On 29 October 2000, the World Health Organization (WHO) Regional Commission for the Certification of Poliomyelitis Eradication in the Western Pacific certified the WHO Western Pacific Region as free of indigenous wild poliovirus. This status has been maintained to date: wild poliovirus importations into Singapore (in 2006) and Australia (in 2007) did not lead to secondary cases, and an outbreak in China (in 2011) was rapidly controlled. Circulation of vaccine derived polioviruses in Cambodia, China and the Philippines was quickly interrupted. A robust acute flaccid paralysis surveillance system, including a multitiered polio laboratory network, has been maintained, forming the platform for integrating measles, neonatal tetanus, and other vaccine-preventable disease surveillance and their respective control goals. While polio elimination remains one of the most important achievements in public health in the Western Pacific Region, extended delays in global eradication have, however, led to shifting and competing public health priorities among member states and partners and have made the region increasingly vulnerable.
On 20 September 2015, the Global Commission for the Certification of Poliomyelitis Eradication (GCC) declared that type 2 wild poliovirus had been eradicated. The GCC examined reports from 189 of 195 countries around the world and studied separate data [held by the World Health Organization (WHO)] from the remaining countries. According to WHO, nearly 2 million appropriate clinical samples had been tested, and no type 2 wild polioviruses were found. The announcement is the first step toward eradicating all three wild poliovirus types. But this landmark will only be declared when the GCC is confident that there have been no wild virus cases of any type for 3 years.
Previous work in using Artificial Neural Networks for computational stylistics has concentrated on using large, arbitrary network structures. This paper examines the use of the Cascade-Correlation algorithm for the construction of minimal networks. We find that a number of problems in computational stylistics with a large number of variables, but a limited number of training examples may be solved successfully without resorting to large networks. The issue of redundancy in the data is also considered.
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