Contact in context: Animal profiles, human activities, and land use histories shape human-animal contacts with implications for zoonotic spillover in the Democratic Republic of Congo
Notice bibliographique
Résumé
Abstract Pathogenic spillovers from animals into humans have catalyzed epidemics throughout history. They result from multiple factors. One such factor, human-animal contact, remains poorly understood. Current studies often neglect variability in human-animal engagements across ecological zones and the broader processes bringing people, animals, and pathogens into engagement. We investigated factors and longer-term processes shaping human-animal contacts and risks of zoonotic spillover in a region experiencing landscape fragmentation. We conducted our mixed-methods investigation in three villages along an ecological gradient of forest fragmentation in the Democratic Republic of Congo (DRC), a hotspot of biodiversity and disease emergence. Among 24 village participants, we collected daily activities and contacts with highly diverse animal species to evaluate the types and frequencies of these contacts. We developed a cluster analysis to categorize classes of animals according to type and frequency of contact. We also conducted transects to estimate animal species abundance according to village proximity. We tested the influence of animal species abundance, human activities, gender, and village on human-animal contact frequency. We conducted ethnographic and ethnohistorical interviews and observations to explore changing human-animal relations. Participants had physical and environmental contact with 61 different animal species. We found three classes of animal species with which participants had most frequent physical and environmental contact. Historical processes and human activities, avoidance toward some animal species, and relative abundance of species contribute to shape contemporary human-animal contacts, and more broadly, potential risks of exposures to zoonotic pathogens. Our modeling of gender, village, relative abundance and human activities on animals clustered by contact frequency, however, yielded few predictors of contact frequency. We identify factors and processes associated with human-animal contacts in an ecologically varied zone and its categorization of contact profiles. Future studies should explore a wider array of human-animal contacts and situate them in their historical contexts. Author Summary Pathogenic spillovers from animals into humans have catalyzed epidemics throughout history. These spillovers result from many factors, although one –– human-animal contact –– is poorly understood. The variability of human-animal interactions and historical changes shaping interactions between people, animals, and pathogens are not well addressed. Our mixed-methods study explored factors and longer-term processes affecting human-animal contacts and risks of spillover in a fragmented forest of the Democratic Republic of Congo. We found that participants had physical and environmental contact with 61 different animal species. We identified three classes of animal species with which participants had most frequent physical and environmental contact. These classes were shaped by three major factors: historical changes affecting human activities and ecologies; human preferences to avoid certain species; and relative abundance of animal species. More broadly, these classes reflected potential risks of exposures to zoonotic pathogens. Although our model to predict how gender, village, relative abundance and human activities influenced these animal classes clustered by contact frequency, it yielded few predictors. Our study did, however, identify factors and processes associated with human-animal contacts in an fragmented forest zone. We recommend that future studies explore a wider array of human-animal contacts, situating them in their historical contexts.
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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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».