GIS-Based Model for Mapping Malaria Risk under Climate Change Case Study: Burundi
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Malaria is one of the largest problems threatening global public health that is expected to increase in the future under climate change due to associated warming and wetter conditions. This will exacerbate disease burden in Burundi as one of sub-Saharan African countries, where 2 million cases of malaria were reported in 2015. This highlights the need for developing a methodology for mapping malaria risk under climate change and delineating those regions that may potentially experience malaria epidemics in the future. Malaria transmission and distribution are generally determined by a wide range of climatic, topographic and socioeconomic factors. The paper in hand is intended to map malaria risk in Burundi under climate change up to 2050. For this purpose, a GIS-based model was developed for mapping malaria as a function of various climatic and topographic determinants of malaria. The developed GIS-model was used in mapping malaria risk under current climatic conditions. Thereafter, the produced risk map was validated compared to malaria morbidity data in Burundi at health district level. Finally, the GIS-model was applied to map malaria risk in the future under RCPs 2.6 and 8.5 scenarios up to 2050. It was found that about 34.6% and 44% of Burundi land surface will be highly vulnerable to malaria risk by 2050 under RCPs 2.6 and 8.5 scenario, respectively. Also, it was noted that such highly vulnerable areas are distributed mainly in northern parts of the country. The suggested GIS-based model for mapping malaria risk under climate change can contribute largely to more informed decision-making and policy making process in terms of planning for intervention and control malaria risk. This in turn can support reducing disease burden and improving resilience to climate change.
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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 | 0.001 | 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 | 0.001 | 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