Philosophy Underlying Modeling of Infection

Before proceeding further, it is perhaps useful to emphasize what transmission dynamics models can do and what they cannot. In order for a model to be useful, many assumptions and generalizations need to be made if its behavior is not to become so complex that it is impossible to interpret the results or to understand how the various processes interact in the model. Unless we understand why a model produces the results it does we cannot sensibly interpret the results, nor answer the questions we wish to ask (and nor can we identify when an error has crept into the design or implementation of the model!). The value of the results is directly related to the skill with which the complexity of the real-world system is simplified while still retaining the essential features of the epidemiology. When undertaking modeling work, and when reviewing the results of other peoples' models, it should always be borne closely in mind that the results of such work can never be predictions of the future course of an epidemic with the precision that the term "prediction" implies. What such models can do is to make qualitative projections of possible future trends under specific scenarios allowing such questions to be addressed as follows: How should we interpret the epidemiological trends in infection that are being observed, and what might be the underlying processes involved? What data do we need to gather to gain a better understanding of these trends and processes? If a specific infection is introduced into a population, is there the potential for an epidemic, and how many people might be infected? How best might we optimize the design of control programs? What might be the relative advantages and disadvantages of one health intervention versus another?

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