April 24, 2014
Summer 2010 Thematic Program on the Mathematics of Drug Resistance in Infectious Diseases

Coxeter Lecture Series

Professor Neil M Ferguson
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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