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 Summer
2010 Thematic Program on the Mathematics of Drug Resistance in Infectious
Diseases
Coxeter Lecture Series
Professor Neil M Ferguson
OBE, FMedSc
Director, MRC Centre for Outbreak Analysis and Modelling Imperial
College London
August 4-6, 2010
Fields Institute
Mathematical modelling of emerging infectious disease epidemics
and their control
Epidemic modelling has grown in prominence as a tool to assist
public health professionals and policy-makers to plan for and
respond to outbreaks of human and animal diseases. Recent examples
include Foot and Mouth Disease in livestock, SARS in humans, planning
for a severe H5N1 'bird flu' pandemic, and responding to the H1N1
'swine flu' pandemic last year. This series of lectures will review
recent progress in the field of epidemic modelling. I will discuss
how increasing computer power and expectations of public health
'consumers' of modelling have led to a trend towards dramatically
increased model complexity in the last 5 years, posing challenges
for model assessment and validation. Fortunately, methodological
progress in inference for complex models, plus vastly increased
availability of population and epidemiological data offers some
potential to meet those challenges. After reviewing developments
in model design and parameterisation (Lecture 1), I will discuss
how models have been used to inform public health policy making
during a range of outbreaks (Lecture 2), before focussing on how
modelling can be used to evaluate the risk posed by the evolution
of resistance to antiviral drugs during an influenza pandemic
(Lecture 3).
August 4, 2010
Modelling infectious disease outbreaks - recent progress
I will review how outbreak modelling has evolved over the last two decades,
and discuss the drivers leading to the more complex computational simulations
being increasingly used in preferences to simpler compartmental models of
disease transmission. The demand for increased model 'realism' and therefore
complexity poses challenges for model parameterisation and validation, so
I will give an overview of the data needs for current models and how application
of modern inferential methods is giving greater insight into the details
of transmission processes than ever before. I will discuss how data limitations,
intrinsic stochasticity plus uncertainties about disease biology, mechanisms
of transmission and the impact of controls limit our ability to predict
detailed patterns of epidemic spread. Throughout my lecture, I will draw
on the examples of work undertaken on pandemic influenza and other emerging
infectious disease outbreaks over the last few years.
August 5, 2010
The public health role of modelling in responding to emerging infectious
disease threats
I will give a personal view of how modelling can best be used to assist
public health policymakers in planning for and responding to emerging infectious
disease threats. Staff at the MRC Centre at Imperial College have worked
with policymakers around the world on a wide range of outbreaks, from Foot
and Mouth Disease in UK cattle in the UK in 2001, to H1N1 'swine flu' in
2009. I will initially discuss how modelling has been used to assist in
preparing for disease outbreaks, notably pandemic influenza, and the challenges
of estimating the likely population impact of public health interventions
(such as vaccines, antiviral treatment, school closure and other 'social
distancing' measures) from limited data. Of particular note is the ability
of modelling to give insight into the potential impact of combined - or
layered - interventions of different types. Targeted layered interventions
are now the mainstay of community mitigation planning for many developed
countries, and modelling has therefore played an important role in defining
pandemic plans. Giving examples from animal disease outbreaks, SARS in 2003
and pandemic influenza in 2009, I will then discuss the role of modelling
in outbreak response - in giving real-time assessment of an emerging outbreak
(notably assessing severity and speed of spread), generating projections
of epidemic trajectory and informing decision-making on appropriate and
effective control measures. I will discuss the difficulties faced in assessing
severity and predicting the spread of the H1N1 pandemic last year and the
wider challenges of real-time outbreak analysis, such as working with ever-changing
and incomplete data, and needing to draw preliminary conclusions when underlying
uncertainty is huge.
August 6, 2010
The potential impact of antiviral resistance during an influenza pandemic
In my last lecture I will focus on the issue of antiviral resistance during
closed epidemics, again taking pandemic influenza as the paradigm. I will
present new work which shows that previous assessments of the risk of antiviral
resistance in influenza pandemics have been over-pessimistic, for 2 reasons:
(a) previous simple models have often over-estimated the selection pressure
imposed by antiviral treatment; (b) spread of new, rare phenotypes is dramatically
slower when one accounts for host population structure than one would predict
by assuming homogenous mixing. In addition, I will examine how the final
impact of resistance during a closed epidemic depends on the transmissibility
of a sensitive and resistant virus, the mutation rate from sensitive to
resistant types and the level of seeding of both viral types at the start
of the epidemic. Non-pharmaceutical public health interventions are shown
to be able to either slow or hasten the spread of resistance depending on
their effectiveness. Delaying use of antivirals or sequential use of different
antivirals is shown to be effective at reducing the final impact of resistance.
Overall, providing the strains seeding a pandemic in a country are predominantly
drug-sensitive, I will argue that resistance is unlikely substantially reduce
the effectiveness of antivirals during the first wave of a pandemic, but
that intensive use of such drugs in the first wave can lead to a high frequency
of resistance in later epidemics.
Neil Ferguson is a professor of mathematical biology in the Division of Epidemiology,
Public Health, and Primary Care of the Medical School at Imperial College,
London. He is a world leader in the use of mathematical models in infectious
disease epidemiology, and published numerous influential scientific papers
on the effects of various interventions in the spread of disease. He has worked
on a wide variety of different diseases, including childhood infections, BSEm
vCJD, HIV, foot and mouth disease, and influenza.
His work on foot and mouth disease was particularly prominent, as it played
a central role in policy making during that outbreak in the UK. Professor
Ferguson is a member of the Pandemic Influenza Scientific Advisory Group in
the UK Department of Health, the UK Dept. of Environment Food and Rural Affairs
Science Advisory Council, and is on the Steering Committee for CBRN Modelling,
and the UK Home Office.
He is a Fellow of the Royal Statistical Society, and is on the editorial
boards of PLoS Computational Biology, Journal of the Royal Society Interface,
and is a founding editor of the journal Epidemics.
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For additional information contact thematic(PUT_AT_SIGN_HERE)fields.utoronto.ca
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