Nets versus spraying: A spatial modelling approach reveals indoor residual spraying targets Anopheles mosquito habitats better than mosquito nets in Tanzania
Bibliographic record
Abstract
The global implementation of malaria interventions has averted hundreds of millions of clinical malaria cases in the last decade. This study assesses predicted Anopheles mosquito distributions across the United Republic of Tanzania before large-scale insecticide-treated net (ITN) rollouts and indoor residual spraying (IRS) initiatives to determine whether mosquito net usage by children under the age of five and IRS are targeted to areas where historical evidence indicates mosquitoes thrive. Demographic and Health Surveys data from 2011-2012 and 2015-2016 include detailed measurements of mosquito net and IRS use across Tanzania. Anopheline data are far less intensively collected, but we constructed a Maxent-built baseline mosquito habitat suitability (MHS) map (AUC = 0.872) with Tanzanian Anopheles occurrence records from 1999-2003. This MHS model was tested against independently-observed georeferenced Plasmodium falciparum cases from the Malaria Atlas Project, with ~87% of cases from 1999-2003 (n = 107) and ~84% of cases from 1985-2012 (n = 919) occurring in areas of high predicted suitability for mosquitoes. We compared the validated MHS with subsequent malaria interventions using mixed effects logistic regression. Specifically, we assessed whether Anopheles habitat suitability related to the frequency that ≥1 child in a household reportedly slept under a mosquito net when that intervention later became widely available, and whether IRS was reportedly applied to dwellings over a one-year period. There was no evidence that mosquito net use the night before the survey related to MHS from 2011-2012 and marginally significant evidence (p<0.05) from 2015-2016 (β = 1.466, 95% C.I. = 0.848-2.103, marginal R2 = 0.020, respectively). However, the likelihood of IRS treatments rose relatively strongly in the 12 months prior to both surveys (β = 13.466, 95% C.I. = 10.488-16.456, marginal R2 = 0.144, and β = 6.817, 95% C.I. = 5.439-8.303, marginal R2 = 0.136, respectively). IRS treatments have therefore been targeted more effectively than mosquito nets toward areas where anopheline habitat suitability was previously found to be high.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".