PIPP Phase I: Real-time Analytics to Monitor and Predict Emerging Plant Disease

植物病害的爆发正在增加，威胁着世界许多地区和美国弱势群体的粮食安全。 需要稳定、有营养的粮食供应来帮助人们摆脱贫困并改善健康状况。植物病害导致主食作物损失20%至30%。常见和新近出现的植物疾病正在蔓延，并因气候变化、全球粮食贸易网络的传播以及可能难以控制的新菌株的出现而加剧。该研究小组将开发更好的方法来检测和预测植物疾病何时何地出现。这项研究将描述人类的态度和利益相关者的社会行为如何影响植物疾病的传播和传感器，监测和疾病预测技术的采用。 该团队将通过参与研究和研讨会，吸引不同的博士后、研究生和研究人员，并为未来的植物疾病大流行防范中心培养合作伙伴关系。由于缺乏实时检测、监测和数据分析，植物疾病大流行的预测是不可靠的，无法为决策提供信息并防止传播。这是融合研究团队将在大流行预防预测智能（PIPP）规划拨款中解决的重大挑战。为了改善大流行预测并应对这一重大挑战，需要一套新的预测工具。在PIPP第一阶段项目中，多学科团队将开发一个名为“植物援助数据库（PAdb）”的大流行预测系统，该系统将通过原位植物疾病传感器进行病原体检测和作物健康遥感，基因组监测，实时时空数据分析和气候数据，以开发植物疾病大流行的预测模拟。该团队计划使用几种模式植物病原体来验证PAdb，包括致病疫霉和葫芦科霜霉病病原体黄瓜霜霉病菌的新谱系。他们计划在一个大流行准备研讨会上邀请广泛的利益相关者，包括科学家、种植者、推广专家、美国农业部动植物检疫局植物保护和检疫人员、国土安全部检查员和国家植物诊断网络的诊断专家。将使用当前的方法和新的PAdb的帮助来检查病原体和利益相关者经验的响应和传播的差异。该奖项由跨部门的大流行病预防阶段预测情报（PIPP）计划支持，该计划由生物科学（BIO），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，并被认为是值得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的支持。

Plant disease outbreaks are increasing and threatening food security for the vulnerable in many areas of the world and in the US. A stable, nutritious food supply is needed to both lift people out of poverty and improve health outcomes. Plant diseases cause crop losses from 20% to 30% in staple food crops. Plant diseases, both common and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, and emergence of new strains that may be difficult to control. This team of researchers will develop better ways to detect and predict when and where plant diseases will emerge. This research will characterize how human attitudes and social behavior of stakeholders impacts plant disease transmission and adoption of sensor, surveillance and disease prediction technologies. The team will engage a diverse group of postdoctoral associates, graduate students and research staff through research and workshop participation and foster partnerships for a future Plant Disease Pandemic Preparedness Center.Prediction of plant disease pandemics is unreliable due to the lack of real-time detection, surveillance, and data analytics to inform decision-making and prevent spread. This is the grand challenge that the convergence research team will tackle in this Predictive Intelligence for Pandemic Prevention (PIPP) planning grant. In order to improve pandemic prediction and tackle this grand challenge, a new set of predictive tools is needed. In the PIPP Phase I project, the multidisciplinary team will develop a pandemic prediction system called the “Plant Aid Database (PAdb)” that links pathogen detection by in-situ plant disease sensors and remote sensing of crop health, genomic surveillance, real-time spatial and temporal data analytics and climate data to develop predictive simulations of plant disease pandemics. The team plans to validate the PAdb using several model plant pathogens including novel lineages of Phytophthora infestans and the cucurbit downy mildew pathogen Pseudoperonospora cubensis. They plan to engage a broad group of stakeholders including scientists, growers, extension specialists, the USDA APHIS Plant Protection and Quarantine personnel, the Department of Homeland Security inspectors, and diagnosticians in the National Plant Diagnostic Network in a Pandemic Preparedness workshop. Differences in response and spread of pathogens and stakeholder experiences will be examined using current methods and the aid of the new PAdb.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.