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.

中文翻译：

---

使用协变量信息进行有效的疾病监测

对传染病、癌症和其他致命疾病的有效监测对于我们社会的公共卫生和安全至关重要。此类疾病的发病率数据通常是通过区域、国家或全球疾病报告系统从不同诊所和医院在空间上收集的。在这样的系统中，随着时间的推移不断收集新批次的数据，并且需要在收集新数据后立即做出关于当前时间点是否有疾病爆发的决定。这是本文将重点关注的疾病监测问题。已有一些解决这个问题的方法，其中大部分只使用发病率数据。然而，在实践中，疾病发生率往往与一些协变量相关，包括气温、湿度、以及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，可以利用有用的协变量信息来提高其有效性。这种新方法的新颖之处在于，只有那些与真正疾病爆发相关的协变量信息才能帮助触发信号。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。这些功能使其可以在许多实际应用中使用。这种新方法的新颖之处在于，只有那些与真正疾病爆发相关的协变量信息才能帮助触发信号。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。这些功能使其可以在许多实际应用中使用。这种新方法的新颖之处在于，只有那些与真正疾病爆发相关的协变量信息才能帮助触发信号。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。这些功能使其可以在许多实际应用中使用。