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Record W2789780966 · doi:10.1201/9781351114509-6

Changing patterns of disease affecting pigs: Porcine Reproductive and Respiratory Syndrome (PRRS) and Porcine Epidemic Diarrhoea (PED)

2018· book-chapter· en· W2789780966 on OpenAlexaboutno aff
Scotland's Rural College Carla Correia-Gomes

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Virus Infections Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRespiratory systemDiseaseVirologyMedicinePhysiologyBiologyImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Any disease in humans or animals has its own unique epidemiological pattern, that is, behaviour (occurrence, disappearance, re-occurrence as well as distribution of outbreaks and cases in space and time) (Blaha, 2000). The epidemiological behaviour is determined by the biological properties of the causative agent (such as pathogenicity and infectivity), the characteristics of the pathogen-host interaction (such as immune response, shedding and transmission pattern) and socio-economic conditions (such as structure of the industry, animal movements and herd size) (Thrusfield, 2005). The pig industry, like any other livestock sector, is not immune to disease, and recent examples of outbreaks that have affected the pig industry are the African Swine Fever outbreaks in Eastern European countries (Gogin et al., 2013), the Porcine Epidemic Diarrhoea (PED) outbreak in the United States (US) in 2013 (Stevenson et al., 2013) and the Porcine Circovirus Associated Disease (PCVAD) outbreak in Ontario, Canada, in 2004 (Carman et al., 2008). Some of these outbreaks have led to epidemics and then went on to become endemic in some countries, changing their disease epidemiological behaviour over time and space. Overall, in the last three decades, the pig industry worldwide has experienced several major disease epidemics – all caused by viruses: Swine Influenza, Porcine Circovirus (PCV), Porcine Reproductive and Respiratory Syndrome virus (PRRSv) and Porcine Epidemic Diarrhoea virus (PEDv). Some of these viruses are not host-specific and can spread to other hosts, including humans (H1N1 influenza); others are highly host-specific (PRRSv and PEDv). The latter agents also share remarkable features exhibiting rapid rates of mutation and appear to have been associated with pigs for years to decades before highly pathogenic disease syndromes were manifested (Davies, 2015). In this chapter we focus on these two agents (PRRSv and PEDv) due to their importance in pig production worldwide and their evolution over the years. We also discuss the agent, transmission, clinical presentation and evolution and spread of these two viruses in the pig population both worldwide, and more specifically, the United Kingdom, in detail. This will provide readers with an overview of the complexity of these two agents and how that influences their clinical presentation and evolution over time and space. However, this is not an exhaustive literature review and as these agents are two of the most widely studied viruses, current and future research may update some of the findings reported here. As PED outbreaks have only been seen quite recently in the Western world, the US epidemic of 2013 and the current situation in Europe (including surveillance) are summarised in more detail.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.034
GPT teacher head0.239
Teacher spread0.205 · 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 designNot applicable
Domainnot available
GenreReview

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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