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: 9789907
• 负责人: Mauricio Santillana
• 金额: $ 36.66万
• 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/9789907
• 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; Infrastructure; Institution; Internet; Knowledge; Knowledge Discovery; Lead; Link; Literature; Machine Learning; Methodology; Methods; Modeling; Monitor; Municipalities; Population Surveillance; Process; Public Health; Readiness; Research; Socioeconomic Status; Stream; Techniques; Testing; Time; Twitter; Vaccination; Vector-transmitted infectious disease; Water; Weather; Work; Zika Virus; base; chikungunya; climate variability; computational platform; computer infrastructure; digital; disease transmission; economic determinant; experience; flu; genomic data; improved; innovation; mathematical methods; 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.

项目摘要 传染病活动的可靠和实时的传染性水平预测建模和预测， 改变公共卫生决策者设计干预措施的方式的潜力， 运动，先发制人/反应性疫苗接种和病媒控制，在整个健康威胁的存在， 世界虽然疾病活动与人类流动性、气候和 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， 流行的文献，很少有努力集中在开发一个开放源码平台的明显需要 能够利用多个数据源、因素和不同的建模方法， 和异质国家来监测和预测疾病传播，在四个地理尺度上（国家， 州、市、自治区）。本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来自多个不同研究的发现 关于地球仪的疾病动态与当地和全球因素，如天气条件，社会， 经济状况，卫星图像和在线人类行为，以开发一个可操作的，强大的，和真实的- 时间数据驱动的疾病预测平台。 该补助金的目的是利用三个互补的科学研究团队的专业知识 以及来自各种数据源的丰富信息，以构建一个能够 结合信息，在地方一级作出实时的短期疾病预报。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用于监测和预测的建模方法 疾病在多个地理尺度-国家，州，城市和自治市-使用巴西作为测试案例。 此外，我们将使用机器学习和机械模型来了解疾病动态， 多个空间尺度，在一个异质的国家，如巴西。 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）建模技术和数据流，以提高四个方面的准确性和鲁棒性 空间尺度;（3）描述建立一个可操作的 疾病预测平台;以及（4）在现实世界环境中使用我们的方法。 这一贡献是重要的，因为它将推进我们的科学知识的准确性和 不同的数据流和多种建模方法在用于预测疾病时的局限性 传输我们的努力将有助于在地方一级（城市- 等级）。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 在由世卫组织、疾病预防控制中心、学术界和地方政府组成的主题专家小组的指导下， 和巴西境内的联邦利益攸关方。 所提出的方法是创新的，因为很少有努力集中在开发一个开源的 计算平台，能够跨异构环境组合不同的数据源和驱动程序 和大国，到多种建模方法来监测和预测疾病的传播， 多个地理尺度。此外，我们建议研究如何最好地结合联合收割机建模方法 到目前为止，已经独立地发展和解释了，即传统的流行病学， 机械模型和新的机器学习预测模型，以产生准确和鲁棒的 实时疾病活动估计和预测。