Epidemiological Characterization of African Swine Fever Dynamics in Ukraine, 2012–2023
Bibliographic record
Abstract
African swine fever (ASF) is a viral disease, endemic to Africa, that causes high mortality when introduced into domestic pig populations. Since the emergence of p72-genotype II African swine fever virus (ASFV) in Georgia in 2007, an ASF epidemic has been spreading across Europe and many countries in Asia. The epidemic first reached Ukraine in 2012. To better understand the dynamics of spread of ASF in Ukraine, we analyzed spatial and temporal outbreak data reported in Ukraine between 2012 and mid-2023. The highest numbers of outbreaks were reported in 2017 (N = 163) and 2018 (N = 145), with overall peak numbers of ASF outbreaks reported in August (domestic pigs) and January (wild boars). While cases were reported from most of Ukraine, we found a directional spread from the eastern and northern borders towards the western and southern regions of Ukraine. Many of the early outbreaks (before 2016) were adjacent to the border, which is again true for more recent outbreaks in wild boar, but not for recent outbreaks in domestic pigs. Outbreaks prior to 2016 also occurred predominantly in areas with a below average domestic pig density. This new analysis suggests that wild boars may have played an important role in the introduction and early spread of ASF in Ukraine. However, in later years, the dynamic suggests human activity as the predominant driver of spread and a separation of ASF epizootics between domestic pigs and in wild boars. The decline in outbreaks since 2019 suggests that the implemented mitigation strategies are effective, even though long-term control or eradication remain challenging and will require continued intensive surveillance of ASF outbreak patterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".