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Abstract

Background

Wastewater surveillance has expanded globally as a means to monitor spread of infectious diseases. An inherent challenge is substantial noise and bias in wastewater data because of the sampling and quantification process, limiting the applicability of wastewater surveillance as a monitoring tool.

Aim

To present an analytical framework for capturing the growth trend of circulating infections from wastewater data and conducting scenario analyses to guide policy decisions.

Methods

We developed a mathematical model for translating the observed SARS-CoV-2 viral load in wastewater into effective reproduction numbers. We used an extended Kalman filter to infer underlying transmissions by smoothing out observational noise. We also illustrated the impact of different countermeasures such as expanded vaccinations and non-pharmaceutical interventions on the projected number of cases using three study areas in Japan during 2021–22 as an example.

Results

Observed notified cases were matched with the range of cases estimated by our approach with wastewater data only, across different study areas and virus quantification methods, especially when the disease prevalence was high. Estimated reproduction numbers derived from wastewater data were consistent with notification-based reproduction numbers. Our projections showed that a 10–20% increase in vaccination coverage or a 10% reduction in contact rate may suffice to initiate a declining trend in study areas.

Conclusion

Our study demonstrates how wastewater data can be used to track reproduction numbers and perform scenario modelling to inform policy decisions. The proposed framework complements conventional clinical surveillance, especially when reliable and timely epidemiological data are not available.

© Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
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/content/10.2807/1560-7917.ES.2024.29.8.2300277
2024-02-22
2024-11-19
http://instance.metastore.ingenta.com/content/10.2807/1560-7917.ES.2024.29.8.2300277
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References

