Development of an Open-Source and Data-Driven Modeling Platform to Monitor and Forecast Disease Activity

开发开源和数据驱动的建模平台来监测和预测疾病活动

• 批准号: 10000112
• 负责人: Mauricio Santillana
• 金额: $ 36.56万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-21 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10000112
• 关键词: Area; Assimilations; Beds; Behavior; Brazil; Burn injury; Centers for Disease Control and Prevention (U.S.); Cities; Climate; Communicable Diseases; Communities; Complement; Country; Data; Data Set; Data Sources; Dengue; Developing Countries; Development; Disease; Disease Outbreaks; Economics; Elements; Environment; Environmental Risk Factor; Epidemic; Epidemiology; Geography; Goals; Grant; Health; Heterogeneity; High Performance Computing; Human; Imagery; Individual; Influenza; Influenza B Virus; Institution; Internet; Knowledge; Knowledge Discovery; Lead; Link; Literature; Machine Learning; Methodology; Methods; Modeling; Monitor; Municipalities; Population Surveillance; Process; Public Health; Readiness; Research; Socioeconomic Status; Techniques; Testing; Time; Twitter; Vaccination; Vector-transmitted infectious disease; Water; Weather; Work; ZIKA; base; chikungunya; climate variability; computational platform; computer infrastructure; data infrastructure; data modeling; data streams; digital; disease transmission; economic determinant; experience; flu; genomic data; heterogenous data; improved; innovation; mathematical methods; multiple data sources; novel; open data; open source; pathogen; pathogen genomics; predictive modeling; social; social media; sociodemographics; socioeconomics; spreading factor; therapy design; time use; tool; transmission process; trend; vector control; vector-borne

PROJECT SUMMARY Reliable and real-time municipality-level predictive modeling and forecasts of infectious disease activity have the potential to transform the way public health decision-makers design interventions such as information campaigns, preemptive/reactive vaccinations, and vector control, in the presence of health threats across the world. While the links between disease activity and factors such as: human mobility, climate and environmental factors, socio-economic determinants, and social media activity have long been known in the epidemic literature, few efforts have focused on the evident need of developing an open-source platform capable of leveraging multiple data sources, factors, and disparate modeling methodologies, across a large and heterogeneous nation to monitor and forecast disease transmission, over four geographic scales (nation, state, city, and municipal). The overall goal of this project is to develop such a platform. Our long-term goal is to investigate effective ways to incorporate the findings from multiple disparate studies on disease dynamics around the globe with local and global factors such as weather conditions, socio- economic status, satellite imagery and online human behavior, to develop an operational, robust, and real- time data-driven disease forecasting platform. The objective of this grant is to leverage the expertise of three complementary scientific research teams and a wealth of information from a diverse array of data sources to build a modeling platform capable of combining information to produce real-time short term disease forecasts at the local level. As part of this, we will evaluate the predictive power of disparate data streams and modeling approaches to monitor and forecast disease at multiple geographic scales--nation, state, city, and municipality--using Brazil as a test case. Additionally, we will use machine learning and mechanistic models to understand disease dynamics at multiple spatial scales, across a heterogeneous country such as Brazil. Our specific aims will (1) Assess the utility of individual data streams and modeling techniques for disease forecasting; (2) Fuse modeling techniques and data streams to improve accuracy and robustness at the four spatial scales; (3) Characterize the basic computational infrastructure necessary to build an operational disease forecasting platform; and (4) Validate our approach in a real-world setting. This contribution is significant because It will advance our scientific knowledge on the accuracy and limitations of disparate data streams and multiple modeling approaches when used to forecast disease transmission. Our efforts will help produce operational and systematic disease forecasts at a local level (city- and municipality-level). Moreover, we aim at building a new open-source computational platform for the epidemiological community to use as a knowledge discovery tool. Finally, we aim at developing this platform under the guidance of a Subject Matter Expert (SME) panel comprising of WHO, CDC, academics, and local and federal stakeholders within Brazil. The proposed approach is innovative because few efforts have focused on developing an open-source computational platform capable of combining disparate data sources and drivers, across a heterogeneous and large nation, into multiple modeling approaches to monitor and forecast disease transmission, over multiple geographic scales.. In addition, we propose to investigate how to best combine modeling approaches that have, to this date, been developed and interpreted independently, namely, traditional epidemiological mechanistic models and novel machine-learning predictive models, in order to produce accurate and robust real-time disease activity estimates and forecasts.

项目概要 可靠、实时的市级传染病活动预测模型和预报 改变公共卫生决策者设计信息等干预措施的方式的潜力 在整个地区存在健康威胁的情况下，开展运动、先发制人/反应性疫苗接种和病媒控制 世界。虽然疾病活动与人口流动、气候和气候等因素之间的联系 环境因素、社会经济决定因素和社交媒体活动早已为人们所知 流行病文献中，很少有人关注开发开源平台的明显需求 能够跨大范围利用多个数据源、因素和不同的建模方法 和异质国家来监测和预测四个地理范围内的疾病传播（国家、 州、市和市）。该项目的总体目标是开发这样一个平台。 我们的长期目标是研究有效的方法来整合多项不同研究的结果 研究全球疾病动态以及当地和全球因素，如天气条件、社会因素 经济状况、卫星图像和在线人类行为，以开发可操作的、稳健的和真实的 时间数据驱动的疾病预测平台。 这笔赠款的目的是利用三个互补的科研团队的专业知识 以及来自各种数据源的大量信息，以构建能够 结合信息在地方层面生成实时的短期疾病预测。作为其中的一部分，我们 将评估不同数据流的预测能力以及监测和预测的建模方法 以巴西作为测试案例，在多个地理范围（国家、州、城市和直辖市）进行疾病研究。 此外，我们将使用机器学习和机制模型来了解疾病动态 多个空间尺度，跨越巴西等异构国家。 我们的具体目标是 (1) 评估单个数据流和疾病建模技术的效用 预测; (2) 融合建模技术和数据流，提高四个方面的准确性和鲁棒性 空间尺度； (3) 描述构建可操作的计算基础设施所需的基本计算基础设施 疾病预测平台； (4) 在现实环境中验证我们的方法。 这一贡献意义重大，因为它将提高我们关于准确性和准确性的科学知识。 用于预测疾病时不同数据流和多种建模方法的局限性 传播。我们的努力将有助于在地方层面（城市- 和市级）。此外，我们的目标是建立一个新的开源计算平台 流行病学社区用作知识发现工具。最后，我们的目标是开发这个平台 在主题专家 (SME) 小组的指导下，该小组由 WHO、CDC、学术界和当地人士组成 以及巴西境内的联邦利益相关者。 所提出的方法是创新的，因为很少有人致力于开发开源软件 能够跨异构组合不同数据源和驱动程序的计算平台 和大国，采用多种建模方法来监测和预测疾病传播， 多个地理尺度。此外，我们建议研究如何最好地结合建模方法 迄今为止，已经独立发展和解释，即传统的流行病学 机械模型和新颖的机器学习预测模型，以便产生准确且稳健的结果 实时疾病活动估计和预测。