Characterising the epidemiology of influenza A viruses at the swine-human interface: Study Protocol of the PigFluCam+ project in Cambodia (Preprint)
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,030 | 0,011 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».