Effective intervention strategies for epidemics rely on the identification of their origin and on the robustness of the predictions made by network disease models. We introduce a Bayesian uncertainty quantification framework to infer model parameters for a disease spreading on a network of communities from limited, noisy observations; the state-of-the-art computational framework compensates for the model complexity by exploiting massively parallel computing architectures. Using noisy, synthetic data, we show the potential of the approach to perform robust model fitting and additionally demonstrate that we can effectively identify the disease origin via Bayesian model selection. As disease-related data are increasingly available, the proposed framework has broad practical relevance for the prediction and management of epidemics.

流行病的有效干预策略依赖于其起源的确定以及网络疾病模型所作预测的稳健性。我们引入贝叶斯不确定性量化框架，以从有限，嘈杂的观察结果推断在社区网络上传播的疾病的模型参数；最新的计算框架通过利用大规模并行计算架构来补偿模型的复杂性。使用嘈杂的合成数据，我们展示了该方法执行健壮模型拟合的潜力，并另外证明了我们可以通过贝叶斯模型选择有效地识别疾病起源。随着与疾病有关的数据越来越多，拟议的框架对于流行病的预测和管理具有广泛的实际意义。