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

小企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力是协助应对新冠肺炎疫情等情况的准备和决策。由于缺乏关于疾病范围、位置和传播的实时信息或预测性信息，这一健康危机加剧了。此第一阶段项目接收来自政府机构、医疗保健提供者和普通公众的数据流，返回实时、可操作的信息，以帮助指导广泛和协调的响应。虽然该平台与新冠肺炎疫情特别相关，但它与疾病无关，将对季节性流感和其他传染病产生效用，对公众健康产生积极影响。这个小企业创新研究第一阶段项目将创建一个旨在提供实时识别传染病爆发和疾病传播预测的平台，最初重点放在新冠肺炎上。在应对新冠肺炎大流行方面的差距之一是能否准确跟踪传染病病原体的规模、位置和传播。该项目旨在评估和展示利用多种模式和数据来源及早发现和预测疾病暴发的价值，重点是新冠肺炎。该方法将把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结合起来，努力生成与疾病传播有关的可操作信息和预测模型。将使用各种数据流提供有关疾病发病率、运输数据和亚人群相互作用数据的信息，以构建逐步更加复杂的SEIR模型。这些努力将导致更好地理解各种数据流对流行病学监测和预测的效用和适用性，以及为不同水平的技术专业用户提供的平台。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.