Network transmission inference: Host behavior and parasite life cycle make social networks meaningful in disease ecology
Bibliographic record
Abstract
The process of disease transmission is determined by the interaction of host susceptibility and exposure to parasite infectious stages. Host behavior is an important determinant of the likelihood of exposure to infectious stages but is difficult to measure and often assumed to be homogenous in models of disease spread. We evaluated the importance of precisely defining host contact when using networks that estimate exposure and predict infection prevalence in a replicated, empirical system. In particular, we hypothesized that infection patterns would be predicted only by a contact network that is defined according to host behavior and parasite life cycle. Two competing host contact criteria were used to construct networks defined by parasite life cycle and social contacts. First, parasite-defined contacts were based on shared space with a time delay corresponding to the environmental development time of nematode parasites with a direct fecal-oral life cycle. Second, social contacts were defined by shared space in the same time period. To quantify the competing networks of exposure and infection, we sampled natural populations of the eastern chipmunk (Tamias striatus) and infection of their gastrointestinal helminth community using replicated longitudinal capture-mark-recapture techniques. We predicted that (1) infection with parasites with direct fecal-oral life cycles would be explained by the time delay contact network, but not the social contact network; (2) infection with parasites with trophic life cycles (via a mobile intermediate host; thus, spatially decoupling transmission from host contact) would not be explained by either contact network. The prevalence of fecal-oral life cycle nematode parasites was strongly correlated to the number and strength of network connections from the parasite-defined network (including the time delay), while the prevalence of trophic life cycle parasites was not correlated with any network metrics. We concluded that incorporating the parasite life cycle, relative to the way that exposure is measured, is key to inferring transmission and can be empirically quantified using network techniques. In addition, appropriately defining and measuring contacts according the life history of the parasite and relevant behaviors of the host is a crucial step in applying network analyses to empirical systems.
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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 | 0.006 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".