SBIR Phase I: IM3UNE: A Platform for Integrated Monitoring, Mapping, Modeling and Understanding of Novel Epidemics Like COVID-19

SBIR 第一阶段：IM3UNE：一个用于集成监测、绘图、建模和理解新型流行病（如 COVID-19）的平台

• 批准号: 2029153
• 负责人: Ashlee Valente
• 金额: $ 25.56万
• 依托单位: GEOMETRIC DATA ANALYTICS
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029153&HistoricalAwards=false
• 关键词: SBIR; Phase; IM3UNE; Platform; Integrated

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to assist with preparedness and decision-making for situations like the COVID-19 pandemic. This health crisis has been exacerbated by a lack of real-time information or predictive information about the extent, location, and spread of the disease. This Phase I project ingests data streams from government agencies, healthcare providers, and the general public, returning real-time, actionable information to assist in guiding a broad and coordinated response. While this platform is particularly relevant in the COVID-19 pandemic, it is disease agnostic, and will have utility for seasonal influenza and other infectious diseases, positively impacting public health.This Small Business Innovation Research (SBIR) Phase I project will create a platform aimed at providing real-time identification of infectious disease outbreaks and predictions of disease spread, with an initial focus on COVID-19. Among the gaps in the response to the COVID-19 pandemic is capability to accurately track the magnitude, location, and spread of infectious agents. This project aims to assess and demonstrate the value of leveraging multiple modalities and sources of data for early detection and prediction of disease outbreaks, with focus on COVID-19. The approach will combine data fusion methods and epidemiological modeling approaches with continual input from subject matter experts in an effort to generate actionable information and predictive models related to disease spread. A variety of data streams providing information on disease incidence, transportation data, and sub-population interaction data will be used to construct progressively more sophisticated SEIR models. These efforts will result in an improved understanding of the utility and applicability of various data streams to epidemiological monitoring and forecasting, as well as platform for users of various levels of technical expertise.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.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力是协助准备和决策，如COVID-19大流行的情况。由于缺乏有关疾病范围、位置和传播的实时信息或预测信息，这一健康危机加剧了。第一阶段项目从政府机构、医疗保健提供者和公众那里获取数据流，返回实时、可操作的信息，以帮助指导广泛和协调的应对措施。虽然该平台在COVID-19大流行中特别相关，但它是疾病不可知的，并将对季节性流感和其他传染病产生效用，对公共卫生产生积极影响。这个小企业创新研究（SBIR）第一阶段项目将创建一个平台，旨在提供传染病爆发的实时识别和疾病传播的预测，最初的重点是COVID-19。应对COVID-19大流行的差距之一是准确跟踪传染性病原体的规模、位置和传播的能力。该项目旨在评估和展示利用多种模式和数据来源早期发现和预测疾病爆发的价值，重点是COVID-19。该方法将把联合收割机数据融合方法和流行病学建模方法与主题专家的持续投入相结合，以努力生成与疾病传播有关的可操作信息和预测模型。提供疾病发病率、运输数据和亚群体相互作用数据信息的各种数据流将用于构建逐步更复杂的SEIR模型。这些努力将使人们更好地了解各种数据流对流行病学监测和预测的效用和适用性，以及为各种技术专业水平的用户提供平台。该奖项反映了NSF的法定使命，并被认为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来支持。