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In this study and in (Convertino and Hedberg 2014a) it is shown that such problem can be mathematically decoupled and the optimization of surveillance implies the optimal detection of outbreak sources but not vice versa.
The model developed is extremely useful for the optimization of surveillance networks to drastically reduce the burden of food-borne and other infectious diseases.
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Extensive research on the optimization and comparison of surveillance algorithms exists (28 – 34 ); most published articles, however, are concerned with weekly reporting of rare infectious diseases in relatively wealthy countries.
This implies that optimization of resources between surveillance levels can be based on an informed guess about progression probabilities, since only if this guess is wrong by several orders of magnitude will precision decrease substantially.
Few optimal design studies concern disease surveillance: Most have been simulation studies of surveillance protocols for animal diseases (19– 23) and optimization studies of general practitioner recruitment for more precise estimation of influenza incidence in the community (24– 24).
One of the most promising uses of the technology is in the planning, optimization, and postoperative surveillance of mitral valve repair techniques and devices.
Other studies, such as (Pinto et al. 2012) considered random and non-random placement of surveillance observers which partially captures reporting uncertainty; nonetheless these studies did not formulate any optimization algorithm maximizing detection accuracy even in presence of uncertainty.
Prominent examples of surveillance include surveillance cameras, wiretaps, GPS tracking, and internet surveillance.
Aspects of surveillance could be improved.
There's a lot of surveillance.
Donner, F. The Age of Surveillance.
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