Assessing the potential for application of machine learning in predicting weather-sensitive waterborne diseases in selected districts of Tanzania
Bibliographic record
Abstract
Introduction: This study evaluates the potential of machine learning (ML) to predict and manage weather-sensitive waterborne diseases (WSWDs) in selected Tanzanian districts, focusing on environmental health officers' (EHOs) knowledge and perceptions. It explores EHOs' familiarity with information and communication technology (ICT) and artificial intelligence (AI)/ML, alongside challenges and opportunities for integrating AI-driven public health solutions. Methods: A census-style survey was conducted among EHOs in three district councils. A structured questionnaire, piloted in one district, was administered to 76 EHOs, achieving a 66% response rate. Data were analyzed using descriptive and inferential statistics to assess knowledge levels, perceptions, and gender-related differences. Results: Most EHOs were moderately familiar with ICT; however, only 54% had prior exposure to AI/ML concepts, and 64% reported limited AI familiarity. Among the variables examined, only prior exposure to AI/ML concepts and self-reported familiarity with AI demonstrated statistically significant associations with gender. Despite this, the majority recognized AI/ML's potential to improve disease prediction accuracy. Key barriers to ML adoption include inadequate technical infrastructure, data quality issues, and a shortage of expertise. Opportunities identified included utilizing historical disease data, integrating AI with meteorological information, and using satellite imagery for surveillance. Discussion: The study highlights frontline health workers' perceived barriers to ML adoption and suggests that gender influences awareness and engagement with AI and ML technologies. Strengthening technical capacity, improving data quality, and fostering cross-sector collaboration are critical for successful AI/ML integration. These insights offer a roadmap for resilience to WSWDs in developing countries like Tanzania through data-driven technologies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".