Spatio-temporal analysis of environmental and climatic factors impacts on malaria morbidity in Ondo State, Nigeria
Bibliographic record
Abstract
This study examined the spatio-temporal dynamics of malaria epidemiological patterns considering environmental(vegetation, water bodies, slope, elevation) and climatic factors (rainfall, temperature and relative humidity) in Ondo State, Nigeria, from 2013 to 2017 using ArcGIS 10.4 and QGIS software. The factors influencing malaria were studied using a multi-criteria analysis (Analytical Hierarchical Process-AHP). The trend analysis revealed an increase in cases over time, indicating a significant increase in the occurrence of malaria in all study areas. The most important climatic variable impacting malaria transmission in the study was temperature. Nevertheless, other environmental and climatic factors causing transmission include vegetation, water bodies, slopes, elevation, rainfall, and relative humidity. With the exception of Okitipupa, the study identified high-risk locations (vulnerable areas/hot spots) in almost all of the local government areas, while Ondo East, Akure South, Akoko South West, and Owo are the most vulnerable areas. The findings reveal that the malaria incidence is high in the developed LGAs having more towns where temperature is higher due to several anthropogenesis activities, high population and increased land-use. Thus, in-depth epidemiological studies on malaria should be undertaken in Ondo State and other regions of Nigeria considering environmental factors impacting malaria incidence as this will enable one to ascertain the major factors influencing the disease, thereby taking adequate measures to curb the increase in incidence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".