Regional variation and epidemiological insights in malaria underestimation in Cameroon
Why this work is in the frame
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Bibliographic record
Abstract
Background: Despite significant global effort to control and eradicate malaria, many cases and deaths are still reported yearly. These efforts are hindered by several factors, including the severe underestimation of cases and deaths, especially in Africa. Methods: We used a mathematical model, incorporating the underestimation of cases and seasonality in mosquito biting rate, to study the malaria dynamics in Cameroon. Using a Bayesian inference framework, we calibrated our model to the monthly reported malaria cases in ten regions of Cameroon from 2019 to 2021 to quantify the underestimation of cases and estimate other important epidemiological parameters. We performed Hierarchical Clustering on Principal Components analysis to understand regional disparities, looking at underestimation rates, population sizes, healthcare personnel, and healthcare facilities per 1000 people. Results: We found varying levels of case underestimation across regions, with the East region having the lowest (14 %) and the Northwest having the highest (70 %). The mosquito biting rate peaks once every year in most regions, except in the Northwest where it peaks every 6.02 months and in Littoral every 15 months. We estimated a median mosquito biting rate of over 5 bites/day for most regions with Littoral having the highest (9.86 bites/day). Two regions have rates below five: Adamawa (4.78 bites/day) and East (4.64 bites/day). Conclusions: The low case estimation underscores the pressing requirement to bolster reporting and surveillance systems. Regions in Cameroon display a range of unique features contributing to the differing levels of underestimation. These distinctions should be considered when evaluating the efficacy of community-based interventions.
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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 it