Cohort profile: Virus Watch: Understanding community incidence, symptom profiles, and transmission of COVID-19 in relation to population movement and behaviour (preprint)

[bullet] Virus Watch is a national community cohort study of COVID-19 in households in England and Wales, established in June 2020. The study aims to provide evidence on which public health approaches are most effective in reducing transmission, and investigate community incidence, symptoms, and transmission of COVID-19 in relation to population movement and behaviours. [bullet] 28,527 households and 58,628 participants of age (0-98 years, mean age 48), were recruited between June 2020 - July 2022 [bullet] Data collected include demographics, details on occupation, co-morbidities, medications, and infection-prevention behaviours. Households are followed up weekly with illness surveys capturing symptoms and their severity, activities in the week prior to symptom onset and any COVID-19 test results. Monthly surveys capture household finance, employment, mental health, access to healthcare, vaccination uptake, activities and contacts. Data have been linked to Hospital Episode Statistics (HES), inpatient and critical care episodes, outpatient visits, emergency care contacts, mortality, virology testing and vaccination data held by NHS Digital. [bullet] Nested within Virus Watch are a serology & PCR cohort study (n=12,877) and a vaccine evaluation study (n=19,555). [bullet] Study data are deposited in the Office of National Statistics (ONS) Secure Research Service (SRS). Survey data are available under restricted access upon request to ONS SRS.

Changes in mobility pre and post first SARS-CoV-2 vaccination: findings from a prospective community cohort study including GPS movement tracking in England and Wales (Virus Watch) (preprint)

Abstract Background: Some evidence suggests that individuals may change adherence to public health policies aimed at reducing contact, transmission and spread of the SARS-CoV-2 virus after they receive their first SARS-CoV-2 vaccination. In this study, we aim to estimate the rate of change in average daily travel distance from a participant's registered address before and after SARS-CoV-2 vaccination. Method: Participants were recruited into Virus Watch starting in June 2020. Weekly surveys were sent out to participants and vaccination status was collected from January 2021 onwards. Between September 2020 and February 2021, we invited 13,120 adult Virus Watch participants to contribute towards our tracker sub-cohort which uses the Global Positioning System (GPS) to collect data on movement. We used segmented linear regression to estimate the median daily travel distance before and after the first self-reported SARS-CoV-2 vaccine dose. Results: We analysed the daily travel distance of 228 vaccinated adults. Between 157 days prior to vaccination until the day before vaccination, the median daily travel distance travelled was 8.9km (IQR: 3.50km, 24.17km). Between the day of vaccination and 100 days after vaccination, the median daily travel distance travelled was 10.30km (IQR: 4.11, 27.53km). Between 157 days prior to vaccination and the vaccination date, there was a daily median decrease in mobility of 40m (95%CI: -51m, -31m, p-value <0.001) per day. After the removal of outlier data, and between the vaccination date and 99 days after vaccination, there was a median daily increase in movement of 45.0m (95%CI: 25m, 65m, p-value = <0.001). Restricting the analysis to the 3rd national lockdown (4th of January 2021 to the 5th of April 2021), we found a median daily movement increase of 9m (95%CI: -25m, 45m, p = 0.57) in the 30 days prior to vaccination and the vaccination date, and a median daily movement increase of 10m (95%CI: -60m, 94m, p-value = 0.69) in the 30 days after vaccination. Conclusions: Our study demonstrates the feasibility of collecting high volume geolocation data as part of research projects, and the utility of these for understanding public health issues. Our results are consistent with both an increase and decrease in movement after vaccination and suggest that, amongst Virus Watch participants, any changes in movement distances post-vaccination are small.

The effects of different travel modes on COVID-19 transmission in global cities (preprint)

