Simulation modeling of the long-term evolution of local malaria transmission and acquired immunity in the context of urban growth and urban-rural travel
Bibliographic record
Abstract
Malaria occurrence is lower in urban versus rural areas of sub-Saharan Africa for a variety of reasons, including limitation of suitable mosquito habitat, generally improved housing standards and access to prevention and treatment, and a relative decrease in the ratio of vectors to humans. Nonetheless, empirical observation confirms that malaria cases, whether locally transmitted or imported, are frequently observed in cities in endemic areas. Theoretical considerations imply that local transmission will decrease as a city grows, transitioning from a relatively malaria-permissive state to one that encourages reduction or extinction. Using a simple deterministic systems dynamic model based on traditional malaria models, we simulate scenarios for the evolution of local transmission rates and acquired immunity in a homogenous urban area with respect to population size, growth rate, and level of suppression of mosquito breeding. In particular, we consider how the timing and extent of decreases in local transmission are mitigated by travel by city dwellers to endemic rural areas and their resulting exposure to infected mosquitoes. We explore the generalizability and sensitivity to assumptions of our results using microsimulation and analytic methods to account for various aspects of environmental (urban/rural) and demographic heterogeneity. The results of these simulation models should inform projections for long-term urban malaria trends, and have implications for the targeting of malaria prevention efforts in urban and peri-urban areas of sub-Saharan Africa.
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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 | 0.001 | 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 | 0.001 |
| 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".