Leif Gustafsson

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Swedish population-based incidence and mortality rates for cancer of the uterine cervix, both in situ and invasive, during the period 1958 to 1981 were determined by means of a dynamic model. This new approach describes without any preconceptions the development of the disease as a sequential process over the stages cancer in situ, invasive cancer before(More)
Population models concern collections of discrete entities such as atoms, cells, humans, animals, etc., where the focus is on the number of entities in a population. Because of the complexity of such models, simulation is usually needed to reproduce their complete dynamic and stochastic behaviour. Two main types of simulation models are used for different(More)
A population system can be modelled using a micro model focusing on the individual entities, a macro model where the entities are aggregated into compartments, or a state-based model where each possible discrete state in which the system can exist is represented. However, the concepts, building blocks, procedural mechanisms and the time handling for these(More)
In many studies of dynamic systems, the stochastic aspects are as important as the dynamic. It is then important to consider uncertainty in the results. Furthermore, dynamics and stochastics interact because the stochastics excite the dynamics and the dynamics change the conditions for the stochastics. Poisson Simulation is an extension of Continuous System(More)
Cervical cancer incidence and mortality can be reduced by removal of precursor lesions detected at cytological screening. Organised screening, i.e. regular invitation of defined target groups, is generally considered more effective than opportunistic screening. The latter method however, is predominant in most settings. There is no scientific basis for(More)
Markov Simulation and the more recent Poisson Simulation are two fully consistent ways of modelling, applicable to the same types of problems. A Markov model is based on a detailed description of every situation, called state, that a system can be in and every possible transition between these states. This approach allows powerful analysis and makes Markov(More)
Modeling flow rates as stochastic processes introduce stochastic properties in Continuous System Simulation models. This is particularly useful in models of middle-sized system where both stochastic and dynamic properties can be of great importance for the behavior of the system. Wanda for MATLAB consists of two programs for statistical handling of dynamic(More)
A dynamic population system is often modelled by a deterministic difference equation model to obtain average estimates. However, there is a risk of the results being distorted because unexplained (random) variations are left out and because entities in the population are described by continuous quantities of an infinitely divisible population so that(More)