Mixed-Effects Models with Skewed Distributions for Time-Varying Decay Rate in HIV Dynamics

A Bayesian approach for mixed effects state-space models under skewness and heavy tails

Human immunodeficiency virus (HIV) dynamics have been the focus of epidemiological and biostatistical research during the past decades to understand the progression of acquired immunodeficiency syndrome (AIDS) in the population. Although there are several approaches for modeling HIV dynamics, one of the most popular is based on Gaussian mixed-effects models because of its simplicity from the implementation and interpretation viewpoints. However, in some situations, Gaussian mixed-effects models cannot (a) capture serial correlation existing in longitudinal data, (b) deal with missing observations properly, and (c) accommodate skewness and heavy tails frequently presented in patients' profiles. For those cases, mixed-effects state-space models (MESSM) become a powerful tool for modeling correlated observations, including HIV dynamics, because of their flexibility in modeling the unobserved states and the observations in a simple way. Consequently, our proposal considers an MESSM where the observations' error distribution is a skew-t. This new approach is more flexible and can accommodate data sets exhibiting skewness and heavy tails. Under the Bayesian paradigm, an efficient Markov chain Monte Carlo algorithm is implemented. To evaluate the properties of the proposed models, we carried out some exciting simulation studies, including missing data in the generated data sets. Finally, we illustrate our approach with an application in the AIDS Clinical Trial Group Study 315 (ACTG-315) clinical trial data set.

Viability of SARS-CoV-2 in river water and wastewater at different temperatures and solids content

COVID-19 patients can excrete viable SARS-CoV-2 virus via urine and faeces, which has raised concerns over the possibility of COVID-19 transmission via aerosolized contaminated water or via the faecal-oral route. These concerns are especially exacerbated in many low- and middle-income countries, where untreated sewage is frequently discharged to surface waters. SARS-CoV-2 RNA has been detected in river water (RW) and raw wastewater (WW) samples. However, little is known about SARS-CoV-2 viability in these environmental matrices. Determining the persistence of SARS-CoV-2 in water under different environmental conditions is of great importance for basic assumptions in quantitative microbial risk assessment (QMRA). In this study, the persistence of SARS-CoV-2 was assessed using plaque assays following spiking of RW and WW samples with infectious SARS-CoV-2 that was previously isolated from a COVID-19 patient. These assays were carried out on autoclaved RW and WW samples, filtered (0.22 µm) and unfiltered, at 4 °C and 24 °C. Linear and nonlinear regression models were adjusted to the data. The Weibull regression model achieved the lowest root mean square error (RMSE) and was hence chosen to estimate T90 and T99 (time required for 1 log and 2 log reductions, respectively). SARS-CoV-2 remained viable longer in filtered compared with unfiltered samples. RW and WW showed T90 values of 1.9 and 1.2 day and T99 values of 6.4 and 4.0 days, respectively. When samples were filtered through 0.22 µm pore size membranes, T90 values increased to 3.3 and 1.5 days, and T99 increased to 8.5 and 4.5 days, for RW and WW samples, respectively. Remarkable increases in SARS-CoV-2 persistence were observed in assays at 4 °C, which showed T90 values of 7.7 and 5.5 days, and T99 values of 18.7 and 17.5 days for RW and WW, respectively. These results highlight the variability of SARS-CoV-2 persistence in water and wastewater matrices and can be highly relevant to efforts aimed at quantifying water-related risks, which could be valuable for understanding and controlling the pandemic.