Censored regression modelling to predict virus inactivation in wastewaters

Brainard, Julii, Pond, Katherine and Hunter, Paul R. (2017) Censored regression modelling to predict virus inactivation in wastewaters. Environmental Science and Technology, 51 (3). 1795–1801. ISSN 1520-5851

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      Abstract

      Among the many uncertainties presented by poorly studied pathogens is possible transmission via human faecal material or waste waters. For instance, possible disease transmission associated with poor sanitation was a documented concern during the Ebola outbreak that started in West Africa in 2013. Using published experimental data on virus inactivation rates in wastewater and similar matrices, we extracted data to construct a model predicting the T90 (1 x log10 inactivation measured in seconds) of a virus. Extracted data were: RNA or DNA, enveloped or not, primary transmission pathway, temperature, pH, light levels and matrix. From the primary details, we further determined matrix level of contamination, genus and family. Prior to model construction, three records were separated for verification. A censored normal regression model provided the best fit model, which predicted T90 from DNA or RNA structure, enveloped status, whether primary transmission pathway was faecal-oral, temperature and whether contamination was low, medium or high. Model residuals and predicted values are evaluated against observed values. Model predictions were verified against independent data, with consideration of both mean predicted value and 95% confidence ranges. A relatively simple model can predict virus inactivation rates from virus and matrix attributes, providing valuable input when formulating risk management strategies for little studied pathogens.

      Item Type: Article
      Uncontrolled Keywords: wastewaters,viruses,inactivation,faeces,model,censored regression
      Faculty \ School: Faculty of Medicine and Health Sciences > Norwich Medical School
      Depositing User: Pure Connector
      Date Deposited: 06 Jan 2017 00:01
      Last Modified: 09 Apr 2019 11:29
      URI: https://ueaeprints.uea.ac.uk/id/eprint/61925
      DOI: 10.1021/acs.est.6b05190

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