Environmental and geographical factors influence malaria transmission in KwaZulu-Natal province, South Africa
Bibliographic record
Abstract
The malaria burden remains largely concentrated in sub- Saharan Africa. South Africa, a country within this region, has made significant progress toward malaria elimination. However, malaria continues to be endemic in three of its nine provinces: Limpopo, Mpumalanga, and KwaZulu-Natal (KZN), which are located in the northern part of the country and share borders with Botswana, Zimbabwe, and Mozambique. This study focuses on KZN, where district municipalities report monthly malaria cases ranging from zero to 8,981. Fitting Bayesian zero-inflated models in the INLA R package, we assessed the effects of various climate and environmental variables on malaria prevalence and spatio-temporal transmission dynamics from 2005-2014. Specifically, we analyzed precipitation, day and night land surface temperature, the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI) and elevation data for KZN local municipalities. Our findings indicate that the best model was the Zero- Inflated Negative Binomial (ZINB) and that at 95% Bayesian Credible Interval (CI), NDVI (0.74; CI (0.95, 3.87) is significantly related to malaria transmission in KZN, with the north-eastern part of the province exhibiting the highest risk of malaria transmission. Additionally, our model captured the reduction of malaria from 2005 to 2010 and the following resurgence. The modelling approach employed in this study represents a valuable tool for understanding and monitoring the influence of climate and environmental variables on the spatial heterogeneity of malaria. Also, this study reveals the need to strengthen the already existing crossborder collaborations to fortify KZN's malaria elimination goals.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".