Status of African Swine Fever in Arunachal Pradesh: Spatial and Temporal Analysis Since First Outbreak (2020-2025)
Bibliographic record
Abstract
African swine fever (ASF) has emerged as a major transboundary threat to pig production systems in Arunachal Pradesh, necessitating a detailed understanding of its epidemiology. This study analyzed laboratory-confirmed ASF outbreaks reported between 2020 and 2025 using integrated temporal, spatial, and positivity-based approaches. Surveillance data from NIHSAD, NERDDL, and the State Department of Animal Husbandry & Veterinary Services covered 18 confirmed outbreak locations across seven districts. Temporal analysis revealed a clear cyclic pattern marked by three major epidemic waves (2020, 2022, and 2025) and a strong bi-modal seasonal trend, with outbreaks consistently peaking during the pre-monsoon (March–April) and post-monsoon (August–November) periods. Spatial analysis performed in R demonstrated significant clustering of outbreaks, with persistent hotspots in Papum Pare and East Siang and emerging hotspots in Leparada, West Siang, and Lower Siang. Directional diffusion mapping indicated progressive westward and southward spread of ASF along major transportation and trade routes. District-wise positivity ranged from 66.67% to 100%, with the highest infection intensity observed in Lohit, West Siang, Lower Siang, and Longding. Overall, the combined spatio-temporal assessment highlights predictable high-risk seasons, persistent transmission corridors, and human-mediated movement as key drivers of ASF spread. These findings underscore the need for targeted surveillance, strengthened biosecurity, and movement control strategies to mitigate ongoing and future ASF incursions in Arunachal Pradesh.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".