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Record W3191427002 · doi:10.3389/fvets.2021.723081

African Swine Fever in the Russian Far East (2019–2020): Spatio-Temporal Analysis and Implications for Wild Ungulates

2021· article· en· W3191427002 on OpenAlexaff
О. И. Захарова, Ilya Titov, Gogin Andrey, T.A. Sevskikh, Fedor I. Кorennoy, Denis Kolbasov, Levon Abrahamyan, A. A. Blokhin

Bibliographic record

VenueFrontiers in Veterinary Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsWild boarOutbreakAfrican swine fever virusAfrican swine feverDomestic pigGeographyGenotypePopulationBiologyChinaLivestockVirusVeterinary medicineVirologyDemographyEcologyMedicine

Abstract

fetched live from OpenAlex

African swine fever (ASF) is an emerging viral contagious disease affecting domestic pigs (DP) and wild boar (WB). ASF causes significant economic damage to the pig industry worldwide due to nearly 100% mortality and the absence of medical treatments. Since 2019, an intensive spread of ASF has been observed in the Russian Far East region. This spread raises concerns for epidemiologists and ecologists given the potential threat to the WB population, which is an essential member of the region's wild ungulates and provides a notable share of food resources for predatory species. This study aims to determine the genotype of ASF virus circulating in the region, reveal the spatio-temporal patterns of the ASF outbreaks' emergence, and assess the potential reduction of the regional fauna because of expected depopulation of WB. The first historical case of ASF in the study region was caused by an African swine fever virus (ASFV) isolated from DPs and belonging to Genotype 2, CVR1; IGR-2 (TRS +). Sequencing results showed no significant differences among ASFV strains currently circulating in the Russian Federation, Europe, and China. The spatiotemporal analysis with the space-time permutations model demonstrated the presence of six statistically significant clusters of ASF outbreaks with three clusters in DPs and one cluster in WBs. DP outbreaks prevail in the north-west regions of the study area, while northern regions demonstrate a mixture of DP and WB outbreaks. Colocation analysis did not reveal a statistically significant pattern of grouping of one category of outbreaks around the others. The possible damage to the region's fauna was assessed by modeling the total body mass of wild ungulates before and after the wild boars' depopulation, considering a threshold density of WB population of 0.025 head/km 2 , according to the currently in force National Plan on the ASF Eradication in Russia. The results suggest the total mass of ungulates of the entire study region will likely decrease by 8.4% (95% CI: 4.1–13.0%), while it may decrease by 33.6% (19.3–46.1%) in the Primorsky Krai, thereby posing an undeniable threat to the predatory species of the region.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.264
Teacher spread0.236 · 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 teacher head, 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

Citations22
Published2021
Admission routes1
Has abstractyes

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