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Statistical Analysis of SARS-CoV-2 Using Wastewater-Based Data of Stockholm, Sweden

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  • Aashlesha Chekkala

    (Department of Chemical Engineering, KTH Royal Institute of Technology, 10044 Stockholm, Sweden)

  • Merve Atasoy

    (Department of Chemical Engineering, KTH Royal Institute of Technology, 10044 Stockholm, Sweden
    UNLOCK, Wageningen University & Research and Technical University Delft, 6708PB Wageningen, The Netherlands)

  • Cecilia Williams

    (Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, 17121 Solna, Sweden)

  • Zeynep Cetecioglu

    (Department of Chemical Engineering, KTH Royal Institute of Technology, 10044 Stockholm, Sweden
    Department of Industrial Biotechnology, KTH Royal Institute of Technology, AlbaNova University Center, 11421 Stockholm, Sweden)

Abstract

An approach based on wastewater epidemiology can be used to monitor the COVID-19 pandemic by assessing the gene copy number of SARS-CoV-2 in wastewater. In the present study, we statistically analyzed such data from six inlets of three wastewater treatment plants, covering six regions of Stockholm, Sweden, collected over an approximate year period (week 16 of 2020 to week 22 of 2021). SARS-CoV-2 gene copy number and population-based biomarker PMMoV, as well as clinical data, such as the number of positive cases, intensive care unit numbers, and deaths, were analyzed statistically using correlations and principal component analysis (PCA). Despite the population differences, the PCA for the Stockholm dataset showed that the case numbers are well grouped across wastewater treatment plants. Furthermore, when considering the data from the whole of Stockholm, the wastewater characteristics (flow rate m 3 /day, PMMoV Ct value, and SARS-CoV gene copy number) were significantly correlated with the public health agency’s report of SARS-CoV-2 infection rates (0.419 to 0.95, p -value < 0.01). However, while the PCA results showed that the case numbers for each wastewater treatment plant were well grouped concerning PC1 (37.3%) and PC2 (19.67%), the results from the correlation analysis for the individual wastewater treatment plants showed varied trends. SARS-CoV-2 fluctuations can be accurately predicted through statistical analyses of wastewater-based epidemiology, as demonstrated in this study.

Suggested Citation

  • Aashlesha Chekkala & Merve Atasoy & Cecilia Williams & Zeynep Cetecioglu, 2023. "Statistical Analysis of SARS-CoV-2 Using Wastewater-Based Data of Stockholm, Sweden," IJERPH, MDPI, vol. 20(5), pages 1-12, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:5:p:4181-:d:1081048
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    References listed on IDEAS

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    1. Stefania Salvatore & Jørgen Gustav Bramness & Malcolm J Reid & Kevin Victor Thomas & Christopher Harman & Jo Røislien, 2015. "Wastewater-Based Epidemiology of Stimulant Drugs: Functional Data Analysis Compared to Traditional Statistical Methods," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-14, September.
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