Modeling Pathogen Dispersal in Marine Fish and Shellfish. Issue 3 (March 2020)

• Record Type: Journal Article
• Authors: Cantrell, Danielle L.; Groner, Maya L.; Ben-Horin, Tal; Grant, Jon; Revie, Crawford W.
• Is Part Of: Trends in parasitology. Volume 36:Issue 3(2020)
• Page Start: 239
• Page End: 249
• Publication Date: 2020-03
• Subjects: biophysical modeling -- pathogen dispersal -- parasite dispersal; Parasitology -- Periodicals; Parasitology -- Periodicals; Biology -- Periodicals; Parasitology; Biology; Parasitologie -- Périodiques; Online resources; 571.999
• DOI: 10.1016/j.pt.2019.12.013
• Languages: English
• ISSNs: 1471-4922

Abstract

Abstract : In marine ecosystems, oceanographic processes often govern host contacts with infectious agents. Consequently, many approaches developed to quantify pathogen dispersal in terrestrial ecosystems have limited use in the marine context. Recent applications in marine disease modeling demonstrate that physical oceanographic models coupled with biological models of infectious agents can characterize dispersal networks of pathogens in marine ecosystems. Biophysical modeling has been used over the past two decades to model larval dispersion but has only recently been utilized in marine epidemiology. In this review, we describe how biophysical models function and how they can be used to measure connectivity of infectious agents between sites, test hypotheses regarding pathogen dispersal, and quantify patterns of pathogen spread, focusing on fish and shellfish pathogens. Highlights: Bio-physical models are a useful tool for understanding dispersal and transmission of marine pathogens. While utilized for larval dispersal models, they are only recently being used in epidemiological studies and are currently underutilized by the marine epidemiology field. Bio-physical models are useful for spatial planning and coastal management. For example, they have been used for spatial planning of salmon farm site locations, and to establish early warning networks. Bio-physical modeling can be used to test hypotheses, rather than simply develop them. Model resolution and computation demands must be balanced when making decisions about model parameters. Epidemiological bio-physical models are in their infancy. While they have proven useful so far, future applications of these models can incorporate more aspects of disease dynamics and address many additional questions.