Model-based Surveillance System Design under Practical Constraints with Application to White Nose Syndrome

Abstract

Infectious diseases are powerful ecological forces structuring ecosystems, causing devastating economic impacts and disrupting society. Successful disease prevention and control require not only awareness of the current disease situation, but also the ability to understand disease dynamics, all of which rely on collection of data at strategically chosen locations during surveillance seasons. In particular, knowledge about the location of the disease front is foundational for deploying disease counter measures to prevent further disease spread and focusing control efforts to reduce disease intensity in affected areas. In this paper, we develop a model-based approach to designing sampling strategies for wildlife disease surveillance at the disease front. Specifically, we use a mechanistic spatio-temporal model based on an underlying partial differential equation to track the disease dynamics and predict the disease prevalence in the future surveillance season. We also devise an optimal surveillance system design at the disease front that takes into account practical constraints of sampling. We evaluate the effectiveness of our proposed design via a simulation study and demonstrate the application of the proposed approach by designing a surveillance strategy for white-nose syndrome in the contiguous US.

Matching decision support modeling frameworks to disease emergence stages and associated management objectives

“…While multi‐population (e.g., metapopulation) occupancy models may be useful for host species living in discrete populations, diffusion models may be more appropriate for populations distributed continuously across the landscape (Oh et al, 2023; Hefley et al, 2017). For the analyses of landscapes in a continuous‐time framework (vs. a patch occupancy framework in discrete time), ecological diffusion models have also been used to estimate the spread of pathogens at large spatial scales (e.g., Hefley et al, 2017; Wu, 2008).…”