Regional variation and epidemiological insights in malaria underestimation in Cameroon
Bibliographic record
Abstract
Abstract Malaria, caused by Plasmodium parasites and transmitted by female Anopheles mosquitoes, is most common in tropical regions, especially in Sub-Saharan Africa. Despite significant global effort to control and eradicate the disease, 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, making it difficult to assess the disease burden accurately. We used a mathematical model of malaria, incorporating the underestimation of cases and seasonality in mosquito biting rate, to study the disease dynamics in Cameroon. Using a Bayesian inference framework, we calibrated our model to the monthly reported malaria cases in ten regions of Cameroon from January 2019 to December 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 1,000 people. We found varying levels of underestimation of cases across regions, with the East region having the lowest underestimation (14%) and the Northwest region with the highest (70%). The mosquito biting rate peaks once every year in most of the regions, except in the Northwest region where it peaks every 6.02 months and in Littoral every 15 months. We estimated a median mosquito biting rate of over five bites per day for most of the regions with Littoral having the highest (9.86 bites/day). Two regions have rates below five bites per day: Adamawa (4.78 bites/day) and East (4.64 bites/day). The notably low estimation of malaria cases in Cameroon underscore the pressing requirement to bolster reporting and surveillance systems. Regions in Cameroon display a range of unique features, which may contribute to the differing levels of malaria underestimation. These distinctions should be considered when evaluating the efficacy of community-based interventions. Author summary We used a deterministic mathematical model of malaria that incorporated the underestimation of cases and seasonality in the biting rate of mosquitoes to retroactively study the dynamics of the disease in Cameroon from January 2019 to December 2021. We found varying levels of underestimation of malaria cases across regions in Cameroon, with the East region having 14% underestimation and the Northwest region having 70%. We found consistent malaria-induced death rates and natural immunity duration across Cameroon. We estimated that the mosquito biting rate for the Northwest region oscillated with a period of 6.02 months, while those of the remaining regions had a period of 12 months or more. Most regions had median mosquito biting rates exceeding five bites per day, with the Littoral having the highest (9.86 bites/day). In comparison, two regions had rates below five bites per day: Adamawa (4.78 bites/day) and East (4.64 bites/day). We clustered the ten regions into four major groups using the case underestimation rate, population size, total healthcare human resources per 1,000, and total healthcare facilities per 1,000.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".