SARS-CoV2 in public spaces in West London, UK during COVID-19 pandemic.

BACKGROUND: Spread of SARS-CoV2 by aerosol is considered an important mode of transmission over distances >2 m, particularly indoors. OBJECTIVES: We determined whether SARS-CoV2 could be detected in the air of enclosed/semi-enclosed public spaces. METHODS AND ANALYSIS: Between March 2021 and December 2021 during the easing of COVID-19 pandemic restrictions after a period of lockdown, we used total suspended and size-segregated particulate matter (PM) samplers for the detection of SARS-CoV2 in hospitals wards and waiting areas, on public transport, in a university campus and in a primary school in West London. RESULTS: We collected 207 samples, of which 20 (9.7%) were positive for SARS-CoV2 using quantitative PCR. Positive samples were collected from hospital patient waiting areas, from hospital wards treating patients with COVID-19 using stationary samplers and from train carriages in London underground using personal samplers. Mean virus concentrations varied between 429 500 copies/m3 in the hospital emergency waiting area and the more frequent 164 000 copies/m3 found in other areas. There were more frequent positive samples from PM samplers in the PM2.5 fractions compared with PM10 and PM1. Culture on Vero cells of all collected samples gave negative results. CONCLUSION: During a period of partial opening during the COVID-19 pandemic in London, we detected SARS-CoV2 RNA in the air of hospital waiting areas and wards and of London Underground train carriage. More research is needed to determine the transmission potential of SARS-CoV2 detected in the air.

Real-time updating of dynamic social networks for COVID-19 vaccination strategies.

Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamic systems where contact networks are highly time-variant. Furthermore, the numerous mutations of SARS-CoV-2 have a significant impact on the infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamic networks collected in a high school, followed by sequential multi-layer networks generated relying on the Barabasi-Albert model emulating large-scale social networks with several communities.

Real-time updating of dynamic social networks for COVID-19 vaccination strategies

Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamic systems where contact networks are highly time-variant. Furthermore, the numerous mutations of SARS-CoV-2 have a significant impact on the infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamic networks collected in a high school, followed by sequential multi-layer networks generated relying on the Barabasi-Albert model emulating large-scale social networks with several communities.

Data Assimilation Predictive GAN (DA-PredGAN) Applied to a Spatio-Temporal Compartmental Model in Epidemiology.

We propose a novel use of generative adversarial networks (GANs) (i) to make predictions in time (PredGAN) and (ii) to assimilate measurements (DA-PredGAN). In the latter case, we take advantage of the natural adjoint-like properties of generative models and the ability to simulate forwards and backwards in time. GANs have received much attention recently, after achieving excellent results for their generation of realistic-looking images. We wish to explore how this property translates to new applications in computational modelling and to exploit the adjoint-like properties for efficient data assimilation. We apply these methods to a compartmental model in epidemiology that is able to model space and time variations, and that mimics the spread of COVID-19 in an idealised town. To do this, the GAN is set within a reduced-order model, which uses a low-dimensional space for the spatial distribution of the simulation states. Then the GAN learns the evolution of the low-dimensional states over time. The results show that the proposed methods can accurately predict the evolution of the high-fidelity numerical simulation, and can efficiently assimilate observed data and determine the corresponding model parameters.

SARS-CoV2 and Air Pollution Interactions: Airborne Transmission and COVID-19

A link between outdoor pollution of particulate matter (PM) and the mortality from COVID-19 disease has been reported. The potential interaction of SARS-CoV2 emitted from an infected subject in the form of droplets or as an aerosol with PM2.5 (PM of 2.5 μm or less in aerodynamic diameter) may modulate SARS-CoV2 replication and infectivity. This may represent an important airborne route of transmission, which could lead to pneumonia and a poor outcome from COVID-19. Further studies are needed to assess the potential infectivity and severity of such transmission.

