Where environment and malaria intersect: Exploring the spatio-temporal footprints of Plasmodium falciparum in Tanzania
Bibliographic record
Abstract
Malaria remains a public health crisis in Tanzania, with persistent morbidities and mortalities. Malaria etiology is multifactorial, with environmental factors playing a vital role in mosquito development and malaria transmission. In Tanzania and most of Sub-Saharan Africa, the Plasmodium falciparum parasite remains the most prevalent and virulent malaria parasite. Using data from the Tanzania Demographic and Health Surveys and spatio-temporal analysis, we explore the environmental determinants of P. falciparum across different regions in Tanzania over the last 2 decades. The hotspots analysis showed that the Kigoma and Kagera regions in the north-west of Tanzania as well as the Lindi and Mtwara regions in southern Tanzania were consistently hotspots of P. falciparum malaria from 2000 to 2020. Our findings also reveal and reinforce the role of environmental factors in mediating malaria epidemiology in Tanzania. Factors such as the use of insecticide-treated nets, population, evapotranspiration and aridity were often adversely associated with P. falciparum incidence. In contrast, vegetative landcover, temperature, precipitation, and the number of wet days were directly associated with P. falciparum in Tanzania. However, the relationship between these environmental factors and malaria prevalence varied temporally and spatially. Our findings further showed that, the two most important environmental factors that mediate P falciparum incidence in Tanzania over the last two decades were precipitation and aridity. Other vital predictors included the use of insecticide nets and the number of wet days. The findings provide policy pointers for targeted malaria interventions in Tanzania in the context of environmental change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".