Characterising the epidemiology of influenza A viruses at the swine-human interface: Study Protocol of the PigFluCam+ project in Cambodia (Preprint)
Bibliographic record
Abstract
Background: Influenza A viruses are a significant cause of global morbidity, mortality, and economic losses. Swine are considered an important host for pandemic emergence; however, knowledge on the ecology and evolution of swine influenza viruses in relation to pig production and exchange systems is limited. The PigFluCam+ project was first initiated in 2019 as a One Health-focused research collaboration between public and animal health stakeholders in Cambodia. Objective: The primary objectives of the project were to (1) describe the epidemiology and diversity of swine influenza A viruses (swIAVs) in the Cambodian pig sector, (2) assess the risk of zoonotic influenza transmission across different occupations, (3) characterize the pig trade network, (4) develop mathematical models of swIAV transmission to target control activities, and (5) promote in-country One Health research and surveillance. This paper presents the methods and approaches used by the project, serving as a resource for future research initiatives with similar aims. Methods: These approaches consist of systematic sample collections and survey studies. Influenza surveillance in pigs was conducted over 2 years through repeated (monthly) cross-sectional sampling at 18 slaughterhouses across 4 provinces. Phylogenetic analysis was used to describe the diversity of swIAVs detected and was used to develop antigens for Luminex xMAP assays for screening human and pig sera. Cross-sectional surveys among actors in the pig value chain characterized pig production practices and trading networks. In parallel, a cohort study was carried out involving households with and without occupational exposure to live pigs to compare the seroprevalence of influenza A viruses among different swine-associated occupational groups. Results: The surveys began in 2020 and despite disruptions caused by the COVID-19 pandemic and the introduction of African Swine Fever into the region, the project has generated a wealth of data. Over 4000 pigs were sampled at slaughterhouses, and network surveys collected pig production and trading data from 379 study participants. Higher influenza A seroprevalence (960/2399, 40%) and prevalence (37/2413, 1.5%) were found among pigs from commercial farms, compared to smallholder farms (seroprevalence 8.9%, 95/1066; prevalence 0.6%, 6/1071). Duration at slaughterhouse and seroprevalence correlated positively, suggesting potential transmission after leaving the farm. A total of 997 individuals were recruited into the cohort study, with 775 consenting to provide at least 1 serum sample. Funding for the project ended in September 2025; 3 results papers and 1 PhD thesis have been published, with analysis and publication expected to be completed by the end of 2026. Conclusions: This project has developed surveillance protocols and modern technologies for establishing active zoonotic disease surveillance. These efforts support the region's capability to effectively identify zoonotic pathogens and enhance the prediction and response to zoonotic outbreaks and pandemic risk associated with pig production systems in the Lower Mekong region.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".