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.

项目总结/摘要 在过去的30年里，一个新的领域-被称为计算流行病学（compepi）-已经出现在 数字数据流的交叉（例如，新闻和社交媒体、搜索查询和移动数据）、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），以及公共 健康危机。由于持续的COVID-19大流行，以及其他疫苗可预防的疾病（例如， 由于疫苗的犹豫，麻疹）在美国重新出现，compepi已经将其部分 将重点作为一个领域，以改善疫苗暴发和流行期间的公共卫生决策- 可预防的疾病。在这份提案中，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compepi更广泛。虽然前三个挑战更多的是 第四个问题涉及科学界的建设：（1）估计时间- 变化的可传输性（即，特定疫苗可预防传染病的有效繁殖数，REff） （2）实时监测和测量影响疾病传播的健康行为 (e.g.,疫苗犹豫、流动性等）; (3)预测疫苗可预防的疾病暴发和流行病， 个人健康行为的功能;（4）招募新的学者到尚未封闭的compepi领域。 为了应对这些挑战，我们建议开发（1）集成的元分析工具 多个研究组的REff估计;（2）监测疫苗犹豫的监测系统， 一个推理系统，以产生更有代表性的措施，为人类的流动性;（3）一个可推广的代理- 基于流行病预测的模型，以行为参数为特征，正如前面所述 监督和推理系统;（4）一个跨机构的虚拟实验室，供compepi学者使用， 在世界各地开展疫苗可预防疾病的研究。通过解决前三个问题 挑战，我们希望帮助临床医生和公共卫生政策制定者在 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 我们的研究成果。同时，通过应对第四个挑战，我们希望帮助新的 学者--特别是那些来自代表性不足背景的学者--与 在compepi的先驱，并与对方，同时促进增长和多样化的， 随着我们的前进。

项目成果

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

• DOI: 10.1016/j.lana.2023.100533
• 发表时间: 2023-07
• 期刊: LANCET REGIONAL HEALTH-AMERICAS
• 作者: Martoma, Rosemary A.;Washam, Matthew;Martoma, Joshua C.;Cori, Anne;Majumder, Maimuna S.
• 通讯作者: Majumder, Maimuna S.

Early human judgment forecasts of human monkeypox, May 2022.

• DOI: 10.1016/s2589-7500(22)00127-3
• 发表时间: 2022-08
• 期刊: LANCET DIGITAL HEALTH
• 影响因子: 30.8
• 作者: McAndrew, Thomas;Majumder, Maimuna S.;Lover, Andrew A.;Venkatramanan, Srini;Bocchini, Paolo;Besiroglu, Tamay;Codi, Allison;Braun, David;Dempsey, Gaia;Abbott, Sam;Chevalier, Sylvain;Bosse, Nikos, I;Cambeiro, Juan
• 通讯作者: Cambeiro, Juan

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

• DOI: 10.2196/40706
• 发表时间: 2023-02-27
• 期刊: JOURNAL OF MEDICAL INTERNET RESEARCH
• 影响因子: 7.4
• 作者: Ramjee, Divya;Pollack, Catherine C.;Charpignon, Marie-Laure;Gupta, Shagun;Rivera, Jessica Malaty;El Hayek, Ghinwa;Dunn, Adam G.;Desai, Angel N.;Majumder, Maimuna S.
• 通讯作者: Majumder, Maimuna S.

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。

• DOI: 10.1093/ageing/afae018
• 发表时间: 2024
• 期刊: Age and ageing
• 影响因子: 6.7
• 作者: Charpignon,Marie-Laure;Onofrey,Shauna;Chen,Yea-Hung;Rewegan,Alex;Glymour,MedellenaMaria;Klevens,RMonina;Majumder,MaimunaShahnaz
• 通讯作者: Majumder,MaimunaShahnaz

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

• DOI: 10.1016/j.puhe.2022.08.019
• 发表时间: 2023-03
• 期刊: PUBLIC HEALTH
• 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.