RAPID: Development of an Interactive Web-based Dashboard to Track COVID-19 in Real-time

RAPID：开发基于网络的交互式仪表板来实时跟踪 COVID-19

• 批准号: 2028604
• 负责人: Lauren Gardner
• 金额: $ 20万
• 依托单位: Johns Hopkins University
• 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: 2020-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028604&HistoricalAwards=false
• 关键词: RAPID; Development; Interactive; Web; based

pThis Rapid Response Research (RAPID) grant will be used to support the management and development of the COVID-19 online interactive dashboard hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. The dashboard was first released publicly on January 22, 2020 to visualize and track the COVID-19 outbreak in real-time, and has since served as the prominent centralized source of COVID-19 epidemiological data throughout the COVID-19 outbreak to date. The dashboard illustrates the location and number of confirmed cases, deaths and recoveries for all affected countries, with additional features added over time. Further, all the data collected and displayed is made freely available to researchers, public health authorities and the general public in a GitHub repository, along with the feature layers of the dashboard. The availability of this data at the initial stages of the outbreak enabled the public health and research community to implement modeling tools, specifically the calibration of parameters for estimating transmission and spread at the earliest stages of the outbreak, when more formal situation reports were lagging, and thus gain a better understanding of the disease characteristics early on, which allowed for evidenced based decision making. Further, members of the public health community, including national and local level governmental organizations and public health offices around the world, continue to rely on the dashboard for informing, planning, and decision making regarding public safety, such as clinical staffing and resource allocation./ppGiven the popularity and impact of the dashboard to date, there is a need to continue to maintain it, and further build out its capabilities. Specifically, this includes the collection and curation process to capture data from a broader set of sources, enabling an increase in the spatial resolution (ideally to the city level) of the reporting. Introduction of additional features, including health-related context and confidence measures for the data and information presented, will increase the dashboard's utility in supporting awareness and decision-making. Additional model and analytic research and development will enhance detection and classification, and more broadly, relative risk determination of events of concern from all sourced health related data. Longer term, systems engineering activities associated with this development will ensure an enduring capability established, vetted, and well-used prior to the next potential pandemic. Thus, the research efforts are critical to provide an early-warning system for future public health events, and help inform control measures in the current and future outbreaks./ppThis 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./p

p这笔快速响应研究 (RAPID) 拨款将用于支持约翰·霍普金斯大学系统科学与工程中心 (CSSE) 托管的 COVID-19 在线交互式仪表板的管理和开发。该仪表板于 2020 年 1 月 22 日首次公开发布，用于实时可视化和跟踪 COVID-19 疫情，此后一直是迄今为止整个 COVID-19 疫情期间 COVID-19 流行病学数据的重要集中来源。该仪表板显示了所有受影响国家的确诊病例、死亡和康复的位置和数量，并随着时间的推移添加了其他功能。此外，收集和显示的所有数据以及仪表板的功能层都可以在 GitHub 存储库中免费提供给研究人员、公共卫生当局和公众。在疫情爆发的初始阶段获得这些数据使公共卫生和研究界能够实施建模工具，特别是在疫情爆发的最早阶段（当更正式的情况报告滞后时）对用于估计传播和传播的参数进行校准，从而尽早更好地了解疾病特征，从而做出基于证据的决策。此外，公共卫生界的成员，包括世界各地的国家和地方级政府组织和公共卫生办公室，继续依靠仪表板来提供有关公共安全的信息、规划和决策，例如临床人员配置和资源分配。 /pp鉴于仪表板迄今为止的受欢迎程度和影响力，有必要继续维护它，并进一步增强其功能。具体来说，这包括从更广泛的来源捕获数据的收集和管理过程，从而提高报告的空间分辨率（最好是城市级别）。 引入其他功能，包括与健康相关的背景以及对所呈现的数据和信息的置信度测量，将增加仪表板在支持意识和决策方面的实用性。 额外的模型和分析研究和开发将增强检测和分类，更广泛地说，增强从所有来源的健康相关数据中确定关注事件的相对风险。从长远来看，与这一发展相关的系统工程活动将确保在下一次潜在的大流行之前建立、审查和充分利用持久的能力。因此，研究工作对于为未来的公共卫生事件提供预警系统至关重要，并有助于为当前和未来的疫情爆发提供控制措施。/pp该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。/p

项目成果

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

美国 COVID-19 死亡率的个体和整体概率预测评估

• DOI: 10.1073/pnas.2113561119
• 发表时间: 2022-04
• 期刊: Proceedings of the National Academy of Sciences
• 作者: Cramer, Estee Y.;Ray, Evan L.;Lopez, Velma K.;Bracher, Johannes;Brennen, Andrea;Castro Rivadeneira, Alvaro J.;Gerding, Aaron;Gneiting, Tilmann;House, Katie H.;Huang, Yuxin;et al
• 通讯作者: et al

An evaluation of prospective COVID-19 modelling studies in the USA: from data to science translation

对美国前瞻性 COVID-19 建模研究的评估：从数据到科学转化

• DOI: 10.1016/s2589-7500(22)00148-0
• 发表时间: 2022-09-20
• 期刊: The Lancet. Digital Health
• 作者: Kristen Nixon;S. Jindal;F. Parker;N. Reich;K. Ghobadi;Elizabeth C. Lee;S. Truelove;L. Gardner
• 通讯作者: L. Gardner

Real-time COVID-19 forecasting: challenges and opportunities of model performance and translation

实时 COVID-19 预测：模型性能和转换的挑战和机遇

• DOI: 10.1016/s2589-7500(22)00167-4
• 发表时间: 2022-10
• 期刊: The Lancet Digital Health
• 作者: Nixon, Kristen;Jindal, Sonia;Parker, Feli;Marshall, Maximilian;Reich, Nicholas G;Ghobadi, Kimia;Lee, Elizabeth C;Truelove, Shaun;Gardner, Lauren
• 通讯作者: Gardner, Lauren