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Collaborative Research: Genetic-epidemiology framework for malaria mosquito and disease

合作研究：疟疾蚊子和疾病的遗传流行病学框架

基本信息

批准号：
2052363
负责人：
Abba Gumel
金额：
$ 30万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2052363&HistoricalAwards=false
关键词：
Collaborative Research Genetic epidemiology framework

项目摘要

Malaria is one of the deadliest diseases affecting mankind. The disease, which is caused by the protozoan Plasmodium parasites, is spread between humans via the bite of infected adult female mosquitoes and creates severe public health and socio-economic burdens in regions inhabited by almost half of the world’s population. Each year, malaria infects an average of over 230 million people and causes over 400,000 deaths (mostly in children under the age of five) in endemic areas globally. The widescale use of insecticides-based interventions, notably in the form of long-lasting insecticidal (LLINs) nets and indoor residual spraying (IRS), during the period 2000-2015, has resulted in a dramatic decrease in malaria burden in endemic areas, prompting a concerted global effort to eradicate the disease by 2040. Unfortunately since 2015, the malaria mosquito has developed widespread resistance to all five chemicals used in LLINs and IRS. Insecticide resistance and changes in climatic variables are two of the main impediments to malaria eradication. Since LLINs and IRS are the cornerstone interventions for malaria control, one of the most crucial challenges in the malaria ecology community is to determine whether insecticide resistance affects malaria epidemiology. This project will use mathematical modeling approaches, backed by novel empirical data collected in the laboratory as well as in the field, to provide realistic insight into the impact, control and mitigation of the impediments. The project will provide strategies for realistically achieving malaria eradication using existing insecticides-based control resources. The methodologies and results generated will be made available for broad application, and for studying the transmission dynamics and control of other vector-borne diseases such as chikungunya, dengue, Lyme disease, West Nile virus and Zika virus. The project will support the training of graduate and undergraduate students, as well as the participation of local high school students and teachers.The project will develop a genetic-epidemiology modeling framework for providing realistic insight into the malaria transmission dynamics and control, subject to insecticide pressure. The modeling framework extends the classical Ross-Macdonald compartmental modeling framework for malaria by adding, inter alia, the detailed lifecycle and population genetics of malaria mosquitoes (i.e., genetics of insecticide resistance) and the complex host-vector-parasite interactions. The models will allow for the assessment of the impacts of local changes in climatic variables (notably temperature) on the population abundance of the malaria mosquitoes by genotype. The approach of modeling the host-vector-parasite dynamics, in the context of malaria, will offer significant advances in applied mathematics and numerical analysis, particularly in designing and applying dynamical systems and numerical discretization theories and techniques for studying the transmission dynamics and control of diseases caused by vectors (such as mosquitoes and ticks). Specifically, PIs will (i) design a modeling framework for assessing the role of insecticide resistance on the fitness costs of insecticide resistance at different environmental conditions, (ii) evaluate the role of insecticide resistance on the ability of resistant mosquitoes to transmit malaria and (iii) assess the role of natural environmental factors on the abundance of insecticide resistance genotypes and how it relates to malaria incidence. This project will generate hard-to-get data on the fitness costs of insecticide resistance and impact of resistance on malaria parasite development in mosquitoes, which are so invaluable to the design and parametrization of realistic mathematical models for studying the role of insecticide resistance on the dynamics of malaria mosquitoes and disease. This will provide a realistic framework for the design and testing of resistance management strategies for malaria-endemic areas that only have a small chemical arsenal left to fight the disease. Ultimately, this project will generate conditions, in parameter space, for the effective control or elimination of malaria using existing insecticides-based resources, thereby contributing to the global malaria eradication efforts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

疟疾是影响人类的最致命的疾病之一。这种疾病由原生动物疟原虫引起，通过受感染的成年雌性蚊子的叮咬在人与人之间传播，在世界近一半人口居住的地区造成严重的公共卫生和社会经济负担。每年，疟疾平均感染2.3亿多人，并在全球流行地区造成40多万人死亡(主要是五岁以下儿童)。2000-2015年期间广泛使用以杀虫剂为基础的干预措施，特别是以长效杀虫蚊帐和室内残留喷洒(IRS)的形式，导致流行地区的疟疾负担大幅减少，促使全球共同努力，到2040年根除这种疾病。不幸的是，自2015年以来，疟疾蚊子对LLIN和IRS中使用的所有五种化学品产生了广泛的抗药性。杀虫剂抗药性和气候变量的变化是根除疟疾的两个主要障碍。由于LLIN和IRS是疟疾控制的基石干预措施，疟疾生态界最关键的挑战之一是确定杀虫剂耐药性是否影响疟疾流行病学。该项目将使用数学建模方法，并以实验室和实地收集的新的经验数据为后盾，对障碍的影响、控制和缓解提供切合实际的见解。该项目将为切实利用现有的以杀虫剂为基础的防治资源实现根除疟疾提供战略。所产生的方法和结果将被广泛应用，并用于研究其他媒介传播疾病的传播动力学和控制，如基孔肯雅病、登革热、莱姆病、西尼罗河病毒和寨卡病毒。该项目将支持研究生和本科生的培训，以及当地高中生和教师的参与。该项目将开发一个遗传-流行病学建模框架，以提供对疟疾传播动态和控制的现实见解，受杀虫剂压力的影响。该模型框架通过增加疟疾蚊子的详细生命周期和种群遗传学(即杀虫剂抗性的遗传学)和复杂的宿主-病媒-寄生虫相互作用，扩展了经典的Ross-Macdonald区隔模型框架。这些模型将能够评估当地气候变量(特别是温度)的变化对按基因划分的疟疾蚊子种群丰度的影响。在疟疾的背景下对宿主-病媒-寄生虫动态进行建模的方法将在应用数学和数值分析方面取得重大进展，特别是在设计和应用动力系统和数值离散化理论和技术以研究由病媒(如蚊子和扁虱)引起的疾病的传播动力学和控制方面。具体地说，PIS将(I)设计一个模型框架，以评估不同环境条件下抗药性对抗药性适宜性成本的影响，(Ii)评估抗药性对抗药性蚊子传播疟疾的能力的作用，以及(Iii)评估自然环境因素对抗药性基因丰度的作用及其与疟疾发病率的关系。该项目将产生难以获得的关于杀虫剂耐药性的适宜性成本以及耐药性对蚊子中疟疾寄生虫发展的影响的数据，这些数据对于研究杀虫剂耐药性对疟疾蚊子和疾病动态的作用的现实数学模型的设计和参数化是非常宝贵的。这将为设计和测试疟疾流行地区的耐药性管理战略提供一个现实的框架，这些地区只剩下少量化学武器库来抗击这种疾病。最终，该项目将在参数空间创造条件，利用现有的基于杀虫剂的资源有效控制或消除疟疾，从而为全球根除疟疾的努力做出贡献。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。