  1. Ainslie KEC, Backer JA, de Boer PT, van Hoek AJ, Klinkenberg D, Korthals Altes H, et al. A scenario modelling analysis to anticipate the impact of COVID-19 vaccination in adolescents and children on disease outcomes in the Netherlands, summer 2021. Euro Surveill. 2022;27(44):2101090.  https://doi.org/10.2807/1560-7917.ES.2022.27.44.2101090  PMID: 36330824 
  2. Keeling MJ, Dyson L, Tildesley MJ, Hill EM, Moore S. Comparison of the 2021 COVID-19 roadmap projections against public health data in England. Nat Commun. 2022;13(1):4924.  https://doi.org/10.1038/s41467-022-31991-0  PMID: 35995764 
  3. COVID 19 Scenario Modeling Hub. COVID 19 scenario modeling hub. [Accessed: 5 Mar 2023]. Available from: https://covid19scenariomodelinghub.org
  4. European Covid-19 Scenario Hub. European Covid-19 Scenario Hub. [Accessed: 5 Mar 2023]. Available from: https://covid19scenariohub.eu
  5. Leung K, Lau EHY, Wong CKH, Leung GM, Wu JT. Estimating the transmission dynamics of Omicron in Beijing, November to December 2022. BioRxiv2022.  https://doi.org/10.1101/2022.12.15.22283522 
  6. Klous G, McDonald S, de Boer P, van Hoek AJ, Franz E, van Rooijen M. Staat van Infectieziekten in Nederland, 2021. RIVM rapport 2022-0141. [State of Infectious Diseases in the Netherlands, 2021]. Bilthoven: Rijksinstituut voor Volksgezondheid en Milieu (RIVM); 2022. Available from:
  7. Naughton CC, Roman FA Jr, Alvarado AGF, Tariqi AQ, Deeming MA, Kadonsky KF, et al. Show us the data: global COVID-19 wastewater monitoring efforts, equity, and gaps. FEMS Microbes. 2023;4:xtad003.  https://doi.org/10.1093/femsmc/xtad003  PMID: 37333436 
  8. Hill V, Githinji G, Vogels CBF, Bento AI, Chaguza C, Carrington CVF, et al. Toward a global virus genomic surveillance network. Cell Host Microbe. 2023;31(6):861-73.  https://doi.org/10.1016/j.chom.2023.03.003  PMID: 36921604 
  9. Kitajima M, Ahmed W, Bibby K, Carducci A, Gerba CP, Hamilton KA, et al. SARS-CoV-2 in wastewater: State of the knowledge and research needs. Sci Total Environ. 2020;739:139076.  https://doi.org/10.1016/j.scitotenv.2020.139076  PMID: 32758929 
  10. Wolfe MK, Yu AT, Duong D, Rane MS, Hughes B, Chan-Herur V, et al. Use of wastewater for mpox outbreak surveillance in California. N Engl J Med. 2023;388(6):570-2.  https://doi.org/10.1056/NEJMc2213882  PMID: 36652340 
  11. Ryerson AB, Lang D, Alazawi MA, Neyra M, Hill DT, St George K, et al. Wastewater testing and detection of poliovirus type 2 genetically linked to virus isolated from a paralytic polio case - New York, March 9-October 11, 2022. MMWR Morb Mortal Wkly Rep. 2022;71(44):1418-24.  https://doi.org/10.15585/mmwr.mm7144e2  PMID: 36327157 
  12. Wastewater SPHERE. Wastewater SPHERE (SARS Public Health Environmental REsponse). [Accessed: 23 Mar 2023]. Available from: https://sphere.waterpathogens.org
  13. Ahmed W, Bivins A, Bertsch PM, Bibby K, Choi PM, Farkas K, et al. Surveillance of SARS-CoV-2 RNA in wastewater: Methods optimisation and quality control are crucial for generating reliable public health information. Curr Opin Environ Sci Health. 2020;17:82-93.  https://doi.org/10.1016/j.coesh.2020.09.003  PMID: 33052320 
  14. Jiang G, Wu J, Weidhaas J, Li X, Chen Y, Mueller J, et al. Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology. Water Res. 2022;218:118451.  https://doi.org/10.1016/j.watres.2022.118451  PMID: 35447417 
  15. Morvan M, Jacomo AL, Souque C, Wade MJ, Hoffmann T, Pouwels K, et al. An analysis of 45 large-scale wastewater sites in England to estimate SARS-CoV-2 community prevalence. Nat Commun. 2022;13(1):4313.  https://doi.org/10.1038/s41467-022-31753-y  PMID: 35879277 
  16. van Boven M, Hetebrij WA, Swart AM, Nagelkerke E, van der Beek RF, Stouten S, et al. Modelling patterns of SARS-CoV-2 circulation in the Netherlands, August 2020-February 2022, revealed by a nationwide sewage surveillance program. MedRxiv 2022. . https://doi.org/10.1101/2022.05.25.22275569 
  17. Li X, Kulandaivelu J, Zhang S, Shi J, Sivakumar M, Mueller J, et al. Data-driven estimation of COVID-19 community prevalence through wastewater-based epidemiology. Sci Total Environ. 2021;789:147947.  https://doi.org/10.1016/j.scitotenv.2021.147947  PMID: 34051491 
  18. Proverbio D, Kemp F, Magni S, Ogorzaly L, Cauchie H-M, Gonçalves J, et al. Model-based assessment of COVID-19 epidemic dynamics by wastewater analysis. Sci Total Environ. 2022;827:154235.  https://doi.org/10.1016/j.scitotenv.2022.154235  PMID: 35245552 
  19. Phan T, Brozak S, Pell B, Gitter A, Mena KD, Kuang Y, et al. A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data. MedRxiv2022 .  https://doi.org/10.1101/2022.07.17.22277721 
  20. Nourbakhsh S, Fazil A, Li M, Mangat CS, Peterson SW, Daigle J, et al. A wastewater-based epidemic model for SARS-CoV-2 with application to three Canadian cities. Epidemics. 2022;39:100560.  https://doi.org/10.1016/j.epidem.2022.100560  PMID: 35462206 
  21. Huisman JS, Scire J, Caduff L, Fernandez-Cassi X, Ganesanandamoorthy P, Kull A, et al. Wastewater-based estimation of the effective reproductive number of SARS-CoV-2. Environ Health Perspect. 2022;130(5):57011.  https://doi.org/10.1289/EHP10050  PMID: 35617001 
  22. Ando H, Iwamoto R, Kobayashi H, Okabe S, Kitajima M. The Efficient and Practical virus Identification System with ENhanced Sensitivity for Solids (EPISENS-S): A rapid and cost-effective SARS-CoV-2 RNA detection method for routine wastewater surveillance. Sci Total Environ. 2022;843:157101.  https://doi.org/10.1016/j.scitotenv.2022.157101  PMID: 35952875 
