Theory and practice of infectious disease surveillance
Seminar Room 1, Newton Institute
AbstractSurveillance is the first line of defence against infectious disease outbreaks, making the design of effective and efficient surveillance systems an important public health challenge. Both statistical and process models of outbreak dynamics are potentially useful in this context, but there have been relatively few applications of these tools to designing surveillance systems, in marked contrast to the many and influential applications to prevention and control programmes. Here, I review efforts to fill this gap, focussing on the design of so-called ‘smart’ surveillance systems that incorporate knowledge of patterns of risk to target surveillance effort more efficiently. There are several examples where smart surveillance systems have been shown to be considerably more efficient: post-epidemic surveillance for freedom from foot-and-mouth disease (5x more efficient); detection of new infections spreading through a network of hospitals (up to 8x). Designing surveillance systems is more challenging when signal has to be separated from noise. This is important for understanding the impact of vaccination on the detection of H5N1 influenza in poultry or the detection of pandemic influenza in the presence of seasonal influenza. There is an even more difficult problem of identifying novel “events”, e.g. unusual clinical cases or outbreaks due to unrecognised, unexpected or even completely new infectious diseases. This is being addressed by using data reduction methods to provide a benchmark for expected patterns of variation in clinical presentation or outbreak characteristics. Designing smart surveillance systems presents a number of interesting challenges, both in theory and in practice. The take home message from the various studies described here is that model-based approaches have considerable potential to contribute to improving the effectiveness and efficiency of surveillance systems, to the benefit of both human and animal health.
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