Active Air Monitoring for Understanding the Ventilation and Infection Risks of SARS-CoV-2 Transmission in Public Indoor Spaces

Indoor, airborne, transmission of SARS-CoV-2 is a key infection route. We monitored fourteen different indoor spaces in order to assess the risk of SARS-CoV-2 transmission. PM2.5 and CO2 concentrations were simultaneously monitored in order to understand aerosol exposure and ventilation conditions. Average PM2.5 concentrations were highest in the underground station (261 ±62.8 μgm-3), followed by outpatient and emergency rooms in hospitals located near major arterial roads (38.6 ±20.4 μgm-3), the respiratory wards, medical day units and intensive care units recorded concentrations in the range of 5.9 to 1.1 μgm-3. Mean CO2 levels across all sites did not exceed 1000 ppm, the respiratory ward (788 ±61 ppm) and the pub (bar) (744 ±136 ppm) due to high occupancy. The estimated air change rates implied that there is sufficient ventilation in these spaces to manage increased levels of occupancy. The infection probability in the medical day unit of hospital 3, was 1.6-times and 2.2-times higher than the emergency and outpatient waiting rooms in hospitals 4 and 5, respectively. The temperature and relative humidity recorded at most sites was below 27 °C, and 40% and, in sites with high footfall and limited air exchange, such as the hospital medical day unit, indicate a high risk of airborne SARS-CoV-2 transmission.

Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic.

The outbreak of the coronavirus disease 2019 (COVID-19) has now spread throughout the globe infecting over 150 million people and causing the death of over 3.2 million people. Thus, there is an urgent need to study the dynamics of epidemiological models to gain a better understanding of how such diseases spread. While epidemiological models can be computationally expensive, recent advances in machine learning techniques have given rise to neural networks with the ability to learn and predict complex dynamics at reduced computational costs. Here we introduce two digital twins of a SEIRS model applied to an idealised town. The SEIRS model has been modified to take account of spatial variation and, where possible, the model parameters are based on official virus spreading data from the UK. We compare predictions from one digital twin based on a data-corrected Bidirectional Long Short-Term Memory network with predictions from another digital twin based on a predictive Generative Adversarial Network. The predictions given by these two frameworks are accurate when compared to the original SEIRS model data. Additionally, these frameworks are data-agnostic and could be applied to towns, idealised or real, in the UK or in other countries. Also, more compartments could be included in the SEIRS model, in order to study more realistic epidemiological behaviour.

Numerical study of COVID-19 spatial-temporal spreading in London.

A recent study reported that an aerosolized virus (COVID-19) can survive in the air for a few hours. It is highly possible that people get infected with the disease by breathing and contact with items contaminated by the aerosolized virus. However, the aerosolized virus transmission and trajectories in various meteorological environments remain unclear. This paper has investigated the movement of aerosolized viruses from a high concentration source across a dense urban area. The case study looks at the highly air polluted areas of London: University College Hospital (UCH) and King's Cross and St Pancras International Station (KCSPI). We explored the spread and decay of COVID-19 released from the hospital and railway stations with the prescribed meteorological conditions. The study has three key findings: the primary result is that the concentration of viruses decreases rapidly by a factor of 2-3 near the sources although the virus may travel from meters up to hundreds of meters from the source location for certain meteorological conditions. The secondary finding shows viruses released into the atmosphere from entry and exit points at KCSPI remain trapped within a small radial distance of < 50 m. This strengthens the case for the use of face coverings to reduce the infection rate. The final finding shows that there are different levels of risk at various door locations for UCH; depending on which door is used there can be a higher concentration of COVID-19. Although our results are based on London, since the fundamental knowledge processes are the same, our study can be further extended to other locations (especially the highly air polluted areas) in the world.

The ventilation of buildings and other mitigating measures for COVID-19: a focus on wintertime.

The year 2020 has seen the emergence of a global pandemic as a result of the disease COVID-19. This report reviews knowledge of the transmission of COVID-19 indoors, examines the evidence for mitigating measures, and considers the implications for wintertime with a focus on ventilation.