Determinants of arbovirus vector density as a measure of transmission risk in regions of recent Zika virus introduction in the Americas
Bibliographic record
Abstract
Abstract Background The global impact of Zika virus in Latin America and the Caribbean has drawn renewed attention to circulating mosquito-borne viruses in this region, such as dengue and chikungunya. Our objective was to assess socio-ecological factors associated with Aedes mosquito vector density as a measure of arboviral transmission risk in three cities of potential recent Zika virus introduction: Ibagué, Colombia; Manta, Ecuador; and Posadas, Argentina, in order to inform disease mitigation strategies. Methods We monitored Aedes mosquito populations over 12 months starting in 2018 in a total of 1,086 randomly selected households, using indoor and peridomestic mosquito collection methods. For each sampled household, we collected socio-economic data using structured questionnaires and data on microenvironmental conditions using iButton data loggers. Results A total of 3,230 female Aedes mosquitoes were collected, of which 99.8% were Ae. aegypti and 0.2% were Ae. albopictus. Mean female Aedes spp. mosquito abundance per household was 1.71. We modeled the interaction between neighborhood and household wealth profiles, and found significantly lower Aedes density in highest compared to lowest and middle wealth households, although the effect differed by level of neighborhood wealth. We used mixed-effects Poisson regression analyses to identify predictors of Aedes density, using month, neighborhood, and country as random-effect variables. Across study sites, the number of household occupants (Incidence rate ratio, IRR = 1.08), presence of entry points for mosquitoes into the household (IRR = 1.51), and presence of decorative vegetation (IRR = 1.52) were associated with higher Aedes density; while being in the highest wealth tertile of household wealth (IRR = 0.78), knowledge of how arboviruses are transmitted (IRR = 0.94) and regular emptying of water containers by occupants (IRR = 0.79) were associated with lower Aedes density. Conclusions Our study addresses the complexities of arbovirus vectors of global significance at the interface between society and the environment. Our results point to several determinants of Aedes mosquito vector density in countries with co-circulation of multiple Aedes-borne viruses, and could help us identify targets of practical intervention for disease prevention and control.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".