Data61, Commonwealth Scientific and Industrial Research Organisation, Pullenvale, QLD 4069, Australia;
Health & Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Canberra, ACT 2601, Australia.
Proc Natl Acad Sci U S A. 2019 Jan 8;116(2):401-406. doi: 10.1073/pnas.1801429116. Epub 2018 Dec 26.
Diffusion processes are governed by external triggers and internal dynamics in complex systems. Timely and cost-effective control of infectious disease spread critically relies on uncovering underlying diffusion mechanisms, which is challenging due to invisible infection pathways and time-evolving intensity of infection cases. Here, we propose a new diffusion framework for stochastic processes, which models disease spread across metapopulations by incorporating human mobility as topological pathways in a heterogeneous social system. We apply Bayesian inference with the stochastic Expectation-Maximization algorithm to quantify underlying diffusion dynamics in terms of exogeneity and endogeneity and estimate cross-regional infection flow based on Granger causality. The effectiveness of our proposed model is shown by using comprehensive simulation procedures (robustness tests with noisy data considering missing or delayed human case reporting in real situations) and by applying the model to real data from 15-y dengue outbreaks in Australia.
扩散过程受复杂系统中外部触发因素和内部动态的控制。及时有效地控制传染病的传播，关键在于揭示潜在的扩散机制，但由于感染途径难以察觉以及感染病例的强度随时间不断变化，这一任务极具挑战性。在这里，我们提出了一种新的随机过程扩散框架，该框架通过将人类移动性纳入异构社会系统中的拓扑路径，来对跨越多个群体的疾病传播进行建模。我们应用贝叶斯推断和随机期望最大化算法，根据外生性和内源性来量化潜在的扩散动态，并根据格兰杰因果关系来估计跨区域的感染流。我们通过综合模拟程序（在实际情况下考虑到人类病例报告缺失或延迟的情况下，使用带有噪声数据的稳健性测试）以及将模型应用于澳大利亚 15 年登革热爆发的实际数据，展示了我们提出的模型的有效性。
