Department of Biostatistics, University of Florida, Gainesville, Florida, USA.
Stat Med. 2021 Nov 20;40(26):5725-5745. doi: 10.1002/sim.9150. Epub 2021 Jul 30.
Effective surveillance of infectious diseases, cancers, and other deadly diseases is critically important for public health and safety of our society. Incidence data of such diseases are often collected spatially from different clinics and hospitals through a regional, national or global disease reporting system. In such a system, new batches of data keep being collected over time, and a decision needs to be made immediately after new data are collected regarding whether there is a disease outbreak at the current time point. This is the disease surveillance problem that will be focused in this article. There are some existing methods for solving this problem, most of which use the disease incidence data only. In practice, however, disease incidence is often associated with some covariates, including the air temperature, humidity, and other weather or environmental conditions. In this article, we develop a new methodology for disease surveillance which can make use of helpful covariate information to improve its effectiveness. A novelty of this new method is behind the property that only those covariate information that is associated with a true disease outbreak can help trigger a signal. The new method can accommodate seasonality, spatio-temporal data correlation, and nonparametric data distribution. These features make it feasible to use in many real applications.
有效监测传染病、癌症和其他致命疾病对于公共卫生和社会安全至关重要。此类疾病的发病率数据通常通过区域性、全国性或全球性疾病报告系统从不同的诊所和医院进行空间收集。在这样的系统中，随着时间的推移会不断收集新的批次数据，并且在收集新数据后需要立即做出决策，以确定当前是否发生了疾病爆发。这就是本文将重点关注的疾病监测问题。目前已经有一些用于解决该问题的方法，其中大多数方法仅使用疾病发病率数据。然而，在实际中，疾病发病率通常与一些协变量相关，包括气温、湿度和其他天气或环境条件。在本文中，我们开发了一种新的疾病监测方法，该方法可以利用有用的协变量信息来提高其效果。这种新方法的新颖之处在于，只有与真正的疾病爆发相关的协变量信息才能帮助触发信号。该新方法可以适应季节性、时空数据相关性和非参数数据分布。这些功能使其能够在许多实际应用中使用。
