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

• 批准号: 2052355
• 负责人: Jemal Mohammed-Awel
• 金额: $ 12.24万
• 依托单位: Valdosta 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 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2052355&HistoricalAwards=false
• 关键词: Collaborative; Research; Genetic; epidemiology; framework

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疾病之一。这种疾病是由原生动物疟原虫寄生虫引起的，通过受感染的成年雌性蚊子的叮咬在人与人之间传播，并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。在全球疟疾流行地区，每年平均有2.3亿多人感染疟疾，并造成40多万人死亡（主要是5岁以下儿童）。在2000-2015年期间，大规模使用以杀虫剂为基础的干预措施，特别是长效驱虫蚊帐和室内滞留喷洒，使疟疾流行地区的疟疾负担大幅下降，促使全球共同努力，到2040年根除这一疾病。 不幸的是，自2015年以来，疟疾蚊子对长效杀虫剂和IRS中使用的所有五种化学物质都产生了广泛的抗药性。 抗药性和气候变量的变化是消灭疟疾的两个主要障碍。由于长效驱虫蚊帐和室内滞留喷雾杀虫剂是疟疾控制的基础干预措施，疟疾生态界最关键的挑战之一是确定杀虫剂抗药性是否影响疟疾流行病学。该项目将使用数学建模方法，以实验室和现场收集的新经验数据为支持，为障碍的影响，控制和缓解提供现实的见解。该项目将提供战略，利用现有的以疟疾为基础的防治资源，切实实现消灭疟疾的目标。 所产生的方法和结果将广泛应用，并用于研究基孔肯雅病、登革热、莱姆病、西尼罗河病毒和寨卡病毒等其他病媒传播疾病的传播动力学和控制。该项目将支持研究生和本科生的培训，以及当地高中学生和教师的参与，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，取决于杀虫剂的压力。该建模框架通过添加尤其是疟原虫的详细生命周期和群体遗传学（即，杀虫剂抗性的遗传学）和复杂的宿主-媒介-寄生虫相互作用。这些模型将允许评估当地气候变量（特别是温度）的变化对按基因型划分的疟疾蚊子种群数量的影响。在疟疾的背景下，建立宿主-病媒-寄生虫动力学模型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传播动力学和控制病媒（如蚊子和蜱）引起的疾病方面。具体而言，PI将（i）设计一个模型框架，用于评估杀虫剂抗性在不同环境条件下对杀虫剂抗性的适应性成本的作用，（ii）评估杀虫剂抗性对抗性蚊子传播疟疾能力的作用，以及（iii）评估自然环境因素对杀虫剂抗性基因型丰度的作用及其与疟疾发病率的关系。该项目将产生关于杀虫剂抗性的适应性成本以及抗性对蚊子体内疟原虫发育的影响的难以获得的数据，这些数据对于设计和参数化现实数学模型以研究杀虫剂抗性对疟疾蚊子和疾病动态的作用非常宝贵。这将为疟疾流行地区的抗药性管理战略的设计和测试提供一个现实的框架，因为这些地区只剩下少量的化学武器来防治这种疾病。最终，该项目将创造条件，在参数空间，有效控制或消除疟疾使用现有的基于疟疾的资源，从而有助于全球疟疾根除工作。该奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的智力价值和更广泛的影响审查标准。