The Eradication of Bovine Tuberculosis From Infected Wildlife Populations: A New Zealand Scenario

The Animal Health Board (AHB) is the agency responsible for controlling bovine tuberculosis (Tb) in New Zealand. In 2000, the AHB embarked on a strategy designed to reduce the annual period prevalence of Tb infected cattle and farmed deer herds from 1.67% to 0.2% by 2012/13. Under current rules of the Office International des Epizooties (OIE) this would allow New Zealand to claim freedom from Tb. The epidemiology of Tb in New Zealand is largely influenced by wildlife reservoirs of infection and control of Tb vector populations is central to the elimination of Tb from New Zealand’s cattle and deer herds. The AHB has classified New Zealand’s land area into Vector Risk Areas (VRAs) where Tb is established in wildlife (currently 39%) and Vector Free Areas (VFAs) where the disease is not established (61%). Within the VRAs the introduced Australian brushtail possum (Trichosurus vulpecula) is the primary wildlife maintenance host and the main source of infection for domestic cattle and deer herds. Southland is a region of New Zealand with a long history of wildlife associated Tb. Progress in reducing infected herd numbers has been impressive in recent years, primarily due to an intensive possum control program. As a result of this reduction, the focus is now shifting to that of providing increasing levels of confidence that Tb is absent from the remaining susceptible wildlife. High levels of confidence of Tb freedom in wildlife will allow the AHB to reduce the wildlife control programs and ultimately cease control altogether, with minimal risk of Tb reemerging. This paper examines the strategies being utilized to provide that confidence. The types of data, the format in which it is collected and the methods of analysis and review are outlined.

Managing Large Data Sets Using Support Vector Machines

Hundreds of Terabytes of CMS (Compact Muon Solenoid) data are being accumulated for storage day by day at the University of Nebraska-Lincoln, which is one of the eight US CMS Tier-2 sites. Managing this data includes retaining useful CMS data sets and clearing storage space for newly arriving data by deleting less useful data sets. This is an important task that is currently being done manually and it requires a large amount of time. The overall objective of this study was to develop a methodology to help identify the data sets to be deleted when there is a requirement for storage space. CMS data is stored using HDFS (Hadoop Distributed File System). HDFS logs give information regarding file access operations. Hadoop MapReduce was used to feed information in these logs to Support Vector Machines (SVMs), a machine learning algorithm applicable to classification and regression which is used in this Thesis to develop a classifier. Time elapsed in data set classification by this method is dependent on the size of the input HDFS log file since the algorithmic complexities of Hadoop MapReduce algorithms here are O(n). The SVM methodology produces a list of data sets for deletion along with their respective sizes. This methodology was also compared with a heuristic called Retention Cost which was calculated using size of the data set and the time since its last access to help decide how useful a data set is. Accuracies of both were compared by calculating the percentage of data sets predicted for deletion which were accessed at a later instance of time. Our methodology using SVMs proved to be more accurate than using the Retention Cost heuristic. This methodology could be used to solve similar problems involving other large data sets.