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Other ways to achieve the condition S(0)?[http://www.selleckchem.com/products/gsk126.html GSK126 supplier] [http://www.selleckchem.com/products/Staurosporine.html find more] who are geographically and socially closer. Homogeneity of the population��the model does not allow for the fact that individuals may be different from each other in ways that are relevant to the transmission of infection. There are individuals who are more susceptible to infection or more infectious than others; and there are individuals who make more contacts than others. Exponentially distributed duration of infection��this refers to the fact that the model assumes both that a person becomes infectious immediately upon being infected, and that the probability of recovery per unit time does not depend on the time that has passed since infection. Both assumptions are unrealistic [5]. Large population��the very form of the model, formulated in terms of continuous quantities (fractions of the population), implicitly assumes that the population is large (strictly speaking, infinite). In a small population (e.g. a village or school), stochastic effects are much more important, and modelling using mean field approximations (i.e. by differential equations) becomes problematic [6]. Given all these unrealistic [http://en.wikipedia.org/wiki/Transducin Transducin] assumptions, which, if any, of the predictions made by the model can we take seriously? As we have indicated above, the main approach with which modellers can address this question is by constructing more elaborate models that replace some of the unrealistic assumptions with more representative ones. Those predictions that remain unchanged, or only slightly changed, even for the more realistic model, are deemed to be robust, and we gain some confidence that they can be applied to the real world. A large part of the literature on the mathematical modelling of infectious disease transmission consists precisely of relaxing the above assumptions, and some others, by constructing appropriate models, and examining how the models' behavior changes as the model assumptions are modified [6-8]. Going back to the predictions made by the simple SIR model above, we can note that the threshold property (i.e.