Previous studies have been focused primarily on modelling and predicting the transmission of COVID-19. While little research has been conducted to understand the impacts of different travel modes on the transmission of COVID-19, without an explicit understanding of the travel mode effects, many people intuitively perceive non-motorized travel modes to be safer than public transit as passengers in public transit are confined to small, enclosed spaces where the virus can transmit more easily. During the period when urban mobility gradually returns towards what was called ‘normal’ and transit systems and urban facilities reopen, new waves of the pandemic might be generated as travel mode choices significantly differ across cities and different travel behaviors are associated with diverse infectious sources. Thus, the current study focuses on understanding the impact of different travel modes on the transmission of COVID-19 in the long-term and at world-wide scales, aspects that have not received much attention in the research literature. Accordingly, a multivariate time series analysis has been developed to examine the impacts of daily confirmed cases and travel modes, based on driving, public transit, and walking as recorded in the Apple Mobility Trends Reports on COVID-19 transmission risks in 71 cities throughout the world from January to November 2020. The impact of population density in built-up areas and the degree to which the `wearing' of facemasks affects infections are also investigated. Among the three travel modes we examine, driving is the safest way to commute because drivers are physically separate from crowds. Unexpectedly, walking has a relatively low risk when the population density in built-up areas is high, which suggests that, globally, people have increased awareness of pandemic prevention. Although the general public is more worried about using public transit, this mode can still be safe in many large cities, a factor that is vital for informing policy making and developing trust among citizens so they will continue to commute using public transit when strict preventative measures are in place. From another perspective, infectious sources make the largest contribution to daily confirmed cases, thus demonstrating the importance of strict quarantine measures to block the source of infection. The results and conclusions presented herein are based on an analysis of spatio-temporal data that helps inform policy making and enable cities to be kept open when controlling the pandemic, which has become an urgent task for the international community when rebuilding the economy.

Risk factors, symptom reporting, healthcare-seeking behaviour and adherence to public health guidance: protocol for Virus Watch, a prospective community cohort study (preprint)

Introduction The Coronavirus (COVID-19) Pandemic has caused significant global mortality and impacted lives around the world. Virus Watch aims to provide evidence on which public health approaches are most likely to be effective in reducing transmission and impact of the virus, and will investigate community incidence, symptom profiles, and transmission of COVID-19 in relation to population movement and behaviours. Methods and analysis Virus Watch is a household community cohort study of acute respiratory infections in England & Wales and will run from June 2020 to August 2021. The study aims to recruit 50,000 people, including 12,500 from minority ethnic backgrounds, for an online survey cohort and monthly antibody testing using home finger prick kits. Nested within this larger study will be a sub-cohort of 10,000 individuals, including 3,000 people from minority ethnic backgrounds. This cohort of 10,000 people will have full blood serology taken between October 2020 and January 2021 and repeat serology between May 2021 and August 2021. Participants will also post self-administered nasal swabs for PCR assays of SARS-CoV-2 and will follow one of three different PCR testing schedules based upon symptoms. Ethics and dissemination This study has been approved by the Hampstead NHS Health Research Authority Ethics Committee. Ethics approval number – 20/HRA/2320. We are monitoring participant queries and using these to refine methodology where necessary, and are providing summaries and policy briefings of our preliminary findings to inform public health action by working through our partnerships with our study advisory group, Public Health England, NHS and Government Scientific Advisory panels. Strengths and limitations of this study Virus Watch is a large national household community cohort study of the occurrence and risk factors for COVID-19 infection that aims to recruit 50,000 people, including 12,500 from minority ethnic backgrounds. Virus Watch is designed to estimate incidence of PCR confirmed COVID-19 in those with respiratory and non-respiratory presentations and the incidence of hospitalisation among PCR confirmed COVID-19 cases. Virus Watch will measure effectiveness and impact of recommended COVID-19 control measures including testing, isolation, social distancing, respiratory and hand hygiene measures on risk of respiratory infection. Only households with a lead householder able to speak English were able to take part in the study up until March 2021. Only households of up to six people were eligible for inclusion and they were also required to have access to an internet connection. These restrictions will limit the generalisability to large or multigenerational households, and those without access to the internet.

Modeling the Opening SARS-CoV-2 Spike: an Investigation of its Dynamic Electro-Geometric Properties (preprint)

The recent COVID-19 pandemic has brought about a surge of crowd-sourced initiatives aimed at simulating the proteins of the SARS-CoV-2 virus. A bottleneck currently exists in translating these simulations into tangible predictions that can be leveraged for pharmacological studies. Here we report on extensive electrostatic calculations done on an exascale simulation of the opening of the SARS-CoV-2 spike protein, performed by the Folding@home initiative. We compute the electric potential as the solution of the non-linear Poisson-Boltzmann equation using a parallel sharp numerical solver. The inherent multiple length scales present in the geometry and solution are reproduced using highly adaptive Octree grids. We analyze our results focusing on the electro-geometric properties of the receptor-binding domain and its vicinity. This work paves the way for a new class of hybrid computational and data-enabled approaches, where molecular dynamics simulations are combined with continuum modeling to produce high-fidelity computational measurements serving as a basis for protein bio-mechanism investigations.

