Refining Predictive Models for Neglected and Emerging Infectious Diseases

完善被忽视和新出现的传染病的预测模型

基本信息

批准号：
10707496
负责人：
Ye Shen
金额：
$ 37.75万
依托单位：
UNIVERSITY OF GEORGIA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-21 至 2027-07-31
项目状态：
未结题

项目摘要

PROJECT SUMMARY Predictive models play an essential role in disease prevention and control. Recent advances in scientific research have allowed more thorough and in-depth data collection from epidemiological studies (e.g., GPS data, climate data, wearable device data). However, due to the many variables collected and the relatively short time frame for epidemiological data collection during some of the epidemics, missing information is unavoidable, and subsequent updates of the database may be necessary. How to incorporate data with partial information, i.e., with missingness, and predictors measured dynamically over time, into existing models to perform more accurate and efficient predictions remains a challenge. Recently, the PI and his team have developed predictive models for various purposes among several neglected and emerging infectious diseases, including schistosomiasis, COVID-19, and human seasonal influenza. While conducting these studies, we identified several practical issues prohibiting a broader implementation of the proposed models, such as missing data and a lack of adaptive mechanisms based on dynamic inflows of predictors. Existing models adopting the complete data analysis approach will significantly reduce the statistical power and cause potential bias. Moreover, predictive models applied in epidemiological infectious disease studies often rely on historical data collected up to a time point without taking into consideration of future data inputs. Meanwhile, the development in statistical and machine learning methods laid the foundation for new dynamic predictive models based on trajectory data, with recent progress in functional concurrent regression and incremental learning. However, these methodological advances have been poorly integrated into field applications. Even in recent COVID-19 research where advanced dynamic models have been developed, balancing the data flow and prediction window has not been well studied. In addition, existing models often require a large amount of variable collection, so a practical two-stage approach allowing limited data collection early on can be more time- and cost-effective. In this MIRA proposal, we aim at refining predictive models for several neglected and emerging infectious diseases. Specifically, three coherent projects with distinct research activities will be pursued, which include: 1) refining hotspot prediction models for schistosomiasis interventions; 2) development and validation of prognostic risk models for COVID-19 in the US, with methods development on missing data handling and functional regression for dynamic prediction; 3) development and validation of a vaccine benefits score for human seasonal influenza. The refined models are expected to be accompanied by new and more general predictive algorithms involving missing data processing and dynamic prediction mechanisms to enhance model performance and adaptability. The methodological development from this proposal will also inform other epidemiological studies with similar challenges and have a broader long-term impact beyond the scope of the infectious diseases covered in the currently proposed projects.

项目摘要预测模型在疾病预防和控制中起着至关重要的作用。科学的最新进展研究允许从流行病学研究中进行更深入和深入的数据收集（例如GPS 数据，气候数据，可穿戴设备数据）。但是，由于收集了许多变量，并且相对在某些流行病期间，流行病学数据收集的时间短，缺少信息是不可避免的是数据库的后续更新。如何将数据与部分合并信息，即缺失，并且随着时间的推移动态测量的信息，将现有模型衡量执行更准确和有效的预测仍然是一个挑战。最近，PI和他的团队有在几种被忽视和新兴的传染病中开发了用于各种目的的预测模型，包括血吸虫病，Covid-19和人类季节性流感。在进行这些研究时，我们确定了几个实用问题，禁止更广泛的拟议模型实施基于预测变量的动态流入，缺少数据和缺乏自适应机制。现有模型采用完整的数据分析方法将大大降低统计能力并引起潜力偏见。此外，在流行病学传染病研究中应用的预测模型通常依赖于历史数据收集到时间点，而无需考虑将来的数据输入。同时，统计和机器学习方法的开发为新的动态预测奠定了基础基于轨迹数据的模型，在功能并发回归和增量方面取得了最新进展学习。但是，这些方法论的进步已融入现场应用中很差。甚至在最近开发了高级动态模型的Covid-19研究，平衡数据流预测窗口尚未得到很好的研究。此外，现有型号通常需要大量可变收集，因此一种实用的两阶段方法，可以尽早进行有限的数据收集时间和成本效益。在此Mira建议中，我们旨在完善几个被忽视的预测模型，新兴的传染病。具体来说，将有三个具有不同研究活动的连贯项目追捕，其中包括：1）精炼血吸虫病干预措施的热点预测模型； 2）发展以及在美国的Covid-19的预后风险模型的验证，并在缺失的数据上开发方法进行动态预测的处理和功能回归； 3）开发和验证疫苗收益人类季节性流感的得分。预计精致的型号将伴随新的型号一般预测算法涉及缺失数据处理和动态预测机制增强模型性能和适应性。该提议的方法论发展也将向其他具有类似挑战的流行病学研究通知其他流行病学研究当前建议的项目涵盖的传染病范围。