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- D Mollison
- Mathematical biosciences
- 1991

This paper describes the use of linear deterministic models for examining the spread of population processes, discussing their advantages and limitations. Their main advantages are that their assumptions are relatively transparent and that they are easy to analyze, yet they generally give the same velocity as more complex linear stochastic and nonlinear… (More)

- Hans Heesterbeek, Roy M Anderson, +19 authors Cecile Viboud
- Science
- 2015

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)

- D Mollison, V Isham, B Grenfell
- Journal of the Royal Statistical Society. Series…
- 1994

"The problems of understanding and controlling disease raise a range of challenging mathematical and statistical research topics, from broad theoretical issues to specific practical ones. In particular, recent interest in acquired immune deficiency syndrome has stimulated much progress in diverse areas of epidemic modelling, particularly with regard to the… (More)

- Steven Riley, Ken Eames, Valerie Isham, Denis Mollison, Pieter Trapman
- Epidemics
- 2015

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)

- D Mollison, S U Din
- Mathematical biosciences
- 1993

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)

- D Mollison
- Nature
- 1984

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)

- Frank Ball, Denis Mollison
- 1999

We consider epidemics with removal ('SIR epidemics') in populations which mix at two levels, 'global' and 'local'. We analyse the conditions under which a large outbreak is possible, the size of such outbreaks when they can occur, and the implications for vaccination strategies; in each case comparing our results with the simpler homogeneous mixing case.… (More)

- D Mollison, H Daniels
- Mathematical biosciences
- 1993

We derive a simple but surprising connection between the "deterministic epidemic with recovery" and the spatial birth-and-death process, and discuss its implications for the use of nonlinear differential equation models for the spatial spread of epidemics. This paper consists of a manuscript dating back to 1977, which has been widely referred to in the… (More)

- Frank Ball, Tom Britton, +4 authors Gianpaolo Scalia Tomba
- Epidemics
- 2015

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)

- Denis Mollison
- 2003

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)