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Quest for Optimal Regression Models in SARS-CoV-2 Wastewater Based Epidemiology

Author

Listed:
  • Parisa Aberi

    (Department of Infrastructure, University Innsbruck, 6020 Innsbruck, Austria)

  • Rezgar Arabzadeh

    (Department of Infrastructure, University Innsbruck, 6020 Innsbruck, Austria)

  • Heribert Insam

    (Department of Microbiology, University Innsbruck, 6020 Innsbruck, Austria)

  • Rudolf Markt

    (Department of Microbiology, University Innsbruck, 6020 Innsbruck, Austria)

  • Markus Mayr

    (Department of Microbiology, University Innsbruck, 6020 Innsbruck, Austria)

  • Norbert Kreuzinger

    (Institute for Water Quality and Resource Management, Technology University Vienna, 1040 Vienna, Austria)

  • Wolfgang Rauch

    (Department of Infrastructure, University Innsbruck, 6020 Innsbruck, Austria)

Abstract

Wastewater-based epidemiology is a recognised source of information for pandemic management. In this study, we investigated the correlation between a SARS-CoV-2 signal derived from wastewater sampling and COVID-19 incidence values monitored by means of individual testing programs. The dataset used in the study is composed of timelines (duration approx. five months) of both signals at four wastewater treatment plants across Austria, two of which drain large communities and the other two drain smaller communities. Eight regression models were investigated to predict the viral incidence under varying data inputs and pre-processing methods. It was found that population-based normalisation and smoothing as a pre-processing of the viral load data significantly influence the fitness of the regression models. Moreover, the time latency lag between the wastewater data and the incidence derived from the testing program was found to vary between 2 and 7 days depending on the time period and site. It was found to be necessary to take such a time lag into account by means of multivariate modelling to boost the performance of the regression. Comparing the models, no outstanding one could be identified as all investigated models are revealing a sufficient correlation for the task. The pre-processing of data and a multivariate model formulation is more important than the model structure.

Suggested Citation

  • Parisa Aberi & Rezgar Arabzadeh & Heribert Insam & Rudolf Markt & Markus Mayr & Norbert Kreuzinger & Wolfgang Rauch, 2021. "Quest for Optimal Regression Models in SARS-CoV-2 Wastewater Based Epidemiology," IJERPH, MDPI, vol. 18(20), pages 1-17, October.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:20:p:10778-:d:656010
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    References listed on IDEAS

    as
    1. Hana Mlejnkova & Katerina Sovova & Petra Vasickova & Vera Ocenaskova & Lucie Jasikova & Eva Juranova, 2020. "Preliminary Study of Sars-Cov-2 Occurrence in Wastewater in the Czech Republic," IJERPH, MDPI, vol. 17(15), pages 1-9, July.
    2. Parbat, Debanjan & Chakraborty, Monisha, 2020. "A python based support vector regression model for prediction of COVID19 cases in India," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    3. Jonathan Karnon, 2020. "A Simple Decision Analysis of a Mandatory Lockdown Response to the COVID-19 Pandemic," Applied Health Economics and Health Policy, Springer, vol. 18(3), pages 329-331, June.
    4. Smriti Mallapaty, 2020. "How sewage could reveal true scale of coronavirus outbreak," Nature, Nature, vol. 580(7802), pages 176-177, April.
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