Economic Consequences of African Swine Fever: Strengthening U.S. Preparedness and Resilience
Bibliographic record
Abstract
African swine fever (ASF) represents a serious threat to the U.S. pork industry and global agricultural markets due to its high mortality rate and the absence of a commercial vaccine. This study evaluates the potential economic consequences of a hypothetical ASF outbreak in the U.S. using the Global Trade Analysis Project (GTAP) computable general equilibrium model. For our preliminary analysis, we simulate four outbreak scenarios, varying in scale and trade disruption, to estimate impacts on production, trade flows, prices, and welfare across major global regions. Preliminary results indicate that small, localized outbreaks have limited domestic and global economic effects, while large-scale outbreaks could trigger severe welfare losses for the U.S. (up to $11.4 billion), along with substantial price increases and trade realignments. Competing exporters such as Canada, Brazil, and the European Union benefit from reduced U.S. market presence, while import-dependent regions face welfare losses. Welfare decomposition analysis reveals that U.S. losses in small outbreaks are driven primarily by deteriorating terms of trade, whereas losses in large outbreaks stem from technological shocks to domestic productivity. Although preliminary, these findings highlight the importance of early detection, containment, and international regionalization agreements as key strategies to mitigate economic disruption. The study provides evidence to inform U.S. animal health policy and highlights the global interdependence of pork markets in the face of transboundary animal diseases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".