Predicting the spread and impact of transmissible vaccines

预测传染性疫苗的传播和影响

• 批准号: 2314616
• 负责人: Scott Nuismer
• 金额: $ 66.45万
• 依托单位: Regents of the University of Idaho
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2314616&HistoricalAwards=false
• 关键词: Predicting; spread; impact; transmissible; vaccines

Infectious diseases that normally thrive in wild animals occasionally make the leap into the human population. For instance, rabies virus infects and kills tens of thousands of people each year when wild animals carrying the virus bite humans and transmit the virus to them. Other viruses that occasionally leap from animals to humans are even more dangerous because they can also transmit from human to human and thus potentially seed epidemics or pandemics. Unfortunately, we do not yet have effective solutions in place to stop these infectious diseases from spilling over into the human population. Instead, our current approach to these animal diseases is reactive, and focuses on medical treatment of humans who have become infected and corralling human outbreaks before they can spread and become full blown epidemics or pandemics. A promising solution to this challenging problem is the development of wildlife vaccines that can spread themselves from one animal to the next. By self-disseminating, these vaccines magnify the spread of immunity within the wild animal population and reduce or eliminate the risk of spillover into the human population. Although multiple self-disseminating animal vaccines are being developed, we do not yet have the mathematical, statistical, and computational tools we need to critically evaluate their performance and thus make informed decisions about their possible use. Work on this project will develop these quantitative tools and enable candidate self-disseminating vaccines to be critically evaluated before they are used. In addition, this project will train first-generation college students from rural backgrounds to use mathematical and computational models to evaluate and optimize emerging biotechnologies critical to the future of the US economy. Student recruitment will be facilitated by offering competitive financial support that relieves pressure to abandon research experiences in favor of traditional employment. Finally, this project will continue development of a website that explains self-disseminating vaccines to the public, disseminates relevant research results, and examines the state of this emerging technology.Before making the decision to conduct even small-scale field trials, the likelihood that a self-disseminating vaccine will improve human health should be quantified. This requirement poses a formidable technical challenge because data on the behavior of the vaccine within the target animal population cannot be collected prior to release. This project will overcome this technical challenge using mathematical models of recombinant vector transmissible vaccines that can be parameterized using a combination of field and laboratory data. Specifically, mathematical models will be developed that integrate the age structure of the reservoir population and the explicit pattern of vaccine shedding from animals infected with vaccine. These models will take the form of a system of partial differential equations. Field data will come from trapping studies of the reservoir animal that record the age of each captured animal and whether it was infected by the vector virus used to construct the candidate vaccine. Laboratory data will describe the temporal pattern of vaccine shedding from reservoir animals experimentally infected with the vaccine. Approximate Bayesian computation will be used to parameterize the models and a stochastic simulation framework developed for predicting the outcome of a proposed vaccine release. By repeatedly simulating a vaccine release for models parameterized by drawing randomly from the posterior distribution, this framework faithfully integrates reservoir ecology, randomness in biological processes, and uncertainty in parameter estimates. The methodology developed by this project will be applied to a prototype self-disseminating vaccine for Lassa virus but will be broadly applicable to self-disseminating vaccines developing for a range of animal reservoirs.This project is jointly funded by the Population and Community Ecology (PCE) Cluster in the Division of Environmental Biology, the Established Program to Stimulate Competitive Research (EPSCoR), and the Mathematical Biology Program in the Division of Mathematical and Physical Sciences.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.

通常在野生动物中繁衍生息的传染病偶尔会飞向人口。例如，狂犬病病毒每年感染并杀死成千上万的人，当时野生动物携带咬人的人类并将病毒传播给它们。偶尔从动物飞向人类的其他病毒更加危险，因为它们也可以从人类传播到人类，从而可能是种子流行病或流行病。不幸的是，我们还没有有效的解决方案来阻止这些传染病溢出到人口中。取而代之的是，我们目前对这些动物疾病的方法是反应性的，并着重于对人类感染并纠正人类暴发的人类的医疗治疗，然后才能传播并变得完全吹散流行病或流行病。解决这个具有挑战性问题的有前途的解决方案是开发野生动植物疫苗，可以从一种动物到另一种动物传播。通过自我隔离，这些疫苗扩大了免疫在野生动物种群中的传播，并减少或消除了向人口溢出的风险。尽管正在开发多种自我隔离的动物疫苗，但我们尚无数学，统计和计算工具，我们需要批判性地评估其性能，从而就其可能使用的明智决定做出明智的决定。该项目的工作将开发这些定量工具，并使候选人在使用之前对候选疫苗进行严格评估。此外，该项目将培训来自农村背景的第一代大学生，以使用数学和计算模型来评估和优化对美国经济未来至关重要的新兴生物技术。通过提供有竞争力的财政支持，可以促进学生招聘，从而减轻放弃研究经验而支持传统就业的压力。最后，该项目将继续开发一个网站，该网站向公众解释了自我隔离的疫苗，传播相关的研究结果，并检查了这项新兴技术的状态。在决定进行小规模的现场试验之前，即使进行小规模的现场试验，也应量化自我触发疫苗的可能性。该要求提出了巨大的技术挑战，因为在释放之前无法收集目标动物种群中疫苗行为的数据。该项目将使用重组载体传播疫苗的数学模型来克服这一技术挑战，这些疫苗可以使用现场和实验室数据组合进行参数化。具体而言，将开发数学模型，以整合储层种群的年龄结构以及从感染疫苗的动物中脱落的疫苗的明确模式。这些模型将采用部分微分方程系统的形式。现场数据将来自对记录每种捕获动物年龄的储层动物的捕获研究，以及它是否被用于构建候选疫苗的载体病毒感染。实验室数据将描述从疫苗实验感染的储层动物中脱落疫苗的时间模式。近似贝叶斯计算将用于参数化模型，并开发了一个随机仿真框架，以预测拟议的疫苗释放的结果。通过反复模拟通过从后验分​​布随机绘制参数的疫苗释放，该框架忠实地整合了储层生态学，生物过程中的随机性以及参数估计中的不确定性。 The methodology developed by this project will be applied to a prototype self-disseminating vaccine for Lassa virus but will be broadly applicable to self-disseminating vaccines developing for a range of animal reservoirs.This project is jointly funded by the Population and Community Ecology (PCE) Cluster in the Division of Environmental Biology, the Established Program to Stimulate Competitive Research (EPSCoR), and the Mathematical Biology Program in the Division of该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响标准通过评估来支持的。