<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>David James Páez</dc:contributor>
  <dc:contributor>Alyssa-Lois M. Gehman</dc:contributor>
  <dc:creator>Maya L. Groner</dc:creator>
  <dc:date>2026</dc:date>
  <dc:description>&lt;p&gt;&lt;span&gt;Marine diseases can have far-reaching effects on population, community and ecosystem health; however, our ability to track, predict and manage these diseases has, historically, been poor. As a result, the fields of disease ecology and epidemiology have developed at a slower pace for marine than terrestrial systems [&lt;/span&gt;&lt;a class="link link-ref xref-bibr" data-modal-source-id="R1"&gt;1&lt;/a&gt;&lt;span&gt;]. New methodologies, including genomic tools for diagnostics [&lt;/span&gt;&lt;a class="link link-ref xref-bibr" data-modal-source-id="R2"&gt;2&lt;/a&gt;&lt;span&gt;,&lt;/span&gt;&lt;a class="link link-ref xref-bibr" data-modal-source-id="R3"&gt;3&lt;/a&gt;&lt;span&gt;], transcriptomic tools for measuring host and pathogen responses to infection (e.g. [&lt;/span&gt;&lt;a class="link link-ref xref-bibr" data-modal-source-id="R4"&gt;4&lt;/a&gt;&lt;span&gt;,&lt;/span&gt;&lt;a class="link link-ref xref-bibr" data-modal-source-id="R5"&gt;5&lt;/a&gt;&lt;span&gt;]), regional oceanic modelling systems that estimate environmental conditions influencing pathogen dispersal and disease progression [&lt;/span&gt;&lt;a class="link link-ref xref-bibr" data-modal-source-id="R6"&gt;6&lt;/a&gt;&lt;span&gt;], artificial intelligence methods for quantifying pathology from images (e.g. [&lt;/span&gt;&lt;a class="link link-ref xref-bibr" data-modal-source-id="R7"&gt;7&lt;/a&gt;&lt;span&gt;]) and advanced disease modelling techniques [&lt;/span&gt;&lt;a class="link link-ref xref-bibr" data-modal-source-id="R8"&gt;8&lt;/a&gt;&lt;span&gt;,&lt;/span&gt;&lt;a class="link link-ref xref-bibr" data-modal-source-id="R9"&gt;9&lt;/a&gt;&lt;span&gt;] are precipitating a rapid increase in our understanding of marine pathosystems. In 2016, these efforts led to the first special issue of&amp;nbsp;&lt;/span&gt;&lt;i&gt;Philosophical Transactions of the Royal Society B&lt;/i&gt;&lt;span&gt;&amp;nbsp;(&lt;/span&gt;&lt;i&gt;Marine diseases,&lt;/i&gt;&lt;span&gt;&amp;nbsp;volume 371, issue 1689) focused entirely on marine disease ecology and evolution, and in 2020, the first book,&amp;nbsp;&lt;/span&gt;&lt;i&gt;Marine disease ecology,&lt;/i&gt;&lt;span&gt;&amp;nbsp;was devoted to this topic [&lt;/span&gt;&lt;a class="link link-ref xref-bibr" data-modal-source-id="R10"&gt;10&lt;/a&gt;&lt;span&gt;].&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;This special issue, focused on&amp;nbsp;&lt;i&gt;marine disease management&lt;/i&gt;, is being published a decade after the first&amp;nbsp;&lt;i&gt;Philosophical Transactions&lt;/i&gt;&amp;nbsp;special issue on marine diseases. The shift to a management focus reflects an urgent need for management strategies to address high-impact diseases and the rapid methodological advances that have resulted. The papers included in this issue demonstrate the value of combining classical approaches (e.g. routine disease surveillance, reductionistic pathogen challenge trials, rapid throughput diagnostics) with cutting-edge technologies (e.g. high-resolution oceanographic models, Bayesian models, replicated transcriptomic studies) to identify drivers of disease, quantify impacts and suggest management strategies.&lt;/span&gt;&lt;/p&gt;</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>10.1098/rstb.2024.0318</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>The Royal Society Publishing</dc:publisher>
  <dc:title>From understanding to action: Integrating new and old methodologies to manage marine infectious disease</dc:title>
  <dc:type>article</dc:type>
</oai_dc:dc>