Malaria Spatio-Temporal Patterns in Busia and Tororo Districts, Eastern Uganda
Bibliographic record
Abstract
Abstract Malaria burden remains one of the major public health challenges in sub-Saharan Africa, Uganda inclusive. Uganda has the 3rd highest global disease cases estimated to be 225 million and the 8th highest level of deaths equivalent to 781,000 per year. Malaria remains a leading cause of morbidity and mortality in Uganda, accounting for 30-50% of outpatient visits at health facilities, 15-20% of all hospital admissions, and up to 20% of all hospital deaths with at least 27.2% of inpatient deaths among children under five years of age. Widely recommended Plasmodium vector control approaches include utilization of long-lasting insecticidal nets and indoor residual sprays which are insecticide-based. This study assessed a nine-year period malaria cases data (2012-2020) obtained from the health management database to depict malaria spatial and temporal patterns in Busia and Tororo districts pre and post-vector control interventions. The routine malaria surveillance data reported passively through public and high-volume private health facilities were entered and manipulated into MS Excel. This was done separately for each of the 9 years. Considering the malaria cases registered on annual basis, Mann- Kendal test revealed a drastic decline of malaria cases over the nine-year period (2012 -2020) in Tororo district with Sen’s slope of -22, while for Busia district it revealed an increase of malaria cases (Sen’s slope +28). Equally, these trends reveal varied spatial patterns over the two districts. Both Busia and Tororo revealed a high prevalence of malaria between May-November in respect to bimodal rainfall pattern, matching with the perennial transmission setting of Uganda. This study has shown that, for further epidemiological characterization, vector behavior, biology and physiology need consistent monitoring and surveillance while implementing new vector control interventions targeting outdoor biting Plasmodium vectors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".