Effective disease surveillance by using covariate information

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.