Cope Robert C, Ross Joshua V
The University of Adelaide, Stochastic Modelling & Operations Research Group, School of Mathematical Sciences, Adelaide, SA 5005, Australia.
The University of Adelaide, Stochastic Modelling & Operations Research Group, School of Mathematical Sciences, Adelaide, SA 5005, Australia.
J Theor Biol. 2020 Feb 7;486:110079. doi: 10.1016/j.jtbi.2019.110079. Epub 2019 Nov 14.
In an outbreak of an emerging disease the epidemiological characteristics of the pathogen may be largely unknown. A key determinant of ability to control the outbreak is the relative timing of infectiousness and symptom onset. We provide a method for identifying this relationship with high accuracy based on data from simulated household-stratified symptom-onset data. Further, this can be achieved with observations taken on only a few specific days, chosen optimally, within each household. The information provided by this method may inform decision making processes for outbreak response. An accurate and computationally-efficient heuristic for determining the optimal surveillance scheme is introduced. This heuristic provides a novel approach to optimal design for Bayesian model discrimination.
在一种新发疾病的暴发中,病原体的流行病学特征可能很大程度上未知。控制暴发能力的一个关键决定因素是传染性与症状出现的相对时间。我们基于模拟的家庭分层症状出现数据,提供了一种能高精度识别这种关系的方法。此外,通过在每个家庭内仅在几个经优化选择的特定日期进行观察就能实现这一点。该方法提供的信息可为暴发应对的决策过程提供参考。我们引入了一种用于确定最佳监测方案的准确且计算高效的启发式方法。这种启发式方法为贝叶斯模型判别提供了一种新颖的优化设计方法。