Here we conducted a systematic review and meta-analysis to reach a consensus on whether infected and uninfected mosquitoes respond differently to repellents. After screening 2,316 published studies, theses, and conference abstracts, we identified 18 studies that tested whether infection status modulated the effectiveness of repellents. Thirteen of these studies had outcomes available for meta-analysis, and overall, seven repellents were tested (typically DEET with 62% of outcomes), six mosquito species had repellence behaviors measured (typically Aedes aegypti (L.) (Diptera: Culicidae) mosquitoes with 71% of outcomes), and a broad diversity of infections were tested including Sindbis virus (Togaviridae: Alphavirus) (33% of outcomes), Dengue (Flaviviridae: Flavivirus) (31%), malaria (Plasmodium berghei Vincke & Lips (Haemospororida: Plasmodiidae) or P. falciparum Welch (Haemospororida: Plasmodiidae); 25%), Zika (Flaviviridae: Flavivirus) (7%), and microsporidia (4%). Pooling all outcomes with meta-analysis, we found that repellents were less effective against infected mosquitoes—marking an average 62% reduction in protective efficacy relative to uninfected mosquitoes (pooled odds ratio = 0.38, 95% confidence interval = 0.22–0.66; k = 96). Older infected mosquitoes were also more likely to show altered responses and loss of sensitivity to repellents, emphasizing the challenge of distinguishing between age or incubation period effects. Plasmodium- or Dengue-infected mosquitoes also did not show altered responses to repellents; however, Dengue–mosquito systems used inoculation practices that can introduce variability in repellency responses. Given our findings that repellents offer less protection against infected mosquitoes and that these vectors are the most dangerous in terms of disease transmission, then trials on repellent effectiveness should incorporate infected mosquitoes to improve predictability in blocking vector–human contact.
This paper presents a comprehensive study on transportation and urban mobility optimization by synthesizing and analyzing a collection of relevant research papers. The selected papers cover a wide range of topics, including air corridor evaluation, sentiment analysis in electric vehicle discussions, fluid simulation parallelization, integration of urban air mobility, ridership and operations visualization, economies of scale in mobility sharing markets, prediction of last-mile delivery routes, and machine learning in choice analysis and travel behavior prediction.The research findings highlight the significance of purpose-specific metrics in evaluating air corridors for urban air mobility operations, providing insights for safe and efficient integration. Additionally, sentiment analysis reveals positive attitudes towards electric vehicles, supporting strategies for promoting their adoption. The parallelization of fluid simulation offers improved computational modeling capabilities, benefiting industries such as aerospace and manufacturing.Integration of urban air mobility into existing transportation systems requires coordinated planning, addressing infrastructure, airspace management, and regulatory challenges. The introduction of visualization tools aids in understanding transit performance and optimizing passenger journeys. Analysis of mobility sharing markets highlights the importance of balanced growth and mitigating operational complexities and regulatory compliance.Prediction of last-mile delivery routes using advanced neural networks enables efficient logistics operations. The comparative analysis demonstrates the potential of machine learning classifiers in accurately predicting travel behavior. The results contribute to decision-making processes for policymakers, urban planners, and industry stakeholders in enhancing transportation systems' efficiency, sustainability, and user satisfaction.This study consolidates research findings, identifies research gaps, and provides valuable insights for future exploration in the field of transportation and urban mobility optimization. The comprehensive analysis serves as a resource for researchers, practitioners, and policymakers seeking to improve transportation systems and address emerging challenges in the dynamic urban environment.
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