Wastewater-based effective reproduction number and prediction under the absence of shedding information

IF 12.7 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Hiroki Andoa, Kelly A. Reynolds
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引用次数: 0

Abstract

Estimating effective reproduction number (Re) and predicting disease incidences are essential to formulate effective strategies for disease control. Although recent studies developed models for inferring Re from wastewater-based data, they require information on shedding dynamics. Here, we proposed a framework of Re estimation and prediction without shedding information. The framework consists of a space-state model for smoothing wastewater-based data and a renewal equation modified for wastewater-based data. The applicability of the framework was tested with simulated data and real-world data on Influenza A virus (IAV) and SARS-CoV-2 concentration in 2022/2023 season in the USA. We confirmed the state-space model effectively fits various simulated epidemic curves and accurately fits real-world data. In simulations, we found wastewater-based Re (Reww) closely aligns with instantaneous clinical Re when shedding dynamics are rapid. For more prolonged shedding, Reww approximates a smoothed Re over time. We also observed the necessary sampling frequency to trace dynamics of wastewater concentration and Reww accurately in the framework varies depending on the precision of detection methods, the epidemic status, the transmissibility of infectious diseases, and shedding dynamics. By applying our framework to real-world data, we found Reww for SARS-CoV-2 showed similar trend and values to clinically-based Re. Reww for IAV ranged from 0.66 to 1.52 with a clear peak in the winter season, which agrees with previously reported Re. We also succeeded in predicting wastewater concentration in a few weeks from available wastewater-based data. These results indicate that our framework potentially enables real-time approximations of Re and prediction of infectious disease dynamics through wastewater surveillance, which limits the delay between infection and reporting. Our framework is useful especially for regions where reliable clinical surveillance is not available and notifiable surveillance is abolished, and can be expanded to multiple infectious diseases that have been detected from wastewater without shedding information.

Abstract Image

基于废水的有效繁殖数量和在无脱落信息情况下的预测
估算有效繁殖数(Re)和预测疾病发病率对于制定有效的疾病控制策略至关重要。尽管最近的研究开发了从废水数据中推断 Re 的模型,但它们需要脱落动态信息。在此,我们提出了一个无需脱落信息的 Re 估计和预测框架。该框架由用于平滑废水数据的空间状态模型和针对废水数据修改的更新方程组成。该框架的适用性通过模拟数据和美国 2022/2023 年甲型流感病毒(IAV)和 SARS-CoV-2 浓度的实际数据进行了测试。我们证实,状态空间模型有效地拟合了各种模拟流行病曲线,并准确地拟合了真实世界的数据。在模拟中,我们发现当脱落动态较快时,基于废水的 Re(Reww)与瞬时临床 Re 非常接近。在脱落时间较长的情况下,Reww 近似于随时间变化的平滑 Re。我们还观察到,在框架中准确追踪废水浓度和 Reww 动态所需的采样频率因检测方法的精度、流行状况、传染病的传播性和脱落动态而异。通过将我们的框架应用于真实世界的数据,我们发现 SARS-CoV-2 的 Reww 显示出与基于临床的 Re 相似的趋势和数值。IAV 的 Reww 值从 0.66 到 1.52 不等,在冬季有一个明显的峰值,这与之前报告的 Re 值一致。我们还利用现有的废水数据在几周内成功预测了废水浓度。这些结果表明,我们的框架有可能通过废水监测实时逼近 Re 值和预测传染病动态,从而限制感染与报告之间的延迟。我们的框架尤其适用于没有可靠的临床监测和取消了应申报监测的地区,并可扩展到从废水中检测到的多种传染病,而无需脱落信息。
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来源期刊
ACS Central Science
ACS Central Science Chemical Engineering-General Chemical Engineering
CiteScore
25.50
自引率
0.50%
发文量
194
审稿时长
10 weeks
期刊介绍: ACS Central Science publishes significant primary reports on research in chemistry and allied fields where chemical approaches are pivotal. As the first fully open-access journal by the American Chemical Society, it covers compelling and important contributions to the broad chemistry and scientific community. "Central science," a term popularized nearly 40 years ago, emphasizes chemistry's central role in connecting physical and life sciences, and fundamental sciences with applied disciplines like medicine and engineering. The journal focuses on exceptional quality articles, addressing advances in fundamental chemistry and interdisciplinary research.
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