Censored Regression Modeling to Predict Virus Inactivation in Wastewaters

Abstract

Among the many uncertainties presented by poorly studied pathogens is possible transmission via human fecal material or wastewaters. Such worries were a documented concern during the 2013 Ebola outbreak in West Africa. Using published experimental data on virus inactivation rates in wastewater and similar matrices, we extracted data to construct a model predicting the T90 (1 × log10 inactivation measured in seconds) of a virus. Extracted data were as follows: RNA or DNA genome, 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 taxonomic 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 fecal-oral, temperature, and whether contamination was low, medium, or high. Model residuals and predicted values were evaluated against observed values. Mean values of model predictions were compared to independent data and considering 95% confidence ranges (which could be quite large). 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.