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Record W3092678407 · doi:10.1007/s10393-020-01489-6

Integrating the Technical, Risk Management and Economic Implications of Animal Disease Control to Advise Policy Change: The Example of Foot-and-Mouth Disease Control in Uruguay

2020· article· en· W3092678407 on OpenAlexfundno aff
Brian D. Perry, Karl M. Rich, Hernán Rojas, Jaime Romero, David Adamson, José E. Bervejillo, Federico Fernández, Álvaro Pereira, Lautaro Pérez, Fernando Reich, Rafael Sarno, Edgardo Vitale, Federico Stanham, Jonathan Rushton

Bibliographic record

VenueEcoHealth · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsnot available
FundersIndigenous and Northern Affairs CanadaUniversity of OxfordInstituto Nacional de Investigación y Tecnología Agraria y Alimentaria
KeywordsAnimal ecologyFoot-and-mouth diseasePublic healthDiseaseControl (management)Disease controlMedicineEnvironmental healthEconomicsEcologyBiologyImmunologyPathology

Abstract

fetched live from OpenAlex

Countries contemplating a change in their animal disease control policy face a variety of considerations, particularly in circumstances in which disease status, and the use (or not) of vaccines to control or minimise disease risk, has major implications for international trade. Foot-and-mouth disease (FMD) exemplifies these trade-offs, and is particularly important in South America, where FMD virus circulation has declined and appears limited to certain regions. As a result, opportunities for higher-value exports in sustainably produced pasture-fed beef and lamb are growing. Uruguay is arguably at the forefront of these developments. It is renowned for an efficient livestock production base, high standards of animal health, and a pasture-based, extensive feeding system. Uruguay exports over four per cent of the world’s fresh and frozen meat (https://oec.world/en/profile/country/ury/), and in 2018, 70% of these exports went to China (Joseph 2019). Parts of neighbouring countries such as Brazil, Argentina and Paraguay share the advantages of pasture-based feeding, and also aspire to sell to more diverse international markets. Export market access for all these countries depends on the successful control of FMD. A country’s FMD status (whether endemic, free with vaccination, or free without vaccination) has implications for market access and prices, and these depend on trading partners’ willingness to accept different levels of risk. Some of the highest value markets for beef, such as Japan and Korea, only allow imports from the very small subset of countries that are FMD-free without vaccination against FMD (Rich and Winter-Nelson 2007). Uruguay and its neighbours are contemplating new FMD policy measures, including the cessation of blanket vaccination, in order to improve the quality, quantity and diversity of their markets. This will also contribute to the broader hemispheric aspirations of PHEFA (Hemispheric Foot and Mouth Disease Control Programme 2011–2020), together with the countries of South America and Panama, to eradicate FMD under the coordination of PANAFTOSA.Footnote1 In May 2019, the Uruguayan Ministry of Livestock, Agriculture and Fisheries (MGAP), the Instituto Nacional de Investigación Agropecuaria (INIA), and the Instituto Nacional de Carnes (INAC) jointly commissioned an independent evaluation of the implications of moving to a no FMD vaccination policy in the country, and to assess the technical, risk management and economic implications of any such change. The authors undertook this study, and in-country meetings and workshops were conducted in May, June, August and October 2019. Here, we present the study results and the broader implications of such interdisciplinary team studies to underlie animal health policy change in other counties and for other trade-related diseases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.224
Threshold uncertainty score0.445

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0100.005
Scholarly communication0.0120.005
Open science0.0020.009
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.290
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2020
Admission routes1
Has abstractyes

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