Identifying gaps between LLIN use and vector exposure to improve malaria control
Identifying gaps between LLIN use and vector exposure to improve malaria control
批准号:
10443559
负责人:
Paul Joseph Krezanoski
金额:
$19.71万
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-07-01 至 2024-06-30
关键词:
AddressAdherenceAffectAfricaAfricanAnopheles GenusAreaAwardBiteBloodCessation of lifeCharacteristicsChildClassificationCohort StudiesCulicidaeDataEntomologyEpidemiologyExposure toFemaleFutureGenotypeGoalsHealthHourHouseholdHumanIncidenceIndividualInsecticidesInternationalInterventionLeadLifeLinkLocationLongitudinal cohortMalariaMalaria preventionMeasurementMeasuresMentored Patient-Oriented Research Career Development AwardMentorshipModelingMonitorMultivariate AnalysisNational Institute of Allergy and Infectious DiseaseParticipantPatient Self-ReportPatternPersonsPredispositionPreventionPublic HealthReportingResearchResearch ActivityResolutionRiskSleepTestingTrainingUgandaUnited States National Institutes of HealthUniversal CoverageUpdateVariantVertebral columnWorkWorld Health Organizationbasecareercohortdensitydesigndisease transmissionexperiencehigh riskimprovedinfectious disease modelinnovationmalaria transmissionnew technologynovel strategiesprogramsscale upsensorskillstooltransmission processvector
中文摘要
项目总结/摘要
疟疾影响着全世界30亿人。尽管过去一年疟疾发病率显著下降,
15年来,最近的证据表明,我们传统的控制工具正在减弱。长效驱虫
蚊帐是最广泛使用的疟疾预防工具,对降低疟疾发病率作出了重大贡献,但最近的研究表明,蚊帐的效果不如以前,
并没有像报道的那样使用它们。严格评估病媒接触的时间和地点如何与长效驱虫蚊帐的实际使用交叉,对于重新获得疟疾控制的主动权至关重要。但
缺乏对长效杀虫剂使用情况的可靠衡量标准是一项重大挑战。目前的测量工具,如自我报告
使用,是主观的,不能解释使用中的时间变化。为了解决这些局限性,我发明了
LLIN使用的电子监视器。SmartNet使用嵌入在标准LLIN中的传感器,
它是否展开的准确率高达98%我们已经完成了成功的可行性,可接受性和现场试验
智能网这一项目的主要理由是,持续监测单个长效杀虫剂吸入器的使用情况,
与疟疾病媒接触的量化将允许比以前更可靠的分析
长效驱虫蚊帐如何在实践中减少病媒接触。本K23方案的研究目标是开发高产
通过查明接触病媒的个人风险与
个人LLIN使用。为了促进这项工作,我有机会在乌干达纵向队列的480人。
我们的方法利用了在这个群体中每两周收集一次的密集昆虫学监测。此外,我们将在每个睡眠空间部署智能网络,以覆盖多个睡眠空间中的每个人。
年将通过追求三个具体目标来确定高收益的干预措施:
向量,并确定与高风险相关的因素,2)量化LLIN的使用,并确定与
3)查明长效驱虫蚊帐的使用与病媒接触之间的不匹配,制定解决这些差距的干预措施,然后有系统地确定减少病媒-人传播的最有效干预措施
接触使用一个模型的向量曝光。我的长期职业目标是建立一个独立的研究
职业发展创新方法,以改善疟疾控制。此K23提案补充了我之前的
在疟疾昆虫学和流行病学以及传染病建模方面的指导和培训经验。拟议的研究活动和补充培训旨在共同促成一项强有力的
未来的工作计划。我将从这个奖项出来准备一个强大的NIH R 01应用程序,
方法在不同的传输设置,开发业务研究的高产干预措施,我们
确定并扩大范围,从减少病媒与人的接触到减少疟疾的实际发病率。这
K23奖为我目前的经验和实现我的职业目标提供了至关重要的联系,
一位国际领导者,发明、部署和测试改进疟疾预防的创新方法。
英文摘要
PROJECT SUMMARY/ABSTRACT
Malaria affects three billion people worldwide. Despite remarkable reductions in malaria incidence over the last
15 years, recent evidence shows that our traditional control tools are weakening. Long-lasting insecticide-treated
bednets (LLINs) are the most widely used tool for malaria prevention and have contributed significantly to decreases in malaria incidence, but recent studies suggest that LLINs are either less effective than before or people
are not using them as reported. A rigorous assessment of how the timing and location of vector exposure intersects with real-life use of LLINs could be vitally important to regain the initiative in malaria control. However, the
lack of a reliable measure of LLIN use presents a major challenge. Current measurement tools, like self-reported
use, are subjective and unable to account for temporal variations in use. To address these limitations, I invented
an electronic monitor of LLIN use. SmartNet uses sensors embedded in a standard LLIN to continuously assess
whether it is unfurled with 98% accuracy. We have completed successful feasibility, acceptability and field trials
of SmartNet. The central rationale for this project is that continuous monitoring of individual LLIN use combined
with quantified exposure to malaria vectors will allow a more robust analysis than has previously been possible
of how LLINs reduce vector exposure in practice. The research goal of this K23 proposal is to develop high-yield
interventions for improving malaria control by identifying gaps between individual risk of vector exposure and
individual LLIN use. To facilitate this work, I have access to a longitudinal cohort of 480 individuals in Uganda.
Our approach leverages intensive entomology surveillance already being gathered every two weeks in this cohort. Additionally, we will deploy SmartNets over every sleeping space to cover every individual over multiple
years. High-yield interventions will be identified by pursuing three specific aims: 1) quantify exposure to malaria
vectors and identify factors associated with higher risk, 2) quantify LLIN use and identify factors associated with
poor adherence and 3) identify mismatches between LLIN use and vector exposure, develop interventions addressing these gaps and then systematically determine the highest-yield interventions for reducing vector-human
contact using a model of vector exposure. My long-term career goal is to establish an independent research
career developing innovative approaches for improving malaria control. This K23 proposal supplements my prior
experience with mentorship and training in malaria entomology and epidemiology and infectious disease modelling. Together, the proposed research activities and complementary training are designed to lead to a robust
program of future work. I will emerge from this award prepared for a strong NIH R01 application to apply this
approach in different transmission settings, to develop operational studies of the high-yield interventions we
identify and to expand the scope from reducing vector-human contact to reducing actual malaria incidence. This
K23 award provides the crucial link between my current experience and achieving my career goal of becoming
an international leader inventing, deploying and testing innovative approaches for improving malaria prevention.
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会议论文
Training of machine learning algorithms for the classification of accelerometer-measured bednet use and related behaviors associated with malaria risk
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批准号:10727374
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项目类别:
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资助金额:$11.5万
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财政年份:2023
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负责人:Paul Joseph Krezanoski
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依托单位:
Identifying gaps between LLIN use and vector exposure to improve malaria control
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批准号:10647800
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项目类别:
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资助金额:$19.72万
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财政年份:2019
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负责人:Paul Joseph Krezanoski
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依托单位:
海外基金