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Abstract Studies of infectious disease ecology would benefit greatly from knowing when individuals were infected, but estimating this time of infection can be challenging, especially in wildlife. Time of infection can be estimated from various types of data, with antibody‐level data being one of the most promising sources of information. The use of antibody levels to back‐calculate infection time requires the development of a host‐pathogen system‐specific model of antibody dynamics, and a leading challenge in such quantitative serology approaches is how to model antibody dynamics in the absence of experimental infection data.We present a way to model antibody dynamics in a Bayesian framework that facilitates the incorporation of all available information about potential infection times and apply the model to estimate infection times of Channel Island foxes infected withLeptospira interrogans.Using simulated data, we show that the approach works well across a broad range of parameter settings and can lead to major improvements in infection time estimates that depend on system characteristics such as antibody decay rate and variation in peak antibody levels after exposure. When applied to field data we saw reductions up to 83% in the window of possible infection times.The method substantially simplifies the challenge of modelling antibody dynamics in the absence of individuals with known infection times, opens up new opportunities in wildlife disease ecology and can even be applied to cross‐sectional data once the model is trained. 

Abstract Contemporary wildlife disease management is complex because managers need to respond to a wide range of stakeholders, multiple uncertainties, and difficult trade‐offs that characterize the interconnected challenges of today. Despite general acknowledgment of these complexities, managing wildlife disease tends to be framed as a scientific problem, in which the major challenge is lack of knowledge. The complex and multifactorial process of decision‐making is collapsed into a scientific endeavor to reduce uncertainty. As a result, contemporary decision‐making may be oversimplified, rely on simple heuristics, and fail to account for the broader legal, social, and economic context in which the decisions are made. Concurrently, scientific research on wildlife disease may be distant from this decision context, resulting in information that may not be directly relevant to the pertinent management questions. We propose reframing wildlife disease management challenges as decision problems and addressing them with decision analytical tools to divide the complex problems into more cognitively manageable elements. In particular, structured decision‐making has the potential to improve the quality, rigor, and transparency of decisions about wildlife disease in a variety of systems. Examples of management of severe acute respiratory syndrome coronavirus 2, white‐nose syndrome, avian influenza, and chytridiomycosis illustrate the most common impediments to decision‐making, including competing objectives, risks, prediction uncertainty, and limited resources.