The utility and interpretation of SARS-CoV-2 wastewater monitoring data as a predictor of community health can be confounded by variables such as wastewater system complexity, viral variants, and human behavior, including vaccination status and use of at-home tests. Here, we explored the relationship between the COVID caseload and SARS-CoV-2 concentration in wastewater for 23 locations over a 22-month period as the pandemic evolved. Spearman’s rank analysis showed strong correlations (ρ > 0.7) for most facilities, independent of normalization. While correlations remained strong throughout the pandemic, application of change point analysis (CPA) identified shifts in the relationship between reported clinical cases and the wastewater signal over time. These shifts did not generally coincide with known pandemic milestones, suggesting the involvement of multiple interacting or unknown variables. Models accounting for these shifts in the pandemic phase showed significantly improved predictions of reported caseloads. Additionally, the existence of change points highlights the increased reliability of wastewater data over clinical data when changes in the ratio of cases to wastewater concentrations are due to changes in human immunity and behaviors. In future pandemics, public health professionals will ideally be aware that the case-to-copy ratio can change unpredictably as pandemics evolve, and CPA can support public health decision-making.

中文翻译：

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对不断演变的流行病的看法：临床病例与科罗拉多州废水中 SARS-CoV-2 RNA 水平之间关系的变化

SARS-CoV-2废水监测数据作为社区健康预测指标的效用和解释可能会受到废水系统复杂性、病毒变异和人类行为（包括疫苗接种状态和家庭测试使用情况）等变量的影响。在这里，我们探讨了随着大流行的发展，22 个月内 23 个地点的新冠病例数与废水中 SARS-CoV-2 浓度之间的关系。 Spearman 的等级分析显示，大多数设施都具有很强的相关性 (ρ > 0.7)，与标准化无关。虽然在整个大流行期间相关性仍然很强，但变化点分析 (CPA) 的应用发现了报告的临床病例与废水信号之间关系随时间的变化。这些变化通常与已知的大流行里程碑并不相符，这表明涉及多个相互作用或未知的变量。解释大流行阶段这些变化的模型显示，对报告病例数的预测得到了显着改善。此外，当病例与废水浓度之比的变化是由于人类免疫力和行为的变化引起时，变化点的存在凸显了废水数据相对于临床数据的可靠性的提高。在未来的大流行中，公共卫生专业人员理想地会意识到，随着大流行的发展，病例与副本的比率可能会发生不可预测的变化，而 CPA 可以支持公共卫生决策。