  23. Adachi Katayama Y, Hayase S, Ando Y, Kuroita T, Okada K, Iwamoto R, et al. COPMAN: A novel high-throughput and highly sensitive method to detect viral nucleic acids including SARS-CoV-2 RNA in wastewater. Sci Total Environ. 2023;856(Pt 1):158966.  https://doi.org/10.1016/j.scitotenv.2022.158966  PMID: 36162583 
  24. Yamayoshi S, Yasuhara A, Ito M, Akasaka O, Nakamura M, Nakachi I, et al. Antibody titers against SARS-CoV-2 decline, but do not disappear for several months. EClinicalMedicine. 2021;32:100734.  https://doi.org/10.1016/j.eclinm.2021.100734  PMID: 33589882 
  25. Puhach O, Meyer B, Eckerle I. SARS-CoV-2 viral load and shedding kinetics. Nat Rev Microbiol. 2023;21(3):147-61. PMID: 36460930 
  26. Backer JA, Eggink D, Andeweg SP, Veldhuijzen IK, van Maarseveen N, Vermaas K, et al. Shorter serial intervals in SARS-CoV-2 cases with Omicron BA.1 variant compared with Delta variant, the Netherlands, 13 to 26 December 2021. Euro Surveill. 2022;27(6):2200042.  https://doi.org/10.2807/1560-7917.ES.2022.27.6.2200042  PMID: 35144721 
  27. King AA, Ionides EL, Pascual M, Bouma MJ. Inapparent infections and cholera dynamics. Nature. 2008;454(7206):877-80.  https://doi.org/10.1038/nature07084  PMID: 18704085 
  28. Shaman J, Karspeck A, Yang W, Tamerius J, Lipsitch M. Real-time influenza forecasts during the 2012-2013 season. Nat Commun. 2013;4(1):2837.  https://doi.org/10.1038/ncomms3837  PMID: 24302074 
  29. Tokyo Metropolitan Government. Tokyo Metropolitan Government COVID-19 Information Website. Tokyo Metropolitan Government COVID-19 Information Website. [Accessed: 23 Mar 2023]. Available from: https://www.hokeniryo.metro.tokyo.lg.jp/kansen/corona_portal/info/covid19_opendata.html
  30. Gostic KM, McGough L, Baskerville EB, Abbott S, Joshi K, Tedijanto C, et al. Practical considerations for measuring the effective reproductive number, Rt. PLOS Comput Biol. 2020;16(12):e1008409.  https://doi.org/10.1371/journal.pcbi.1008409  PMID: 33301457 
  31. Fraser C. Estimating individual and household reproduction numbers in an emerging epidemic. PLoS One. 2007;2(8):e758.  https://doi.org/10.1371/journal.pone.0000758  PMID: 17712406 
  32. Cori A, Ferguson NM, Fraser C, Cauchemez S. A new framework and software to estimate time-varying reproduction numbers during epidemics. Am J Epidemiol. 2013;178(9):1505-12.  https://doi.org/10.1093/aje/kwt133  PMID: 24043437 
  33. Halloran ME. Longini Jr. IM, Struchiner CJ. Design and Analysis of Vaccine Studies. Springer, New York, NY; 2010.
  34. Braeye T, Catteau L, Brondeel R, van Loenhout JAF, Proesmans K, Cornelissen L, et al. Vaccine effectiveness against transmission of alpha, delta and omicron SARS-COV-2-infection, Belgian contact tracing, 2021-2022. Vaccine. 2023;41(20):3292-300.  https://doi.org/10.1016/j.vaccine.2023.03.069  PMID: 37085456 
  35. Wallinga J, Teunis P. Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Am J Epidemiol. 2004;160(6):509-16.  https://doi.org/10.1093/aje/kwh255  PMID: 15353409 
  36. Parag KV. Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves. PLOS Comput Biol. 2021;17(9):e1009347.  https://doi.org/10.1371/journal.pcbi.1009347  PMID: 34492011 
  37. Thompson RN, Stockwin JE, van Gaalen RD, Polonsky JA, Kamvar ZN, Demarsh PA, et al. Improved inference of time-varying reproduction numbers during infectious disease outbreaks. Epidemics. 2019;29:100356.  https://doi.org/10.1016/j.epidem.2019.100356  PMID: 31624039 
  38. Hewitt J, Trowsdale S, Armstrong BA, Chapman JR, Carter KM, Croucher DM, et al. Sensitivity of wastewater-based epidemiology for detection of SARS-CoV-2 RNA in a low prevalence setting. Water Res. 2022;211:118032.  https://doi.org/10.1016/j.watres.2021.118032  PMID: 35042077 
Wastewater-based reproduction numbers and projections of COVID-19 cases in three areas in Japan, November 2021 to December 2022
<p class="citation"> <span>Citation style for this article:</span> <span class="meta-value authors"> <a href="/search?value1=Shogo+Miyazawa&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Miyazawa Shogo</a>, <a href="/search?value1=Ting+Sam+Wong&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Wong Ting Sam</a>, <a href="/search?value1=Genta+Ito&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Ito Genta</a>, <a href="/search?value1=Ryo+Iwamoto&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Iwamoto Ryo</a>, <a href="/search?value1=Kozo+Watanabe&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Watanabe Kozo</a>, <a href="/search?value1=Michiel+van+Boven&option1=author&noRedirect=true" class="nonDisambigAuthorLink">van Boven Michiel</a>, <a href="/search?value1=Jacco+Wallinga&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Wallinga Jacco</a>, <a href="/search?value1=Fuminari+Miura&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Miura Fuminari</a></span>. Wastewater-based reproduction numbers and projections of COVID-19 cases in three areas in Japan, November 2021 to December 2022. <a href="/content/ecdc">Euro Surveill.</a> 2024;29(8):pii=2300277. <a href="https://doi.org/10.2807/1560-7917.ES.2024.29.8.2300277" title="doi link" target="_blank">https://doi.org/10.2807/1560-7917.ES.2024.29.8.2300277</a> <span class="ES_Article_citation"> <span class="generated">Received</span>: 22 May 2023;&nbsp;&nbsp; <span class="generated">Accepted</span>: 20 Dec 2023 </span> </p>
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