Digital data streams and machine learning for real-time modeling of vaccine-preventable infectious diseases

用于疫苗可预防传染病实时建模的数字数据流和机器学习

• 批准号: 10686942
• 负责人: Maimuna Shahnaz Majumder
• 金额: $ 44.23万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10686942
• 关键词: Address; Behavior; Behavioral; COVID-19 pandemic; Collaborations; Communicable Diseases; Data; Decision Making; Development; Disease; Disease Outbreaks; Epidemic; Epidemiology; Growth; Health behavior; Human; Individual; Institution; Machine Learning; Measles; Measurement; Measures; Modeling; Monitor; Natural Language Processing; Nature; Policy Maker; Public Health; Reproduction; Research; Research Personnel; System; Time; United States; Vaccines; analytical tool; community building; data streams; digital; improved; news; real time model; real time monitoring; recruit; social media; vaccine hesitancy; virtual laboratory

PROJECT SUMMARY/ABSTRACT Over the last 30 years, a new field––known as computational epidemiology (comp epi)––has emerged at the intersection of digital data streams (e.g., news and social media, search query, and mobility data), machine learning (e.g., nonlinear optimization, natural language processing, and agent-based modeling), and public health crises. Due to the ongoing COVID-19 pandemic, as well as other vaccine-preventable diseases (e.g., measles) that have re-emerged in the United States due to vaccine hesitancy, comp epi has shifted part of its focus as a field to improving public health decision-making during outbreaks and epidemics of vaccine- preventable disease. In this proposal, we present four foundational challenges within the context of vaccine- preventable disease research and comp epi more broadly. While the first three of these challenges are more conventionally scientific in nature, the fourth involves scientific community-building: (1) estimating the time- varying transmissibility (i.e., the effective reproduction number, REff) of a given vaccine-preventable infectious disease; (2) real-time monitoring and measurement of health behaviors that impact disease transmissibility (e.g., vaccine hesitancy, mobility, etc.); (3) forecasting of vaccine-preventable outbreaks and epidemics as a function of individual health behaviors; and (4) recruitment of new scholars to the yet-insular field of comp epi. To address these challenges, we propose the development of (1) a meta-analytical tool for ensemble estimation of REff across multiple research groups; (2) a surveillance system to monitor vaccine hesitancy and an inference system to produce more representative measures for human mobility; (3) a generalizable agent- based model for epidemic forecasting that features behavioral parameters, as informed by the aforementioned surveillance and inference systems; and (4) a cross-institutional virtual laboratory for comp epi scholars to collaborate on vaccine-preventable disease research all around the world. By addressing the first three challenges, we hope to help clinicians and public health policymakers make data-informed decisions during vaccine-preventable crises while simultaneously providing opportunities for other public health researchers to augment their own efforts in transmissibility estimation and epidemic forecasting by harnessing expected products from our proposed research. Meanwhile, by addressing the fourth challenge, we hope to help new scholars–-particularly those from under-represented backgrounds––form meaningful collaborations both with pioneers in comp epi and with each other, while simultaneously promoting growth and diversification of the field as we move forward.

项目概要/摘要 在过去的 30 年里，一个新的领域——被称为计算流行病学 (comp epi)——在医学界出现。 数字数据流（例如新闻和社交媒体、搜索查询和移动数据）的交叉点、机器 学习（例如非线性优化、自然语言处理和基于代理的建模）和公共 由于持续的 COVID-19 大流行以及其他疫苗可预防的疾病（例如， 由于对疫苗犹豫不决而在美国重新出现的麻疹病毒，comp epi 已转移其部分 重点作为在疫苗爆发和流行期间改善公共卫生决策的领域 在本提案中，我们提出了疫苗背景下的四个基本挑战： 可预防疾病的研究和更广泛的比较，而前三个挑战则更为广泛。 本质上是传统科学，第四个涉及科学社区建设：（1）估计时间- 给定疫苗可预防的传染病的不同传播率（即有效繁殖数 REff） （2）实时监测和测量影响疾病传播能力的健康行为 （例如，疫苗犹豫、流动性等）；（3）预测疫苗可预防的疫情和流行病 个人健康行为的功能；(4) 招募新学者进入比较孤立的领域。 为了应对这些挑战，我们建议开发（1）集成分析工具 对多个研究小组的 REff 进行估计；(2) 监测疫苗犹豫的监测系统； 一个推理系统，用于产生更具代表性的人类流动性度量；（3）一个可推广的代理—— 基于行为参数的流行病预测模型，如上述所述 监视和推理系统；(4) 供比较外延学者使用的跨机构虚拟实验室 通过解决前三个问题，在世界各地合作开展疫苗可预防疾病的研究。 挑战，我们希望帮助前沿和公共卫生决策者在疫情期间做出基于数据的决策 疫苗可预防的危机，同时为其他公共卫生研究人员提供机会 通过利用预期的信息来加强自己在传播性估计和流行病预测方面的努力 同时，我们希望通过解决第四个挑战来帮助新的产品。 学者们——尤其是那些来自代表性不足背景的学者——与以下组织建立了有意义的合作： 比较外延领域的先驱以及彼此之间的合作，同时促进该领域的增长和多元化 当我们前进时的领域。

项目成果

US COVID-19 clinical trial leadership gender disparities.

美国 COVID-19 临床试验领导性别差异。

• 发表时间: 2023-03
• 作者: Sehgal, Neil K R;Brownstein, John S;Majumder, Maimuna S;Tuli, Gaurav
• 通讯作者: Tuli, Gaurav

Association between social vulnerability and place of death during the first 2 years of COVID-19 in Massachusetts.

马萨诸塞州 COVID-19 爆发前两年的社会脆弱性与死亡地点之间的关联。

• 发表时间: 2024-02-01
• 影响因子: 6.7
• 作者: Charpignon, Marie;Onofrey, Shauna;Chen, Yea;Rewegan, Ale;Glymour, Medellena Maria;Klevens, R Monina;Majumder, Maimuna Shahnaz
• 通讯作者: Majumder, Maimuna Shahnaz

Evolving Face Mask Guidance During a Pandemic and Potential Harm to Public Perception: Infodemiology Study of Sentiment and Emotion on Twitter.

大流行期间不断变化的口罩指导以及对公众认知的潜在危害：推特上情绪和情绪的信息流行病学研究。

• 发表时间: 2023-02-27
• 影响因子: 7.4
• 作者: Ramjee, Divya;Pollack, Catherine C;Charpignon, Marie;Gupta, Shagun;Rivera, Jessica Malaty;El Hayek, Ghinwa;Dunn, Adam G;Desai, Angel N;Majumder, Maimuna S
• 通讯作者: Majumder, Maimuna S

The impact of state paid sick leave policies on weekday workplace mobility during the COVID-19 pandemic.

COVID-19 大流行期间国家带薪病假政策对工作日工作场所流动性的影响。

• 发表时间: 2023-02
• 影响因子: 5.2
• 作者: Pollack, C C;Deverakonda, A;Hassan, F;Haque, S;Desai, A N;Majumder, M S
• 通讯作者: Majumder, M S

Modeling vaccination coverage during the 2022 central Ohio measles outbreak: a cross-sectional study.

对 2022 年俄亥俄州中部麻疹爆发期间的疫苗接种覆盖率进行建模：一项横断面研究。

• 发表时间: 2023-07
• 作者: Martoma, Rosemary A;Washam, Matthew;Martoma, Joshua C;Cori, Anne;Majumder, Maimuna S
• 通讯作者: Majumder, Maimuna S