Wastewater-Based Estimation of the Effective Reproductive Number of SARS-CoV-2

Huisman, Jana S.; Scire, Jérémie; Caduff, Lea; Fernández-Cassi, Xavier; Ganesanandamoorthy, Pravin; Kull, Anina; Scheidegger, Andreas; Stachler, Elyse; Boehm, Alexandria B.; Hughes, Bridgette; Knudson, Alisha; Topol, Aaron; Wigginton, Krista; Wolfe, Marlene K.; Kohn, Tamar; Ort, Christoph; Stadler, Tanja; Julian, Timothy R.

doi:10.1289/ehp10050

Cited by 115 publications

(132 citation statements)

References 65 publications

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“…More generally, the method is applicable to any other type of incidence data, such as admissions to intensive care units or excess death data. We have also extended this method to make use of daily measurements of SARS-CoV-2 viral concentrations in wastewater [41]. The potential limitations of our R e estimation method are discussed in detail in the Appendix A.1.2.…”

Section: Discussionmentioning

confidence: 99%

Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2

Huisman

Scire

Angst

et al. 2020

Preprint

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The effective reproductive number Re is a key indicator of the growth of an epidemic. Since the SARS-CoV-2 pandemic started, many methods and online dashboards have sprung up to monitor this number. However, these methods are not always thoroughly tested or are applied only to a limited geographic range. Here, we present a method for near real time monitoring of Re, applied to epidemic data from 170 countries. We thoroughly validate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that in the majority of countries the estimated Re dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe, Asia, and North America we found that the implementation of non-pharmaceutical interventions was associated with reductions in the effective reproductive number. Globally, we found that relaxing non-pharmaceutical interventions did not fully revert Re values to their original levels. Generally, our framework is useful both to inform governments and the general public on the status of the epidemic in their country, as well as a source for detailed comparison between countries and in relation to local public health policies and external covariates such as mobility, behavioural, or weather data.Significance statementDuring the SARS-CoV-2 pandemic, governments need a way to monitor the epidemiological situation in their country. A key indicator is the effective reproductive number Re. It describes the average number of secondary infections caused by a primary infected individual. Here, we present a method to estimate Re from case report data. We thoroughly validate the method on simulated data, and present Re estimates for 170 countries on an interactive web interface. We then use this method to investigate the impact of non-pharmaceutical interventions on reducing Re worldwide. We find that the estimated Re was significantly above 1 prior to the introduction of major non-pharmaceutical interventions, and that relaxing these interventions does not fully revert Re estimates to their prior levels.

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Section: Discussionmentioning

confidence: 99%

Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2

Huisman

Scire

Angst

et al. 2020

Preprint

Self Cite

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“…( 5). They offer a downsampled proxy of incidence, which is delayed by a shedding load distribution defining the lag before a case is detected in wastewater [49]. Consequently, our metrics are widely applicable.…”

Section: Comparing Different Epidemic Data Streamsmentioning

confidence: 99%

Quantifying the information in noisy epidemic curves

Parag

Donnelly

Zarebski

2022

Preprint

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Reliably estimating the dynamics of transmissible diseases from noisy surveillance data is an enduring problem in modern epidemiology. Key parameters, such as the time-varying reproduction number, Rt at time t, are often inferred from incident time series, with the aim of informing policymakers on the growth rate of outbreaks or testing hypotheses about the effectiveness of public health interventions. However, the reliability of these inferences depends critically on reporting errors and latencies innate to those time series. While studies have proposed corrections for these issues, methodology for formally assessing how these noise sources degrade Rt estimate quality is lacking. By adapting Fisher information and experimental design theory, we develop an analytical framework to quantify the uncertainty induced by under-reporting and delays in reporting infections. This yields a novel metric, defined by the geometric means of reporting and cumulative delay probabilities, for ranking surveillance data informativeness. We apply this metric to two primary data sources for inferring Rt: epidemic case and death curves. We show that the assumption of death curves as more reliable, commonly made for acute infectious diseases such as COVID-19 and influenza, is not obvious and possibly untrue in many settings. Our framework clarifies and quantifies how actionable information about pathogen transmissibility is lost due to surveillance limitations.

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“…Whether they show symptoms of coronavirus disease 2019 (COVID-19), , people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shed the virus and its genetic material in their body fluids. − Because these body fluids are frequently collected via domestic wastewater, wastewater-based epidemiology has emerged as a useful method for monitoring COVID-19 trends within communities. , SARS-CoV-2 RNA levels in primary influent , and primary solids , have been observed to correlate with COVID-19 cases and positivity rate. When clinical testing is limited or delayed, wastewater signals may even precede COVID-19 trends as estimated by clinical surveillance. − More recently, SARS-CoV-2 RNA measurements in wastewater have been used to estimate the reproductive number during COVID-19 epidemics with some finding wastewater outperforms clinical testing to estimate COVID-19 incidence during periods of “high [clinical] positivity” (>26%) . Importantly, the effectiveness of vaccines, including the BNT162b2 mRNA vaccine, against shedding of SARS-CoV-2 RNA remains uncertain, , as is the resulting effect on wastewater monitoring strategies and design.…”

Section: Introductionmentioning

confidence: 99%

“…When clinical testing is limited or delayed, wastewater signals may even precede COVID-19 trends as estimated by clinical surveillance. 12−14 More recently, SARS-CoV-2 RNA measurements in wastewater have been used to estimate the reproductive number during COVID-19 epidemics 15 with some finding wastewater outperforms clinical testing to estimate COVID-19 incidence during periods of "high [clinical] positivity" (>26%). 16 Importantly, the effectiveness of vaccines, including the BNT162b2 mRNA vaccine, against shedding of SARS-CoV-2 RNA remains uncertain, 17,18 as is the resulting effect on wastewater monitoring strategies and design.…”

Section: ■ Introductionmentioning

confidence: 99%

Wastewater Surveillance during Mass COVID-19 Vaccination on a College Campus

Bivins

Bibby

2021

Environ. Sci. Technol. Lett.

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The suitability of wastewater monitoring following widespread vaccination against COVID-19 remains uncertain. SARS-CoV-2 RNA levels were monitored in wastewater solids during a university mass vaccination campaign in which >90% of the 12280 students were fully vaccinated (Pfizer-BioNTech, BNT162b2). SARS-CoV-2 RNA levels in wastewater solids correlated with the 7-day average of COVID-19 cases when lagged by 1–3 days (ρ = 0.51–0.55; p = 0.023–0.039). During and after the second vaccine dose, SARS-CoV-2 RNA was not detected in wastewater solids on 19 of 21 days (12 consecutive days of nondetection at the end of the semester), a significant decrease ( p = 0.027) in positivity rate. A large influx of outside visitors (move out and commencement) led to an immediate increase in wastewater SARS-CoV-2 RNA positivity (seven detections over seven days). Wastewater solids offer a sensitive matrix for environmental surveillance of COVID-19 at the subsewershed level (50% probability of RNA detection with two cases) both during and after mass vaccination. Mass vaccination was coincident with decreased shedding of SARS-CoV-2 RNA into wastewater. This suggests the absence of a large population of shedding infections, symptomatic or not, following mass vaccination among a university campus population.

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Wastewater-Based Estimation of the Effective Reproductive Number of SARS-CoV-2

Cited by 115 publications

References 65 publications

Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2

Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2

Quantifying the information in noisy epidemic curves

Wastewater Surveillance during Mass COVID-19 Vaccination on a College Campus

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