ArticleBMC public health2025
Climate-driven models reveal temporal trends in Aedes breeding: implications for outbreak preparedness and control interventions.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Physics-guided framework for climate dependent disease prediction: coupling compartment model with deep learning.BMC public health · 2026Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
backgroundAedes-borne arboviral diseases, especially dengue, are on the rise. The global expansion of Aedes mosquitoes, driven by changing climatic conditions, has led to the emergence of these diseases in previously non-endemic regions. Effective entomological surveillance, which considers changes in weather conditions, is crucial for the timely detection, emergency preparedness and control of Aedes-borne diseases.
methodsIn this study, we collected Aedes eggs using ovitraps and estimated peri-domestic larval indices from urban locations over 6 months. Regression models were applied to determine the influence of meteorological factors on Aedes breeding and temporal trends in entomological indices pertinent to disease risk prediction.
resultsOvitraps and larval sampling identified different months as most conducive for Ae. aegypti breeding activities, highlighting the impact of sampling method on surveillance and data interpretation for outbreak preparedness. Temperature and wind speed were identified as strong predictors of the entomological indices investigated. Rainfall was not a consistent predictor of Ae. aegypti breeding, except when in combination with temperature or wind speed. The presence of unused tyres within the peri-domestic environment was identified as a primary breeding site, but other open receptacles such as discarded cans and broken clay pots, especially in the absence of tyres, also contributed to Aedes presence.
conclusionsOur models demonstrated predictive potential for estimating vector populations and risk of disease outbreaks, which could help in emergency preparedness and the deployment of control measures. Additionally, these models offer a tool for anticipating shifts in vector distribution under changing climate conditions, providing valuable insights for improving disease prevention strategies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.