Predicting COVID-19 Incidence Using Wastewater Surveillance Data, Denmark, October 2021-June 2022

Oliver McManus; Lasse Engbo Christiansen; Maarten Nauta; Lene Wulff Krogsgaard; Naja Stolberg Bahrenscheer; Lene von Kappelgaard; Tobias Christiansen; Mikkel Hansen; Nicco Claudio Hansen; Jonas Kähler; Anders Rasmussen; Stine Raith Richter; Lasse Dam Rasmussen; Kristina Træholt Franck; Steen Ethelberg

doi:10.3201/eid2908.221634

Predicting COVID-19 Incidence Using Wastewater Surveillance Data, Denmark, October 2021-June 2022

Oliver McManus, Lasse Engbo Christiansen, Maarten Nauta, Lene Wulff Krogsgaard, Naja Stolberg Bahrenscheer, Lene von Kappelgaard, Tobias Christiansen, Mikkel Hansen, Nicco Claudio Hansen, Jonas Kähler, Anders Rasmussen, Stine Raith Richter, Lasse Dam Rasmussen, Kristina Træholt Franck, Steen Ethelberg^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

Abstract

Analysis of wastewater is used in many settings for surveillance of SARS-CoV-2, but it remains unclear how well wastewater testing results reflect incidence. Denmark has had an extensive wastewater analysis system that conducts 3 weekly tests in ≈200 sites and has 85% population coverage; the country also offers free SARSCoV- 2 PCR tests to all residents. Using time series analysis for modeling, we found that wastewater data, combined with information on circulating variants and the number of human tests performed, closely fitted the incidence curve of persons testing positive. The results were consistent at a regional level and among a subpopulation of frequently tested healthcare personnel. We used wastewater analysis data to estimate incidence after testing was reduced to a minimum after March 2022. These results imply that data from a large-scale wastewater surveillance system can serve as a good proxy for COVID-19 incidence and for epidemic control.

Original language	English
Pages (from-to)	1589-1597
Number of pages	9
Journal	Emerging Infectious Diseases
Volume	29
Issue number	8
DOIs	https://doi.org/10.3201/eid2908.221634
Publication status	Published - Aug 2023
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2023 Centers for Disease Control and Prevention (CDC). All rights reserved.

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10.3201/eid2908.221634

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McManus, O., Christiansen, L. E., Nauta, M., Krogsgaard, L. W., Bahrenscheer, N. S., von Kappelgaard, L., Christiansen, T., Hansen, M., Hansen, N. C., Kähler, J., Rasmussen, A., Richter, S. R., Rasmussen, L. D., Franck, K. T., & Ethelberg, S. (2023). Predicting COVID-19 Incidence Using Wastewater Surveillance Data, Denmark, October 2021-June 2022. Emerging Infectious Diseases, 29(8), 1589-1597. https://doi.org/10.3201/eid2908.221634

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title = "Predicting COVID-19 Incidence Using Wastewater Surveillance Data, Denmark, October 2021-June 2022",

abstract = "Analysis of wastewater is used in many settings for surveillance of SARS-CoV-2, but it remains unclear how well wastewater testing results reflect incidence. Denmark has had an extensive wastewater analysis system that conducts 3 weekly tests in ≈200 sites and has 85% population coverage; the country also offers free SARSCoV- 2 PCR tests to all residents. Using time series analysis for modeling, we found that wastewater data, combined with information on circulating variants and the number of human tests performed, closely fitted the incidence curve of persons testing positive. The results were consistent at a regional level and among a subpopulation of frequently tested healthcare personnel. We used wastewater analysis data to estimate incidence after testing was reduced to a minimum after March 2022. These results imply that data from a large-scale wastewater surveillance system can serve as a good proxy for COVID-19 incidence and for epidemic control.",

author = "Oliver McManus and Christiansen, {Lasse Engbo} and Maarten Nauta and Krogsgaard, {Lene Wulff} and Bahrenscheer, {Naja Stolberg} and {von Kappelgaard}, Lene and Tobias Christiansen and Mikkel Hansen and Hansen, {Nicco Claudio} and Jonas K{\"a}hler and Anders Rasmussen and Richter, {Stine Raith} and Rasmussen, {Lasse Dam} and Franck, {Kristina Tr{\ae}holt} and Steen Ethelberg",

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McManus, O, Christiansen, LE, Nauta, M, Krogsgaard, LW, Bahrenscheer, NS, von Kappelgaard, L, Christiansen, T, Hansen, M, Hansen, NC, Kähler, J, Rasmussen, A, Richter, SR, Rasmussen, LD, Franck, KT & Ethelberg, S 2023, 'Predicting COVID-19 Incidence Using Wastewater Surveillance Data, Denmark, October 2021-June 2022', Emerging Infectious Diseases, vol. 29, no. 8, pp. 1589-1597. https://doi.org/10.3201/eid2908.221634

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AU - McManus, Oliver

AU - Christiansen, Lasse Engbo

AU - Nauta, Maarten

AU - Krogsgaard, Lene Wulff

AU - Bahrenscheer, Naja Stolberg

AU - von Kappelgaard, Lene

AU - Christiansen, Tobias

AU - Hansen, Mikkel

AU - Hansen, Nicco Claudio

AU - Kähler, Jonas

AU - Rasmussen, Anders

AU - Richter, Stine Raith

AU - Rasmussen, Lasse Dam

AU - Franck, Kristina Træholt

AU - Ethelberg, Steen

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AB - Analysis of wastewater is used in many settings for surveillance of SARS-CoV-2, but it remains unclear how well wastewater testing results reflect incidence. Denmark has had an extensive wastewater analysis system that conducts 3 weekly tests in ≈200 sites and has 85% population coverage; the country also offers free SARSCoV- 2 PCR tests to all residents. Using time series analysis for modeling, we found that wastewater data, combined with information on circulating variants and the number of human tests performed, closely fitted the incidence curve of persons testing positive. The results were consistent at a regional level and among a subpopulation of frequently tested healthcare personnel. We used wastewater analysis data to estimate incidence after testing was reduced to a minimum after March 2022. These results imply that data from a large-scale wastewater surveillance system can serve as a good proxy for COVID-19 incidence and for epidemic control.

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Predicting COVID-19 Incidence Using Wastewater Surveillance Data, Denmark, October 2021-June 2022

Abstract

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