BOARD INVITED REVIEW: Prospects for improving management of animal disease introductions using disease-dynamic models

Abstract

Management and policy decisions are continually made to mitigate disease introductions in animal populations despite often limited surveillance data or knowledge of disease transmission processes. Science-based management is broadly recognized as leading to more effective decisions yet application of models to actively guide disease surveillance and mitigate risks remains limited. Disease-dynamic models are an efficient method of providing information for management decisions because of their ability to integrate and evaluate multiple, complex processes simultaneously while accounting for uncertainty common in animal diseases. Here we review disease introduction pathways and transmission processes crucial for informing disease management and models at the interface of domestic animals and wildlife. We describe how disease transmission models can improve disease management and present a conceptual framework for integrating disease models into the decision process using adaptive management principles. We apply our framework to a case study of African swine fever virus in wild and domestic swine to demonstrate how disease-dynamic models can improve mitigation of introduction risk. We also identify opportunities to improve the application of disease models to support decision-making to manage disease at the interface of domestic and wild animals. First, scientists must focus on objective-driven models providing practical predictions that are useful to those managing disease. In order for practical model predictions to be incorporated into disease management a recognition that modeling is a means to improve management and outcomes is important. This will be most successful when done in a cross-disciplinary environment that includes scientists and decisionmakers representing wildlife and domestic animal health. Lastly, including economic principles of value-of-information and cost-benefit analysis in disease-dynamic models can facilitate more efficient management decisions and improve communication of model forecasts. Integration of disease-dynamic models into management and decision-making processes is expected to improve surveillance systems, risk mitigations, outbreak preparedness, and outbreak response activities.

Continental‐scale dynamics of avian influenza in U.S. waterfowl are driven by demography, migration, and temperature

“…Single, low pathogenic outbreak losses in domestic poultry are estimated minimally at $131 million (Capua and Alexander 2004) and high pathogenic outbreak losses are estimated at more than $1.15 billion for response/indemnity costs and up to 3.3 billion when accounting for the economic impacts of lost trade (U.S. Department of Agriculture 2016). Predicting the spatiotemporal distribution of AIV across the United States is an important component of broader data‐ and model‐driven management frameworks for wildlife disease (Miller and Pepin et al 2019) and could improve our understanding of infection ecology (Hill and Runstadler 2016). Previous studies characterizing the distribution of AIV have relied on host traits or local‐scale predictors that capture infection patterns among birds aggregated at one location (Ip et al 2008, Farnsworth et al 2012, Bevins et al 2014, Belkhiria et al 2016, Papp et al 2017).…”

A framework for surveillance of emerging pathogens at the human-animal interface: Pigs and coronaviruses as a case study

“…Wildlife disease surveillance is challenging because animals are difficult to sample randomly across a geographic area or at a target proportion, and resources are typically limited ( Park et al, 2013 ). Thus it is useful to guide surveillance programs spatially based on where risk might be highest – targeted risk-based surveillance ( Miller and Pepin, 2019 ). Using pigs and CoVs in the USA as a case study, we illustrate how risk factors can be used to develop guidance for triaging surveillance resources.…”

“…In adaptive management frameworks, management is structured to improve learning about the system by iterating between monitoring that reduces uncertainty about key drivers of management outcomes and updating management strategies based on the improved knowledge ( Williams et al, 2009 ). Similarly a surveillance plan for emerging CoVs in pigs could be implemented adaptively in order to optimize the amount learned based on the surveillance objectives, current conditions, and most recent insight, e.g., ( Clow et al, 2019 ; Miller and Pepin, 2019 ). To implement adaptive surveillance there needs to be methods and personnel in place for regular assessments of risk from the surveillance data, in a manner that is appropriate for the surveillance objective and sampling design.…”

Predicting spread and effective control measures for African swine fever—Should we blame the boars?

“…For example, the framework could be used to model the spread of rabies via foxes. The reason to make the model generic is to ensure the possibility of rapidly modelling disease spread within disease outbreaks for many diseases and species, which is crucial if models are to inform on surveillance, intervention and disease progression during an outbreak (Miller & Pepin, 2019).…”