Large-scale single-cell analysis reveals critical immune characteristics of COVID-19 patients (preprint)

Dysfunctional immune response in the COVID-19 patients is a recurrent theme impacting symptoms and mortality, yet the detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 205 COVID-19 patients and controls to create a comprehensive immune landscape. Lymphopenia and active T and B cell responses were found to coexist and associated with age, sex and their interactions with COVID-19. Diverse epithelial and immune cell types were observed to be virus-positive and showed dramatic transcriptomic changes. Elevation of ANXA1 and S100A9 in virus-positive squamous epithelial cells may enable the initiation of neutrophil and macrophage responses via the ANXA1-FPR1 and S100A8/9-TLR4 axes. Systemic up-regulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis and designing effective therapeutic strategies for COVID-19.

Dynamic Changes of Chest CT Follow-up In Coronavirus Disease-19 (COVID-19) Pneumonia: Relationship To Clinical Typing (preprint)

Background: To investigate the CT changes of different clinical types of COVID-19 pneumonia. Methods: : This retrospective study included 50 patients with COVID-19 from 16 January 2020 to 25 February 2020. We analyzed the clinical characteristics, CT characteristics and the pneumonia involvement of the patients between the moderate group and the severe and critical group, and the dynamic changes of severity with the CT follow-up time. Results: : There were differences in the CT severity score of the right lung in the initial CT, and total CT severity score in the initial and follow-up CT between the moderate group and the severe and critical group (all p <0.05). There was a quadratic relationship between total CT severity score and CT follow-up time in the severe and critical group (r 2 =0.137, p=0.008), the total CT severity score peaked at the second follow-up CT. There was no correlation between total CT severity score and CT follow-up time in the moderate group (p >0.05). There were no differences in the occurrence rate of CT characteristics in the initial CT between the two groups (all p >0.05). There were differences in the occurrence rate of ground-glass opacity and crazy-paving pattern in the second follow-up CT, and pleural thickening or adhesion in the third follow-up CT between the two groups (all p <0.05). Conclusions: : The CT changes of COVID-19 pneumonia with different severity were different, and the extent of pneumonia involvement by CT can help to assess the severity of COVID-19 pneumonia rather than the initial CT characteristics.

Variational-LSTM Autoencoder to forecast the spread of coronavirus across the globe (preprint)

Modelling the spread of coronavirus globally while learning trends at global and country levels remains crucial for tackling the pandemic. We introduce a novel variational LSTM-Autoencoder model to predict the spread of coronavirus for each country across the globe. This deep spatio-temporal model does not only rely on historical data of the virus spread but also includes factors related to urban characteristics represented in locational and demographic data (such as population density, urban population, and fertility rate), an index that represent the governmental measures and response amid toward mitigating the outbreak (includes 13 measures such as: 1) school closing, 2) workplace closing, 3) cancelling public events, 4) close public transport, 5) public information campaigns, 6) restrictions on internal movements, 7) international travel controls, 8) fiscal measures, 9) monetary measures, 10) emergency investment in health care, 11) investment in vaccines, 12) virus testing framework, and 13) contact tracing). In addition, the introduced method learns to generate graph to adjust the spatial dependences among different countries while forecasting the spread. We trained two models for short and long-term forecasts. The first one is trained to output one step in future with three previous timestamps of all features across the globe, whereas the second model is trained to output 10 steps in future. Overall, the trained models show high validation for forecasting the spread for each country for short and long-term forecasts, which makes the introduce method a useful tool to assist decision and policymaking for the different corners of the globe.

Dynamic Changes of Chest CT Follow-up In Coronavirus Disease-19 (COVID-19) Pneumonia: Relationship To Clinical Typing (preprint)

ObjectivesTo investigate the  CT changes of  different clinical  types  of  COVID-19 pneumonia.MethodsThis retrospective study included 50 confirmed patients with COVID-19 from 16 January 2020 to 25 February 2020. We analyzed the clinical and CT characteristics of the patients between the moderate group and the severe and critical group, and the dynamic changes of severity with the CT follow-up time.ResultsThere were no differences in the occurrence rate of CT characteristics between the moderate group (n=34) and the severe and critical group (n=16) in the initial CT (all p >0.05). There were differences in the CT score of right lung and total CT score at the initial CT between the two groups (all p <0.05). There was a quadratic relationship between total CT score and CT follow-up time in the severe and critical group (r2=0.137, p=0.008), the total CT severity score peaked at the second follow-up CT. There was no correlation between total CT score and CT follow-up time in the moderate group (p >0.05). The total CT score of the severe and critical group was different between the initial and first follow-up, the second and third follow-ups, the third and fourth follow-ups, and the fourth and fifth follow-ups CT (all p<0.05). The total CT score of the moderate group was different between the second and third follow-ups CT (p<0.05).ConclusionsCOVID-19 pneumonia with the severe and critical types progressed rapidly with the greatest severity at the second follow-up CT, and the moderate type was relatively stable.