A brief set of lecture notes outlining the many aspects of the course is available here.

LAYOUT OF THE COURSE
This lecture course is divided into eleven sections. Each section generally comes in two parts. In the first part I'll describe the basic mechanisms, concepts and results, in the second part (generally on Thursdays) we will focus our attention on the recent literature to understand how these ideas are used in practice. Most publications listed in this section are available on my web page, any that are not are marked with an asterisk - you will have to photocopy these from the library. Those papers cited within the main text are for reference only.
Introduction Lecture 1
In this first lecture I'll explain how the course will work, and hopefully provide sufficient motivation for you to understand why mathematical modelling has a vital role to play in population dynamics and epidemiology.

Population Models Lectures 2 & 3
These basic models of the behaviour of simple populations utilise the standard tools of dynamical systems (eg stability etc). Particular attention is paid to what biological understanding can be gained and the assumptions that underlie the models. Major topics include the Logistic growth model, Predator-Prey models, and Lokta-Volterra competition.\\

Disease Models Lectures 4-6
The basic models of infectious disease dynamics (the SIS and SIR models) are reviewed. Again, attention is paid to what biological understanding can be gained and the assumptions that underlie the models. The heterogeneities that arise when these assumptions are relaxed are also discussed.

Temporal Forcing Lectures 7-9
The vast majority of organisms in temporal climates breed at fixed times of the year. Others (such as many insects) may die before next years young become adults. In such situations, discrete time models of their dynamics may offer the best description, the most famous example of this is the Nicholson-Bailey model of host-parasitoid interactions. Many disease models are affected by seasonal variations. For example colds and 'flu are most common in the winter, whereas disease of animals may be influenced by patterns of seasonal births. The most commonly studied time of seasonal forcing is the effects on childhood diseases of the opening and closing of schools.
Keeling, Rohani and Grenfell (2001) Seasonally-forced Disease Dynamics Explored as Switching Between Attractors Physica D 148 317-335
Finkenstadt, B. and Grenfell, B. (2000) Time series modelling of childhood diseases: a dynamical systems approach. J. Roy. Stat. Soc. C  49 187-205.
Grenfell, B.T., Wilson, K., Finkenstadt, B.F., Coulson, T.N., Murray, S., Albon, S.D., Pemberton, J.M., Clutton-Brock, T.H. and Crawley, M.J. (1998) Noise and determinism in synchronized sheep dynamics.  Nature394 674-677
Hassell, M.P. and Pacala, S.W. (1990) Heterogeneity and the dynamics of host-parasitoid interactions. Phil. Trans. Roy. Soc. Lond. B 330 203-220

Age/Risk Structure Lectures 10-12
Initially we concentrate on sexually transmitted diseases, where the number of different sexual partners determines the risk of infection and the risk of transmission. In such risk structured models the basic relationships no-longer hold, and we consider R₀ in detail. We also look at adding age-structure to the standard models, and consider why measles and whooping cough are childhood diseases. The implications of non-random mixing and the who acquires infection from whom matrix are discussed. We consider how the average age of infection can change and what effects this may have on disease severity, including the idea of "endemic stability". From an ecological perspective we consider the effects of size and age classification on the interaction of organisms. In particular the cannibalism of large adult fish on much smaller fry will be considered.
*Anderson, R.M., Medley, G.F., May, R.M. and Johnson, A.M. (1986) A Preliminary Study of the Transmission Dynamics of the Human Immunodeficiency Virus (HIV), the Causative Agent of AIDS  IMA J. Math. App. Med. Biol. 3 229-263.
*Schenzle, D. (1984) An age-structured model of pre- and post-vaccination measles transmission.  IMA. J. Math. App. Med. Biol. 1 169-191.
De Roos, A.M. and Persson, L. 2002 Size-dependent life-history traits promote catastrophic collapses of top predators. P.N.A.S. 99 12907-12912.
De Roos, A.M., Persson, L. and Thieme, H.R. 2003 Emergent Allee effects in top predators feeding on structured prey populations. Proc. Roy. Soc. Lond. B 270 611-618.
 

Multi-Host / Multi-Strain Lectures 13-15
So far all population models have considered the interaction of one or two species. Here we consider the interaction of three or more species, focusing on examples of plankton dynamics and lynx-hare cycles to illustrate the range of complex behaviour.
Diseases and hosts do not exist in isolation. Many diseases can infect multiple hosts, all hosts have many disease and strains of diseases that can infect them. Here we review the models necessary to understand such situations, concentrating primarily on the dynamics of strain structure.
Gupta S, Trenholme K, Anderson RM, and Day, K.P. (1994) Antigenic Diversity and the Transmission Dynamics of Plasmodium-Falciparum. Science 263 961-963
Gog J.R., and Swinton J. (2002) A status-based approach to multiple strain dynamics. J. Math. Biol.  44 169-184.
Rohani, P., Earn, D.J., Finkenstadt, B. and Grenfell, B.T. 1998 Population dynamic interference among childhood diseases Proc. Roy. Soc. B 265 2033-2041
Blasius, B., Huppert, A. and Stone, L. 1999 Complex dynamics and phase synchronization in spatially extended ecological systems Nature 399 354-359

Stochasticity Lectures 16-18
All the models we have considered so far are deterministic, clock-work models with no variability. In practice, populations are governed by chance with their dynamics described by a series of random events. We will consider a range of models that mimic this behaviour including event-driven models. We will see how randomness can influence the mean and variance of the dynamics, as well as leading to the risk of extinction for both animal and disease populations.
*Bartlett, M.S. (1956) Deterministic and Stochastic Models for recurrent epidemics. Proc. of the Third Berkley Symp. on Math. Stats. and Prob.  4  81-108.
Nasell, I. (2002) Stochastic models of some endemic infections Math. Biosci. 179 1-19.
Nasell, I. (2003) Moment closure and the stochastic logistic model Theo. Pop. Biol. 63 159-168.

