Dataset Information

Global governance for pandemic prevention and the wildlife trade.

ABSTRACT: Although ideas about preventive actions for pandemics have been advanced during the COVID-19 crisis, there has been little consideration for how they can be operationalised through governance structures within the context of the wildlife trade for human consumption. To date, pandemic governance has mostly focused on outbreak surveillance, containment, and response rather than on avoiding zoonotic spillovers in the first place. However, given the acceleration of globalisation, a paradigm shift towards prevention of zoonotic spillovers is warranted as containment of outbreaks becomes unfeasible. Here, we consider the current institutional landscape for pandemic prevention in light of ongoing negotiations of a so-called pandemic treaty and how prevention of zoonotic spillovers from the wildlife trade for human consumption could be incorporated. We argue that such an institutional arrangement should be explicit about zoonotic spillover prevention and focus on improving coordination across four policy domains, namely public health, biodiversity conservation, food security, and trade. We posit that this pandemic treaty should include four interacting goals in relation to prevention of zoonotic spillovers from the wildlife trade for human consumption: risk understanding, risk assessment, risk reduction, and enabling funding. Despite the need to keep political attention on addressing the current pandemic, society cannot afford to miss the opportunity of the current crisis to encourage institution building for preventing future pandemics.

SUBMITTER: Gallo-Cajiao E

PROVIDER: S-EPMC10069821 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Global governance for pandemic prevention and the wildlife trade.

Gallo-Cajiao Eduardo E Lieberman Susan S Dolšak Nives N Prakash Aseem A Labonté Ronald R Biggs Duan D Franklin Christine C Morrison Tiffany H TH Viens A M AM Fuller Richard A RA Aguiar Raphael R Fidelman Pedro P Watson James E M JEM Aenishaenslin Cécile C Wiktorowicz Mary M

The Lancet. Planetary health 20230401 4

Although ideas about preventive actions for pandemics have been advanced during the COVID-19 crisis, there has been little consideration for how they can be operationalised through governance structures within the context of the wildlife trade for human consumption. To date, pandemic governance has mostly focused on outbreak surveillance, containment, and response rather than on avoiding zoonotic spillovers in the first place. However, given the acceleration of globalisation, a paradigm shift to ...[more]

PMID: 37019574

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Project description:Automated monitoring of websites that trade wildlife is increasingly necessary to inform conservation and biosecurity efforts. However, e-commerce and wildlife trading websites can contain a vast number of advertisements, an unknown proportion of which may be irrelevant to researchers and practitioners. Given that many wildlife-trade advertisements have an unstructured text format, automated identification of relevant listings has not traditionally been possible, nor attempted. Other scientific disciplines have solved similar problems using machine learning and natural language processing models, such as text classifiers. Here, we test the ability of a suite of text classifiers to extract relevant advertisements from wildlife trade occurring on the Internet. We collected data from an Australian classifieds website where people can post advertisements of their pet birds (n = 16.5k advertisements). We found that text classifiers can predict, with a high degree of accuracy, which listings are relevant (ROC AUC ≥ 0.98, F1 score ≥ 0.77). Furthermore, in an attempt to answer the question 'how much data is required to have an adequately performing model?', we conducted a sensitivity analysis by simulating decreases in sample sizes to measure the subsequent change in model performance. From our sensitivity analysis, we found that text classifiers required a minimum sample size of 33% (c. 5.5k listings) to accurately identify relevant listings (for our dataset), providing a reference point for future applications of this sort. Our results suggest that text classification is a viable tool that can be applied to the online trade of wildlife to reduce time dedicated to data cleaning. However, the success of text classifiers will vary depending on the advertisements and websites, and will therefore be context dependent. Further work to integrate other machine learning tools, such as image classification, may provide better predictive abilities in the context of streamlining data processing for wildlife trade related online data.

Project description:Wildlife trade is a multi-billion-dollar sector that impacts a wide range of species, and thus is of significant research and conservation interest. Wildlife trade has also become a prominent topic in the public-facing media, where coverage has intensified following the outbreak of the global COVID-19 pandemic due to the potential connection between wildlife trade and the origin of the SARS Cov2 virus. Given the importance of the media in shaping public understanding and discourse of complex topics such as wildlife trade, this could impact the implementation of and public support for policy decisions. In this study, we followed a standardised protocol to extract wildlife trade-related discussion from 285 professional opinion pieces (NGO reports or articles in conservation-themed forums) and 107 scientific articles published in two time periods: "pre-COVID" (June 1-December 31, 2019) and "during-COVID" (January 1-May 31, 2020). We compared opinion pieces and scientific articles across the two time periods and to each other to investigate potential differences in the presentation of wildlife trade and associated speakers. We found a shift in the way that wildlife trade was discussed in professional opinion pieces between the periods, in that the discussion became less specific in terms of defining the legality and purpose of trade, and the animal groups involved in the "during-COVID" period. The generalised framing of wildlife trade in our dataset also coincided with an increased discussion of highly generalised management strategies, such as blanket bans on wildlife trade. We also found that publications included more quotes from researchers in the "during-COVID" period. In both professional opinion pieces and scientific articles, we found that quotations or research were often from speakers whose affiliation region was different to the geographic range of the trade they were speaking about. This highlights the importance of incorporating local knowledge and considering the diversity of speakers and interviewees in both research and the public-facing media about the wildlife trade.

Dataset Information

Global governance for pandemic prevention and the wildlife trade.

Publications

Global governance for pandemic prevention and the wildlife trade.

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