Understanding the Heterogeneity of the Echinococcus multilocularis transmission patterns, processes, and mechanisms: an agent-based modeling approach
Bibliographic record
Abstract
Epidemiological models are essential in managing disease risks. However, the traditional epidemiological models are less applicable with complex life-cycle parasites. Echinococcus multilocularis (Em) is a parasite with complex life-cycle that is naturally present among wildlife and a cause for a serious zoonosis. One of the notable patterns of Em epidemiology was the spatial heterogeneity in prevalence. To explain the heterogeneity, we proposed three hypotheses, namely 1) intermediate host hypothesis, 2) definitive host hypothesis, and 3) metapopulation hypothesis. Its natural presence in wildlife makes the eradication of Em impractical and thorough understanding of its epidemiology through observation and experiments impossible. In order to understand the transmission processes and test the hypotheses, modeling is essential. Because the parasite’s transmission is indirect (through predation of intermediate hosts by definitive host), hosts display territoriality and distinct home ranges, the landscape is heterogeneous, and hosts display high diversity in the parasite load, we decided to develop a spatially-explicit agent-based model (ABM). Small mammal data from the urban parks in the City of Calgary was statistically analyzed for their statistical association to the environmental variables, and to the observed prevalence of Em among wildlife hosts. The association of small mammal community to the environmental variables were used to develop a map of small mammal communities. Fecal data of dogs and coyotes were analyzed for spatial patterns, association to the environmental variables, and to the park management. These analyses were used to develop the virtual urban landscape of the ABM, allowing the development of Calgary Echinococcus Multilocularis Coyote Agent-based model (CEMCA). While the CEMCA was successfully calibrated on coyote behaviors, the validation using the epidemiological patterns deviated from observation in some of the epidemiological patterns. However, we believe the deviations provide insights on what is unknown or important in the system. The CEMCA was used to conduct experiments on the hypotheses on spatial heterogeneity, and indicated that the intermediate host and metapopulation hypotheses are likely to be true. The CEMCA is a novel work of ABM of trophically-transmitted parasites with complex life-cycle using a complex landscape, and has many more potential use for assessing Em and epidemiology in general.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".