Denis Mollison

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Despite some notable successes in the control of infectious diseases, transmissible pathogens still pose an enormous threat to human and animal health. The ecological and evolutionary dynamics of infections play out on a wide range of interconnected temporal, organizational, and spatial scales, which span hours to months, cells to ecosystems, and local to(More)
This paper considers metapopulation models in the general sense, i.e. where the population is partitioned into sub-populations (groups, patches,...), irrespective of the biological interpretation they have, e.g. spatially segregated large sub-populations, small households or hosts themselves modelled as populations of pathogens. This framework has(More)
This paper reviews the basic components of epidemic models, and discusses some of the different ways of combining them, and relations between the resulting models. The fundamental aim is to help understanding of the relation between assumptions and the resulting dynamics: because without such understanding even a model which fits data perfectly can be of no(More)
Fine and Clarkson used a discrete-time epidemic model with variable transmission parameter to analyze measles data for England and Wales for 1950-1965, during the time of biennial epidemics. Their model seems to provide a convincing fit when its parameters are estimated from these data. In particular, they obtained nearly equal estimates for the variable(More)
Infectious disease incidence data are increasingly available at the level of the individual and include high-resolution spatial components. Therefore, we are now better able to challenge models that explicitly represent space. Here, we consider five topics within spatial disease dynamics: the construction of network models; characterising threshold(More)
Interest has recently revived in the use of simple models for epidemic diseases. In particular, Anderson et al. have introduced an improved simple differential equation model for diseases such as fox rabies which regulate the population density of their host. Here I describe how such apparently simple models can be dissected into their basic components.(More)
The most basic stochastic epidemic models are those involving global transmission, meaning that infection rates depend only on the type and state of the individuals involved, and not on their location in the population. Simple as they are, there are still several open problems for such models. For example, when will such an epidemic go extinct and with what(More)
This work explores the success of pair approximations in capturing local correlations and the spatial structure of population contact networks, especially in respect of the rate of spread of epidemics. Networks of interest range from the local extreme where interactions are only between nearest neighbours in some low dimensional space, and the(More)
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