A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic.

The potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe during the current COVID-19 pandemic. Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted to evaluate the relationship between viral concentration and COVID-19 case numbers at the catchment areas of sewage treatment works (STWs) over time. However, no attempt has been made to develop a model that predicts wastewater concentration at fine spatio-temporal resolutions covering an entire country, a necessary step towards using wastewater monitoring for the early detection of local outbreaks. We consider weekly averages of flow-normalised viral concentration, reported as the number of SARS-CoV-2N1 gene copies per litre (gc/L) of wastewater available at 303 STWs over the period between 1 June 2021 and 30 March 2022. We specify a spatially continuous statistical model that quantifies the relationship between weekly viral concentration and a collection of covariates covering socio-demographics, land cover and virus associated genomic characteristics at STW catchment areas while accounting for spatial and temporal correlation. We evaluate the model's predictive performance at the catchment level through 10-fold cross-validation. We predict the weekly viral concentration at the population-weighted centroid of the 32,844 lower super output areas (LSOAs) in England, then aggregate these LSOA predictions to the Lower Tier Local Authority level (LTLA), a geography that is more relevant to public health policy-making. We also use the model outputs to quantify the probability of local changes of direction (increases or decreases) in viral concentration over short periods (e.g. two consecutive weeks). The proposed statistical framework can predict SARS-CoV-2 viral concentration in wastewater at high spatio-temporal resolution across England. Additionally, the probabilistic quantification of local changes can be used as an early warning tool for public health surveillance.

Misdiagnoses in the Context of Suspected Pandemic Influenza or Coronavirus Disease 2019: A Systematic Review.

There have been numerous reports of patients initially misdiagnosed in the 2009 H1N1 influenza and coronavirus disease 2019 (COVID-19) pandemics within the literature. A systematic review was undertaken to collate misdiagnoses during the H1N1 and COVID-19 pandemics and identify which cognitive biases may contribute to this. MEDLINE, Embase, Cochrane and MedRxiv databases were searched for misdiagnoses or cognitive biases resulting in misdiagnosis, occurring during the H1N1 or COVID-19 virus pandemics. Eligible studies were assessed for quality using JBI criteria; primary outcome was the final diagnosis. Sixty-nine studies involving 2551 participants were included. We identified 686 cases of misdiagnosis, categorized as viral respiratory infection, other respiratory infection, non-respiratory infection, and non-infective. Misdiagnoses are listed and relevant investigations are offered. No article described prospective assessment of decision making in the pandemic setting or debiasing diagnostic thinking. Further research is required to understand why misdiagnoses occur and harm arises and how clinicians can be assisted in their decision making in a pandemic context.

Experimental evaluation of respiratory droplet spread to rooms connected by a central ventilation system.

This article presents results from an experimental study to ascertain the transmissibility of the SARS-CoV-2 virus between rooms in a building that are connected by a central ventilation system. Respiratory droplet surrogates made of mucus and virus mimics were released in one room in a test building, and measurements of concentration levels were made in other rooms connected via the ventilation system. The paper presents experimental results for different ventilation system configurations, including ventilation rate, filtration level (up to MERV-13), and fractional outdoor air intake. The most important finding is that respiratory droplets can and do transit through central ventilation systems, suggesting a mechanism for viral transmission (and COVID-19 specifically) within the built environment in reasonable agreement with well-mixed models. We also find the deposition of small droplets (0.5-4 µm) on room walls to be negligibly small.

Size dependent infectivity of SARS-CoV-2 via respiratory droplets spread through central ventilation systems

Here we evaluate the transport of respiratory droplets that carry SARS-CoV-2 through central air handling systems in multiroom buildings. Respiratory droplet size modes arise from the bronchioles representing the lungs and lower respiratory tract, the larynx representing the upper respiratory tract including vocal cords, or the oral cavity. The size distribution of each mode remains largely conserved, although the magnitude of each droplet mode changes as infected individuals breathe, speak, sing, laugh, cough, and sneeze. Here we evaluate how each type of respiratory droplet transits central ventilation systems and the implications thereof for infectivity of COVID-19. We find that while larger oral droplets can transmit through the air handling systems, their size and concentration are greatly reduced with but few oral droplets leaving the source room. In contrast, the smaller droplets that originate from the bronchioles and larynx are much more effective in transiting through the air handling system into connected rooms. This suggests that ratio of lower respiratory or deep lung infections increases relative to upper respiratory infections in rooms connected by central air handling systems. Also, increasing the temperature and humidity in the range considered after the droplets have achieved an “equilibrium” size reduces the probability of infection.

Investigation of potential aerosol transmission and infectivity of SARS-CoV-2 through central ventilation systems.

The COVID-19 pandemic has raised concern of viral spread within buildings. Although near-field transmission and infectious spread within individual rooms are well studied, the impact of aerosolized spread of SARS-CoV-2 via air handling systems within multiroom buildings remains unexplored. This study evaluates the concentrations and probabilities of infection for both building interior and exterior exposure sources using a well-mixed model in a multiroom building served by a central air handling system (without packaged terminal air conditioning). In particular, we compare the influence of filtration, air change rates, and the fraction of outdoor air. When the air supplied to the rooms comprises both outdoor air and recirculated air, we find filtration lowers the concentration and probability of infection the most in connected rooms. We find that increasing the air change rate removes virus from the source room faster but also increases the rate of exposure in connected rooms. Therefore, slower air change rates reduce infectivity in connected rooms at shorter durations. We further find that increasing the fraction of virus-free outdoor air is helpful, unless outdoor air is infective in which case pathogen exposure inside persists for hours after a short-term release. Increasing the outdoor air to 33% or the filter to MERV-13 decreases the infectivity in the connected rooms by 19% or 93% respectively, relative to a MERV-8 filter with 9% outdoor air based on 100 quanta/h of 5 µm droplets, a breathing rate of 0.48 m3/h, and the building dimensions and air handling system considered.