The Economics of Managing Wildlife Disease

The spread of infectious disease among and between wild and domesticated animals has become a major problem worldwide. Upon analyzing the dynamics of wildlife growth and infection when the diseased animals cannot be identified separately from healthy wildlife prior to the kill, we find that harvest-based strategies alone have no impact on disease transmission. Other controls that directly influence disease transmission and/or mortality are required. Next, we analyze the socially optimal management of infectious wildlife. The model is applied to the problem of bovine tuberculosis among Michigan white-tailed deer, with non-selective harvests and supplemental feeding being the control variables. Using a two-state linear control model, we find a two-dimensional singular path is optimal (as opposed to a more conventional bang-bang solution) as part of a cycle that results in the disease remaining endemic in the wildlife. This result follows from non-selective harvesting and intermittent wildlife productivity gains from supplemental feeding.

A New Approach for Managing Bovine Tuberculosis: Veterinary Services’ Proposed Action Plan

Bovine tuberculosis (TB) is a serious disease with animal health, public health, and international trade consequences. The cooperative Federal-State-industry effort to eradicate bovine TB from cattle in the United States has made significant progress since the program’s inception in 1917. However, the goal of eradication remains elusive. This proposed action plan presents Veterinary Services’ (VS’) current thinking about changes we are considering for the TB program to address our current challenges. This action plan will: 1. Reduce the introduction of TB into the U.S. national herd from imported animals and wildlife by: o Applying additional requirements to cattle imports from Mexico o Enhancing efforts to mitigate risks from wildlife 2. Enhance TB surveillance by: o Crafting a comprehensive national surveillance plan o Accelerating diagnostic test development to support surveillance 3. Increase options for managing TB-affected herds by: o Conducting epidemiological investigations and assessing individual herd risk o Applying whole-herd depopulation judiciously and developing alternative control strategies o Applying animal identification (ID) standards to meet animal ID needs 4. Modernize the regulatory framework to allow VS to focus resources where the disease exists 5. Transition the TB program from a State classification system to a science-based zoning approach to address disease risk To succeed, this new approach will require VS’ continued partnership with State animal health and wildlife officials, other Federal agencies, industry, international partners, academia, and other stakeholders. Successful partnerships will allow us to use available resources efficiently to achieve program objectives and protect our nation’s herd. Implementation of the VS proposed action plan will benefit Federal and State animal health officials, the regulated industries, and producers by allowing a more rapid response that employs up-to-date science and can adapt rapidly to changing situations.

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.