with a vast reduction in the associated morbidity and mortality. To achieve this required placing a huge selection pressure upon the associated pathogens. Despite this pressure, there has been little evolution of the pathogen strains that escape from vaccine-induced immunity.
In this chapter, I first present a modeling approach that allows consideration of competition between strains of pathogens and their responses to changes in the balance of competition that are imposed by a vaccination campaign. This framework allows the calculation of conditions that would allow the emergence of a vaccineresistant strain. The numerical simulation of the evolution of vaccine resistance gives interesting insights into the time scale over which it might occur. Finally, I discuss four case studies from infectious diseases of humans.
Theoretical Framework
This section describes the basic theoretical framework on which the discussion in this chapter is built.
Basic reproduction ratio
The community-level impact of vaccines is best considered within the context of the basic reproduction ratio R0, which is defined as the number of secondary cases caused by one infectious individual introduced into a community in which everyone is susceptible. R0 can be generalized to Rp, the number of secondary cases caused by one infectious individual introduced into a community where a fraction p have been vaccinated and everyone else is susceptible.
Lifespan of human T lymphocytes
- Edited by Valerie Isham, University College London, Graham Medley, University of Warwick
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- Models for Infectious Human Diseases
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- 04 August 2010
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- 29 March 1996, pp 191-192
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Summary
The lifespan of T lymphocytes is of particular interest because of their central role in immunological memory. Is the recall of a vaccination or early infection, which may be demonstrated clinically up to 50 years after antigen exposure, retained by a long-lived cell, or its progeny? Using the observation that T lymphocyte expression of isoforms of CD45 corresponds with their ability to respond to recall antigens, we have investigated the lifespan of both CD45RO (the subset containing responders, or ‘memory’ cells) and CD45RA (the unresponsive, or ‘naive’ subset) lymphocytes in a group of patients after radiotherapy (Michie et al. 1992). We have found a rapid loss of unstable chromosomes (which result in cell death in mitosis) from the CD45RO but not the CD45RA pool. Immunological memory therefore apparently resides in a population with a more rapid rate of division. The survival curves for the two populations are best described by a model in which there is also reversion in vivo from the CD45RO to the CD45RA phenotype. Expression of CD45RO in T cells may therefore be reversible. Further data showing survival curves of T lymphocytes with stable radiation damage (passed to one daughter cell during mitosis) is also considered. These curves show very little loss of such cells. The difference between the two populations (stable and unstable damage) allows an estimate of their proliferation rates and death rates. These parameter estimates may be of interest to people modelling the dynamics of the immune response as they give some rough indicators of the timescales on which T lymphocytes turn over (McLean and Michie 1993).
Invited Discussion
- Edited by Valerie Isham, University College London, Graham Medley, University of Warwick
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- Book:
- Models for Infectious Human Diseases
- Published online:
- 04 August 2010
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- 29 March 1996, pp 181-183
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What's so special about within-host dynamics?
When an epidemiology conference hosts a session on ‘within-host dynamics’, three questions immediately come to a discussant's mind: ‘Why are we doing this?’, ‘What are we doing here?’ and ‘What difference does being within a host make?’ The first of these three questions is answered by the quality of the papers presented in this session. There are many fascinating questions about the pathogenesis of infectious diseases, and about the dynamics of host responses to infectious organisms. These questions often involve highly nonlinear interactions between host and pathogen within the host organism. The rigour and clarity of thought required by mathematical description of such interactions is a great aid in developing an intuitive understanding of which processes are important, and of what patterns those processes might generate.
The subject matter of the four talks: two on HIV, one on malaria and one on schistosomiasis is probably a fair representation of the field. The enigma of HIV's pathogenesis has prompted many theoretical (and empirical) investigations. Nowak's theory is one elegant example of the numerous theories proposed to explain the long period between infection with HIV and illness with AIDS (reviewed in McLean 1993). In contrast to the care and rigour with which Nowak's theory has been expounded, some of the ‘verbal theories’ of HIV's pathogenesis are classic examples of why biologists ought to make mathematical models; so that they can see when the predictions made by their verbal models simply cannot be matched up with the patterns they aim to explain. A cogent argument for the use of mathematical models in an exploratory fashion by biologists is given by Hillis (1993).