Surveillance Analysis and Sample Size Explorer (SASSE): Learning How to Plan Disease Surveillance in Wildlife

Abstract

Wildlife disease surveillance helps in protecting public health, agriculture, and biodiversity. Planning effective surveillance involves strategic methods for identifying an effective sampling design for a program's objectives. Gaps in existing standards and complexity for wildlife surveillance justify a need for tools that can build statistically based intuition in wildlife professionals when designing surveillance systems. To address this need, we present the use of plug-and-play tools, specifically our surveillance analysis and sample size explorer (SASSE), to allow wildlife professionals to build intuition about the role of sample size vis-à-vis sampling design and diagnostic test performance in wildlife systems. SASSE uses open-source software (R, R Shiny) to design an interactive, module-based teaching tool to cover key surveillance objectives, including detection, prevalence, and epidemiological dynamics. Our tool fills the following gaps: (1) allows a broad audience to apply statistical sample size theory for designing disease surveillance, and (2) provides a simple statistical tool for addressing challenges with disease surveillance design in wildlife populations. Thus, the tool we present here can be used readily to identify efficient sampling designs for a surveillance objective across a wide variety of settings.