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
Bibliographic record
Abstract
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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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".