Refining Predictive Models for Neglected and Emerging Infectious Diseases

项目总结 预测模型在疾病预防和控制中起着至关重要的作用。科学研究的最新进展 研究使得从流行病学研究(例如全球定位系统)收集更全面和深入的数据成为可能 数据、气候数据、可穿戴设备数据)。然而，由于收集到的变量很多，而且相对 在一些流行病期间，流行病学数据收集的时间很短，缺少的信息是 无法避免，可能需要对数据库进行后续更新。如何将数据与部分数据合并 信息，即随着时间的推移动态测量的错失率和预测值，纳入现有模型，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 为几种被忽视的和新出现的传染病开发了不同目的的预测模型， 包括血吸虫病，新冠肺炎，和人类季节性流感。在进行这些研究的同时，我们 确定了阻碍拟议模式更广泛实施的几个实际问题，例如 数据缺失，缺乏基于预测值动态流入的适应性机制。现有型号 采用完整的数据分析方法将大大降低统计能力和潜在的原因 偏见。此外，应用于传染病流行病学研究的预测模型往往依赖于历史数据。 在不考虑未来数据输入的情况下收集到某个时间点的数据。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，以及在函数并发回归和增量方面的最新进展 学习。然而，这些方法上的进步并没有很好地整合到现场应用中。即使是在 最近的新冠肺炎研究，开发了先进的动态模型，平衡数据流 而对预测窗口的研究还不够深入。此外，现有的模型通常需要大量的 变量收集，因此允许早期有限数据收集的实用两阶段方法可能更多 既省时又省钱。在这个Mira提案中，我们的目标是改进几个被忽视的和 新出现的传染病。具体地说，将有三个具有不同研究活动的连贯项目 包括：1)完善血吸虫病干预热点预测模型；2)发展 以及对美国新冠肺炎预后风险模型的验证，以及对缺失数据的方法开发 用于动态预测的处理和函数回归；3)疫苗益处的开发和验证 人类季节性流感评分。精致的车型预计将伴随着新的和更多的 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 为其他面临类似挑战的流行病学研究提供信息，并产生更广泛的长期影响 目前拟议项目所涵盖的传染病的范围。

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.