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Background <p><i>Aedes</i>-borne arboviral diseases, especially dengue, are on the rise. The global expansion of <i>Aedes</i> 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 <i>Aedes-</i>borne diseases.</p> Methods <p>In this study, we collected <i>Aedes</i> 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 <i>Aedes</i> breeding and temporal trends in entomological indices pertinent to disease risk prediction.</p> Results <p>Ovitraps and larval sampling identified different months as most conducive for <i>Ae. aegypti</i> 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 <i>Ae. aegypti</i> 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 <i>Aedes</i> presence.</p> Conclusions <p>Our 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.</p>

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Climate-driven models reveal temporal trends in Aedes breeding: implications for outbreak preparedness and control interventions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月12日

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  • Jewelna Akorli,
  • Stephen K. D. Oware,
  • Derrick Baah Sackitey,
  • Reuben M. Pul,
  • Nukunu E. Akyea-Bobi,
  • Samuel S. Akporh,
  • Godwin K. Amlalo,
  • Joseph H. N. Osei,
  • Helena A. Boakye,
  • Mufeez Abudu,
  • Esinam A. Akorli,
  • Kwadwo K. Frempong,
  • Sellase Pi-Bansa,
  • Millicent Opoku,
  • Samuel K. Dadzie

摘要

Background

Aedes-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.

Methods

In 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.

Results

Ovitraps 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.

Conclusions

Our 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.