Moving towards improved surveillance and earlier diagnosis of aquatic pathogens: from traditional methods to emerging technologies

Macaulay, Scott, Ellison, Amy R., Kille, Peter ORCID: https://orcid.org/0000-0001-6023-5221 and Cable, Joanne ORCID: https://orcid.org/0000-0002-8510-7055 2022. Moving towards improved surveillance and earlier diagnosis of aquatic pathogens: from traditional methods to emerging technologies. Reviews in Aquaculture 14 (4) , pp. 1813-1829.

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Abstract

Early and accurate diagnosis is key to mitigating the impact of infectious diseases, along with efficient surveillance. This however is particularly challenging in aquatic environments due to hidden biodiversity and physical constraints. Traditional diagnostics, such as visual diagnosis and histopathology, are still widely used, but increasingly technological advances such as portable next generation sequencing (NGS) and artificial intelligence (AI) are being tested for early diagnosis. The most straightforward methodologies, based on visual diagnosis, rely on specialist knowledge and experience but provide a foundation for surveillance. Future computational remote sensing methods, such as AI image diagnosis and drone surveillance, will ultimately reduce labour costs whilst not compromising on sensitivity, but they require capital and infrastructural investment. Molecular techniques have advanced rapidly in the last 30 years, from standard PCR through loop-mediated isothermal amplification (LAMP) to NGS approaches, providing a range of technologies that support the currently popular eDNA diagnosis. There is now vast potential for transformative change driven by developments in human diagnostics. Here we compare current surveillance and diagnostic technologies with those that could be used or developed for use in the aquatic environment, against three gold standard ideals of high sensitivity, specificity, rapid diagnosis, and cost-effectiveness.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Biosciences
Publisher:	Wiley
ISSN:	1753-5123
Funders:	BBSRC
Date of First Compliant Deposit:	1 March 2022
Date of Acceptance:	1 March 2022
Last Modified:	22 May 2023 19:13
URI:	https://orca.cardiff.ac.uk/id/eprint/147947

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