Spatial Heterogeneity Lectures 19-24
The populations are aggregated and segregated at a variety of different scales. At the most local level, the interaction of individuals or the transmission of infection is generally by close contact -- we examine the travelling waves that arise in this situation. At a larger scale, humans (and many animals) form patchy distributions such that there are areas of high density (which are prone to disease) separated by regions of low density (which act as barriers to disease spread). A wide variety of modelling techniques and tools will be considered.
Murray, J.D., Stanley, E.A. and Brown, D.L. 1986 On the spatial spread of rabies by foxes. Proceedings of the Royal Society of London B 229 111-150
Rhodes, C.J. and Anderson, R.M. 1996 Evaluation of Epidemic thresholds in a lattice model of disease spread. Phys. Letters A 210 183-188
Keeling, M.J. 1999 The Effects of Local Spatial Structure on Epidemiological Invasions. Proc. Roy. Soc. Lond. B 266 859-869
Keeling, M.J. et. al. 2001 Dynamics of the 2001 UK Foot and Mouth Epidemic: Stochastic Dispersal in a Heterogeneous Landscape  Science 294 813-817
*Hassell, M.P., Comins, H. and May, R.M. 1991 Spatial Structure and Chaos in Insect Population Dynamics.  Nature 353 255-258
Hanski, I. 1998 Metapopulation dynamics Nature 396 41-49

Control Lecture 25-27
In this section we focus on human interventions and, in particular, how diseases can be effectively and efficiently controlled or even eradicated. This is a very applied subject area and we will consider several of the most popular methods of disease control: vaccination, quarantining, contract-tracing, ring-vaccination and ring-culling. These will be addressed in terms of efficiency and logistics, with reference to real situations. We will also consider methods to prevent the evolution of the multi-resistant ``super-bugs''.
Muller, J., Kretzschmar, M. and Dietz, K. 2000 Contact tracing in stochastic and deterministic epidemic models. Math. Biosci. 164 39-64
Eames, K.T.D. and Keeling, M.J. 2003 Contact tracing and disease control. Proc. Roy. Soc. Lond. B 270 2554-2560
Ferguson, N.M., Keeling, M.J., Edmunds, W.J., Gani, R., Grenfell, B.T., Anderson, R.M. and Leach, S. 2003 Planning for smallpox outbreaks Nature 425 681-685
Matthews, L., Haydon, D.T., Shaw, D.J., Chase-Topping, M.E., Keeling, M.J. and Woolhouse, M.E.J. (2003) Neighbourhood control policies and the spread of infectious diseases Proc. Roy. Soc. Lond. B 270 1659-1666
Muller, J., Schonfisch, B. and Kirkilionis, M 2000 Ring Vaccination J. Math. Biol. 41 143-171

MacroParasites Lecture 28
All infectious diseases that have so far been considered are microparasites such as viruses and bacteria. Another class of parasites also exist, the macroparasites. As the name suggests these are far larger and consequently have more complex dynamics. In particular, the burden (of number) of such parasites per host plays a vital role.

Biological Data Sources Lectures 29-30
While theoretical models give us an excellent understanding of the expected behaviour of ecological and epidemiological systems, good data sources are needed if our models are to be accurately parameterised and comparisons to observations made. Long temporal data sources are some-what rare, due to the time and expense of collecting them. However, a few notable examples exist and we will investigate the modelling work that is based on them.
Hudson, P.J., Dobson, A.P. and Newborn, D. 1998 Prevention of population cycles by parasite removal Science 282
Bjornstad, O.N. and Grenfell, B.T. 2001 Noisy clockwork: time series analysis of population fluctuations in animals. Science 293 638-643
Turchin, P., Oksanen, L., Ekerholm, P., and Henttonen, H. 2000 Are lemmings prey or predators? Nature 405
Blasius, B., Huppert, A. and Stone, L. 1999 Complex dynamics and phase synchronization in spatially extended ecological systems Nature 399 354-359
May, R.M. 1998 Population biology - The voles of Hokkaido Nature 396 409-410
Ranta, E. and Kaitala, V. 1997 Travelling waves in vole population dynamics Nature 390 456
Ranta, E., Kaitala, V., and Lundberg, P. 1997 The spatial dimension in population fluctuations  Science 278 1621-1623

Simple epidemic simulator (Java Program),
epidemiology lectures on the net (huge amount),
Emerging Infectious Diseases,
Centers for Disease Control and Prevention (USA),
Public Health Lab. (UK),
World Health Organization.