EpiFlow：用于提升污水信号在疾病预测中效用的框架
EpiFlow: A framework for improving the utility of wastewater signals for disease forecasting
- Biocomplexity Institute, University of Virginia（弗吉尼亚大学生物复杂性研究所）
- Virginia Department of Health（弗吉尼亚州卫生部门）
- US Department of Defense, Pentagon（美国国防部五角大楼）
- Old Dominion University（奥多明尼昂大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
该研究提出EpiFlow框架，通过处理污水数据、分析其与疾病负担指标的关系，结合随时间变化的预测模型，在预测弗吉尼亚州COVID-19住院人数时，提升了预测准确性与覆盖率。
中文摘要 AI 辅助
基于污水的监测是一种有效的疾病监测工具，可提供疫情暴发的早期预警。尽管污水病毒载量（WVL）与疾病负担相关，但其在改进实时预测方面的效用仍在研究中。在疫情早期阶段，许多指标可有效监测疾病传播，但由于报告疲劳和流行率较低，其可靠性可能下降。即使在低流行率时期，医院负担也可能大幅变化，因此准确预测负担指标对于最小化疾病影响至关重要。本文中，我们提出了处理污水数据、表征其与负担指标关系并生成实时预测的原则性方法。我们使用熵度量评估WVL的可预测性，通过捕捉时间动态和WVL领先指标行为的因果检验分析WVL与负担指标的关系。我们将这些见解整合到随时间变化的预测模型中，该模型考虑了信号间不断演变的关系，还通过模拟评估了WVL报告延迟的影响。我们通过预测弗吉尼亚州及其卫生区在不同疾病流行率时期的COVID-19住院人数来测试方法的效用。与基线模型相比，整合WVL可提高预测准确性，尤其在疫情关键阶段，预测覆盖率提升了20个百分点。我们的结果表明，即使在低流行率或报告延迟的情况下，WVL信号也可改进传染病预测。
英文摘要
Wastewater-based surveillance is an effective tool for disease monitoring and can provide early warning of outbreaks. Although wastewater viral loads (WVL) correlate with disease burden, their utility for improving real-time forecasting remains under investigation. During the early phases of an epidemic, many indicators can effectively monitor disease spread, but their reliability may decline because of reporting fatigue and low prevalence. Hospital burden can vary substantially even during low-prevalence periods, making accurate forecasting of burden indicators essential for minimizing disease impacts. In this paper, we present principled approaches for processing wastewater data, characterizing its relationship with burden indicators, and generating real-time forecasts. We assess the predictability of WVL using entropy measures. We analyze the relationship between WVL and burden indicators using causality tests that capture temporal dynamics and the leading-indicator behavior of WVL. We incorporate these insights into a time-varying forecasting model that accounts for the evolving relationship between the signals. We also evaluate the effects of delays in WVL reporting through simulations. We test the utility of our methods by forecasting COVID-19 hospital admissions across Virginia and its health regions during periods of varying disease prevalence. Incorporating WVL improves forecast accuracy relative to baseline models, particularly during critical epidemic phases, and results in a 20 percentage point improvement in forecast coverage. Our results demonstrate that WVL signals can improve infectious disease forecasting even under conditions of low prevalence or delayed reporting.
