Spatial distribution of suspected and confirmed cholera cases in Mwanza City, Northern Tanzania
Bibliographic record
Abstract
Cholera, which is caused by Vibrio cholerae, persists as a devastating acute diarrheal disease. Despite availability of information on socio-cultural, agent and hosts risk factors, the disease continues to claim lives of people in Tanzania. The present study explores spatial patterns of cholera cases during a 2015-16 outbreak in Mwanza, Tanzania using a geographical information system (GIS) to identify concentrations of cholera cases. This cross-sectional study was conducted in Ilemela and Nyamagana Districts, Mwanza City. The two-phase data collection included: 1) retrospectively reviewing and capturing 852 suspected cholera cases from clinical files during the outbreak between August, 2015, and April, 2016, and 2) mapping of residence of suspected and confirmed cholera cases using global positioning systems (GPS). A majority of cholera patients were from Ilemela District (546, 64.1%), were males (506, 59.4%) and their median age was 27 (19-36) years. Of the 452 (55.1%) laboratory tests, 352 (77.9%) were confirmed to have Vibrio cholerae infection. Seven patients (0.80%) died. Cholera cases clustered in certain areas of Mwanza City. Sangabuye, Bugogwa and Igoma Wards had the largest number of confirmed cholera cases, while Luchelele Ward had no reported cholera cases. Concentrations may reflect health-seeking behavior as much as disease distribution. Topographical terrain, untreated water, physical and built environment, and health-seeking behaviors play a role in cholera epidemic in Mwanza City. The spatial analysis suggests patterns of health-seeking behavior more than patterns of disease. Maps similar to those generated in this study would be an important future resource for identifying an impending cholera outbreak in real-time to coordinate community members, community leaders and health personnel for guiding targeted education, outreach, and interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".