Faecal shedding models for SARS-CoV-2 RNA among hospitalised patients and implications for wastewater-based epidemiology

The concentration of SARS-CoV-2 RNA in faeces is not well characterised, posing challenges for quantitative wastewater-based epidemiology (WBE). We developed hierarchical models for faecal RNA shedding and fitted them to data from six studies. A mean concentration of 1.9 x 10(6) mL(-1) (2.3 x 10(5)-2.0 x 10(8) 95% credible interval) was found among unvaccinated inpatients, not considering differences in shedding between viral variants. Limits of quantification could account for negative samples based on Bayesian model comparison. Inpatients represented the tail of the shedding profile with a half-life of 34 hours (28-43 95% credible interval), suggesting that WBE can be a leading indicator for clinical presentation. Shedding among inpatients could not explain the high RNA concentrations found in wastewater, consistent with more abundant shedding during the early infection course.

Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh

This research aimed to investigate the spatial autocorrelation and heterogeneity throughout Bangladesh's 64 districts. Moran I and Geary C are used to measure spatial autocorrelation. Different conventional models, such as Poisson-Gamma and Poisson-Lognormal, and spatial models, such as Conditional Autoregressive (CAR) Model, Convolution Model, and modified CAR Model, have been employed to detect the spatial heterogeneity. Bayesian hierarchical methods via Gibbs sampling are used to implement these models. The best model is selected using the Deviance Information Criterion. Results revealed Dhaka has the highest relative risk due to the city's high population density and growth rate. This study identifies which district has the highest relative risk and which districts adjacent to that district also have a high risk, which allows for the appropriate actions to be taken by the government agencies and communities to mitigate the risk effect. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Study on Urban Travel Volume During the Outbreak of COVID-19

The distribution and change of travel intensity reflect the pattern of the city and the activity of trip population. It is important to understand the pattern of the city and the activity of trip flow for urban planning and government decision-making. This paper constructs a Bayesian hierarchical spatiotemporal model with three effects: space, time, and space-time, which uses the travel intensity data during the outbreak of the novel coronavirus (COVID-19) in Hubei province (2020.01.01–2020.05.02). With the help of Markoff’s Monte Carlo method, this paper analyzes the distribution and fluctuation of traffic flow in each city of Hubei province. The results show that the space-time model does not deteriorate compared with the main space model. The study found that nearly 41% of cities with a spatial effect higher than 1 were active during the epidemic in Hubei province and the time effect of travel intensity in Hubei province dropped rapidly from 2 to 0.5 after cities in Hubei province issued measures to close the cities one after another, which lasted nearly a month. Strict social distance intervention is one of the important reasons for Hubei province to control the epidemic effectively in a few months. At the same time, in the stability analysis of the city, we found that Wuhan belongs to an unstable area, which is unfavorable to the control of COVID-19. The research results provide a certain perspective for COVID-19 prevention and control: when there are confirmed patients in the province, we believe that the government should first pay attention to those cities with high spatial effect and instability. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Novel Method to Reduce ELISA Serial Dilution Assay Workload Applied to SARS-CoV-2 and Seasonal HCoVs.

Assays using ELISA measurements on serially diluted serum samples have been heavily used to measure serum reactivity to SARS-CoV-2 antigens and are widely used in virology and elsewhere in biology. We test a method using Bayesian hierarchical modelling to reduce the workload of these assays and measure reactivity of SARS-CoV-2 and HCoV antigens to human serum samples collected before and during the COVID-19 pandemic. Inflection titers for SARS-CoV-2 full-length spike protein (S1S2), spike protein receptor-binding domain (RBD), and nucleoprotein (N) inferred from 3 spread-out dilutions correlated with those inferred from 8 consecutive dilutions with an R2 value of 0.97 or higher. We confirm existing findings showing a small proportion of pre-pandemic human serum samples contain cross-reactive antibodies to SARS-CoV-2 S1S2 and N, and that SARS-CoV-2 infection increases serum reactivity to the beta-HCoVs OC43 and HKU1 S1S2. In serial dilution assays, large savings in resources and/or increases in throughput can be achieved by reducing the number of dilutions measured and using Bayesian hierarchical modelling to infer inflection or endpoint titers. We have released software for conducting these types of analysis.