RAPID: Machine Learning Methods to Understand, Predict and Reduce the Spread of COVID-19 in Small Communities

RAPID：理解、预测和减少 COVID-19 在小社区中传播的机器学习方法

• 批准号: 2031548
• 负责人: Gil Gallegos
• 金额: $ 18.57万
• 依托单位: New Mexico Highlands 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: 2020-05-15 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031548&HistoricalAwards=false
• 关键词: RAPID; Machine; Learning; Methods; Understand

The ongoing COVID-19 outbreak has recently reached pandemic status spreading all around the world. The severity of the pandemic, along with an enormous impact on world’s economy and society, has forced governments to introduce emergency measures. It is essential to utilize the available statistical data from trusted sources in order to model and evaluate the dynamics of the pandemic spread, to not only better understand such complex systems, but to learn and develop possible solutions to prevent further spread of current and/or similar future outbreaks. Thus, this research, devoted to the development of mathematical models of COVID-19 pandemic spread, addresses an urgent national need. Faculty and students in computer science, anthropology, and computational chemistry at New Mexico Highlands University have formed a diverse group for finding a solution to the complicated problems of the description and prediction of COVID-19 spread. This multidisciplinary project is expected to yield a better understanding of the interconnections among many factors that contribute to the spread of COVID-19. Statistical data will be collected in regions of Northern New Mexico, including San Juan and McKinley Counties in the Navajo Nation and Los Alamos county outside of the Navajo Nation. Analysis of the collected statistical data along with socio-cultural assessment from this project will be presented to New Mexico (NM) tribal and health authorities. The project will aim to provide a scientific basis for the prediction of disease spread and will consider scenarios associated with the possibility of another wave of the pandemic. Students from this minority-serving institution involved in the project will obtain valuable experience in the application of advanced machine learning models and methods in providing fast robust reaction to a national health, economic, and societal crisis.In this study, machine learning methods will be used to analyze pandemic spread scenarios in different regions and to glean the most important features of the data characterizing the spread. The research team will use both traditional machine learning techniques and advanced methods, such as artificial neural networks, allowing development of virus incidence model capturing dependencies in both linear and nonlinear domains. The work will concentrate on understanding disease spread with regard to multiple socioeconomic factors. The problem can be treated as a sequence modeling one; so, recurrent neural networks and more complex models based on their recurrent cells might be one promising direction. The next step will be to assemble datasets for small isolated communities with different socioeconomic backgrounds and ethnicities – comparing Navajo Indians living on the Navajo reservation to Los Alamos County (NM) – and to test the applicability of the developed model to these regions. The spatiotemporal data available on the spread is heterogeneous in character. An important goal of this research is to classify the collected data with respect to the similarity in the epidemic curve behavior and then build separate models for different regions according to this classification. The proposed model will be used for prediction of future incidents and to produce the most effective non-medical recommendations for suppression and prevention of future viral outbreaks.This research is supported by the Partnerships for Research and Education in Materials (PREM) program and the Condensed Matter and Materials Theory (CMMT) program in the Division of Materials Research in the Directorate for Mathematical and Physical Science using supplemental funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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.

正在进行的新冠肺炎疫情最近已达到大流行状态，正在向世界各地蔓延。疫情的严重性，以及对世界经济和社会的巨大影响，迫使各国政府采取紧急措施。必须利用来自可信来源的现有统计数据，以模拟和评估大流行传播的动态，不仅要更好地了解这种复杂的系统，而且要学习和制定可能的解决办法，以防止当前和/或未来类似疫情的进一步传播。因此，这项致力于开发新冠肺炎大流行传播数学模型的研究满足了国家的迫切需求。新墨西哥州高地大学计算机科学、人类学和计算化学专业的师生组成了一个多元化的小组，致力于寻找新冠肺炎传播描述和预测这一复杂问题的解决方案。预计这一跨学科项目将更好地理解推动新冠肺炎传播的许多因素之间的相互联系。统计数据将在新墨西哥州北部地区收集，包括纳瓦霍族的圣胡安县和麦金利县，以及纳瓦霍族以外的洛斯阿拉莫斯县。对收集的统计数据的分析以及该项目的社会文化评估将提交给新墨西哥州部落和卫生当局。该项目旨在为疾病传播的预测提供科学依据，并将考虑与另一波大流行的可能性有关的情景。来自这所为少数群体服务的机构的学生将在应用先进的机器学习模型和方法方面获得宝贵的经验，以快速有力地应对国家健康、经济和社会危机。在这项研究中，机器学习方法将用于分析不同地区的流行病传播情景，并收集表征传播的数据的最重要特征。研究小组将使用传统的机器学习技术和先进的方法，如人工神经网络，允许开发病毒感染模型，捕捉线性和非线性领域的相关性。这项工作将集中于了解与多种社会经济因素有关的疾病传播。这个问题可以看作是一个序列建模问题，因此，递归神经网络和基于其递归神经元的更复杂的模型可能是一个很有前途的方向。下一步将是收集具有不同社会经济背景和种族的小型孤立社区的数据集-将居住在纳瓦霍保留地的纳瓦霍印第安人与洛斯阿拉莫斯县(新墨西哥州)进行比较-并测试开发的模型对这些地区的适用性。传播上可用的时空数据在性质上是不同的。本研究的一个重要目的是根据疫情曲线行为的相似性对收集到的数据进行分类，然后根据这种分类为不同的地区建立单独的模型。建议的模型将用于预测未来的事件，并为抑制和预防未来的病毒爆发产生最有效的非医学建议。这项研究由数学和物理科学局材料研究部的材料研究和教育伙伴关系(PREM)计划和凝聚态物质和材料理论(CMMT)计划支持，使用冠状病毒援助、救济和经济安全(CARE)法案提供的补充资金。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。