USDA Animal and Plant Health Inspection Service, Veterinary Services, Centers for Epidemiology and Animal Health, 2150 Centre Ave, Bldg B, CO 80526, USA.
Prev Vet Med. 2010 Apr 1;94(1-2):140-53. doi: 10.1016/j.prevetmed.2009.11.021. Epub 2009 Dec 31.
The ability to combine evidence streams to establish disease freedom or prioritize surveillance is important for the evaluation of emerging diseases, such as viral hemorrhagic septicemia virus (VHSV) IVb in freshwater systems of the United States and Canada. Waterways provide a relatively unconstrained pathway for the spread of VHSV; and structured surveillance for emerging disease in open systems has many challenges. We introduce a decision framework for estimating VHSV infection probability that draws from multiple evidence streams and addresses challenges associated with the assessment of emerging disease. Using this approach, historical and risk-based evidence, whether empirical or expert-derived, supplement surveillance data to estimate disease probability. Surveillance-based estimates of VHSV prevalence were described using beta distributions. Subjective likelihood ratios (LRs), representing contextual risk, were elicited by asking experts to estimate the predicted occurrence of risk factors among VHSV-affected vs. VHSV-unaffected watersheds. We used the odds form of Bayes' theorem to aggregate expert and surveillance evidence to predict the risk-adjusted posterior probability of VHSV-infection for given watersheds. We also used LRs representing contextual risk to quantify the time value of past surveillance data. This evidence aggregation model predicts disease probability from the combined assessment of multiple sources of information. The method also provides a flexible framework for iterative revision of disease freedom status as knowledge and data evolve.
将证据流结合起来以确定疾病是否已消除或确定监测的优先级，对于评估新兴疾病（如美国和加拿大淡水系统中的病毒性出血性败血症病毒（VHSV）IVb）非常重要。水道为 VHSV 的传播提供了相对不受限制的途径；在开放系统中针对新兴疾病进行结构化监测存在许多挑战。我们引入了一种决策框架，用于从多个证据流中估计 VHSV 感染概率，并解决与评估新兴疾病相关的挑战。通过这种方法，无论是经验性还是专家推导的，历史和基于风险的证据都可以补充监测数据以估计疾病的概率。使用贝叶斯定理的odds 形式，通过询问专家来估计 VHSV 感染流域与 VHSV 未感染流域之间风险因素的预期发生情况，得出表示上下文风险的主观似然比（LR）。我们使用 Odds 形式的贝叶斯定理将专家和监测证据进行汇总，以预测给定流域 VHSV 感染的风险调整后后验概率。我们还使用表示上下文风险的 LR 来量化过去监测数据的时间价值。该证据汇总模型通过对多个信息源的综合评估来预测疾病的概率。该方法还为随着知识和数据的发展，对疾病是否已消除状态进行迭代修订提供了一个灵活的框架。
