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.

项目概要预测模型在疾病预防和控制中发挥着重要作用。科学的最新进展研究允许从流行病学研究中收集更彻底和深入的数据（例如，GPS 数据、气候数据、可穿戴设备数据）。然而，由于收集到的变量较多且相对部分疫情期间流行病学数据收集时间较短，信息缺失这是不可避免的，并且可能需要随后更新数据库。如何将数据与部分数据合并信息，即缺失值和随时间动态测量的预测变量，进入现有模型，以执行更准确、更高效的预测仍然是一个挑战。最近，PI 和他的团队为多种被忽视和新出现的传染病的各种目的开发了预测模型，包括血吸虫病、COVID-19 和人类季节性流感。在进行这些研究时，我们确定了阻碍更广泛实施拟议模型的几个实际问题，例如数据缺失和缺乏基于预测变量动态流入的自适应机制。现有型号采用完整的数据分析方法将显着降低统计功效并导致潜在的偏见。此外，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某个时间点的数据，而不考虑未来的数据输入。与此同时，统计和机器学习方法的发展为新的动态预测奠定了基础基于轨迹数据的模型，最近在功能并发回归和增量方面取得了进展学习。然而，这些方法论的进步并没有很好地融入到现场应用中。即使在最近的 COVID-19 研究开发了先进的动态模型，平衡了数据流预测窗口尚未得到很好的研究。此外，现有模型通常需要大量变量收集，因此一种实用的两阶段方法允许早期收集有限的数据可能会更有效具有时间和成本效益。在这个 MIRA 提案中，我们的目标是完善一些被忽视和被忽视的预测模型。新出现的传染病。具体来说，三个具有不同研究活动的连贯项目将所追求的目标包括：1）完善血吸虫病干预热点预测模型； 2）开发以及美国 COVID-19 预后风险模型的验证，以及针对缺失数据的方法开发动态预测的处理和功能回归； 3) 疫苗益处的开发和验证人类季节性流感的评分。精致的模型预计将伴随新的和更多的涉及缺失数据处理和动态预测机制的通用预测算法增强模型性能和适应性。该提案的方法论发展也将为面临类似挑战的其他流行病学研究提供信息，并产生更广泛的长期影响当前拟议项目涵盖的传染病范围。