PIPP Phase I: Transdisciplinary Innovation in Predictive Science for Emerging Infectious Disease and Spillover

鉴于全球环境变化的趋势，预计流行病和大流行病的规模和影响在今后几年将大幅度增加。大流行病通常是由溢出事件引起的，在这种事件中，野生动物的病毒跳到人身上并传播，迅速超过了遏制措施。流行病最好在出现的最初阶段得到控制，但在早期发现所需规模的流行病威胁方面存在重大障碍。我们提出了一项雄心勃勃的科学议程，以克服阻碍发现和定性新出现的大流行病威胁的技术限制。我们的团队涵盖工程学、计算机科学、社会科学、流行病学和病毒学，与来自公共卫生、野生动物健康和工业的利益相关者合作，是首个为潜在流行病推进预测智能的团队。我们将开发新的疾病监测技术，将创新的传感技术、流行病学见解和人工智能策略结合起来，以检测存在的动物和它们携带的病毒--所有这些都是远程的，几乎不需要人为干预。我们寻求发展多元化，包容性和高功能的跨学科研究团队和学员，超越流行病预测情报的最新水平，为国家和全球卫生安全提供信息。我们应对重大挑战，即在疾病从动物蔓延到人类时，为早期发现大流行威胁提供可操作的情报。我们正在计划建立一个跨学科中心和相关研究，以提高我们描述环境变化和动物-人类相互作用的能力，这些变化和动物-人类相互作用促进了大流行威胁的出现，并发展预测大流行风险的预测能力，以建立可以为大流行预防提供信息的证据基础。研究进展将集中在1）表征疾病出现的环境条件和人类层面，特别是动物和人类运动，网络和社会系统，促进动物-人类界面的溢出，2）发明广谱传感器技术，用于快速，准确，安全和可扩展的病原体检测，适用于各种环境中的一系列动物，以及3）通过将流行病学和景观数据与机器学习相结合来改进预测，以提高预测智能。我们的团队将促进创造性的想法，新的方法和技术，跨学科的合作和沟通，参与式社区科学的融合，以及为高度多样化的劳动力进行创新的交叉培训。该奖项得到了跨部门的大流行病预防第一阶段预测情报（PIPP）计划的支持，该计划由生物科学部门（BIO）共同资助;计算机、信息科学与工程（CISE）;工程（ENG）和社会、行为和经济科学（SBE）。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The scale and impact of epidemics and pandemics are expected to increase substantively in future years given global trends in environmental change. Pandemics are most often caused by spillover events in which viruses from wild animals jump to people and spread, quickly outpacing containment efforts. Pandemics are best contained at the earliest stages of emergence, yet major hurdles have stood in the way of early detection of pandemic threats at the scale that is needed. We set forth an ambitious scientific agenda to overcome technological limitations impeding detection and characterization of emerging pandemic threats. Our team spanning engineering, computer science, social science, epidemiology, and virology with stakeholders from public health, wildlife health, and industry is first-of-a-kind to advance predictive intelligence for potential pandemics. We will develop new disease surveillance technologies that incorporate innovative sensing, epidemiological insights, and artificial intelligence strategies, to detect what animals are present and what viruses they carry - all remotely with little human intervention. We seek to develop diverse, inclusive, and highly functional transdisciplinary research teams and trainees, to move beyond the state of the art in predictive intelligence for pandemics to inform national and global health security. We address the grand challenge of generating actionable intelligence for early detection of pandemic threats at the point of disease spillover from animals to people. We are planning a transdisciplinary center and associated research to advance our ability to characterize environmental change and animal-human interactions that facilitate the emergence of pandemic threats, and develop predictive capabilities in forecasting pandemic risk, to enable an evidence-base that can inform pandemic prevention. Research advances will be focused on 1) characterizing environmental conditions and the human dimensions of disease emergence, especially the animal and human movements, networks, and social systems that facilitate spillover at the animal-human interface, 2) inventing broad-spectrum sensor technologies for rapid, accurate, safe, and scalable pathogen detection tailored to a range of animals in diverse environments, and 3) improving forecasting by integration of epidemiologic and landscape data with machine learning to advance predictive intelligence. Our team will foster the convergence of creative ideas, novel approaches and technologies, transdisciplinary collaboration and communication, participatory community science, and cross-training for innovation for a profoundly diverse workforce.This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO); Computer, Information Science and Engineering (CISE); Engineering (ENG) and Social, Behavioral and Economic Sciences (SBE).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.