Centre of Excellence for Biosecurity Risk Analysis, School of Biosciences and School of Ecosystem and Forest Sciences, University of Melbourne, Victoria, Australia.
Australian Centre for Biosecurity and Environmental Economics, Crawford School of Public Policy, Australian National University, Canberra, ACT, Australia.
PLoS One. 2020 Jul 9;15(7):e0235969. doi: 10.1371/journal.pone.0235969. eCollection 2020.
Decisions surrounding the presence of infectious diseases are typically made in the face of considerable uncertainty. However, the development of models to guide these decisions has been substantially constrained by computational difficulty. This paper focuses on the case of finding the optimal level of surveillance against a highly infectious animal disease where time, space and randomness are fully considered. We apply the Sample Average Approximation approach to solve our problem, and to control model dimension, we propose the use of an infection tree model, in combination with sensible 'tree-pruning' and parallel processing techniques. Our proposed model and techniques are generally applicable to a number of disease types, but we demonstrate the approach by solving for optimal surveillance levels against foot-and-mouth disease using bulk milk testing as an active surveillance protocol, during an epidemic, among 42,279 farms, fully characterised by their location, livestock type and size, in the state of Victoria, Australia.
在面对大量不确定性的情况下，通常会做出有关传染病存在的决策。然而，开发用于指导这些决策的模型受到计算困难的极大限制。本文重点研究在充分考虑时间、空间和随机性的情况下，针对高度传染性动物疾病寻找最佳监测水平的情况。我们应用抽样平均逼近方法来解决我们的问题，并通过使用感染树模型，结合合理的“树修剪”和并行处理技术，来控制模型维度。我们提出的模型和技术通常适用于多种疾病类型，但我们通过使用批量奶测试作为主动监测方案，在澳大利亚维多利亚州的 42279 个农场中，针对口蹄疫的最佳监测水平进行求解，演示了该方法，这些农场的位置、牲畜类型和规模等特征均已完全确定。
