Tracking the Structure and Sentiment of Vaccination Discussions on Mumsnet (preprint)

Vaccination is one of the most impactful healthcare interventions in terms of lives saved at a given cost, leading the anti-vaccination movement to be identified as one of the top 10 threats to global health in 2019 by the World Health Organization. This issue increased in importance during the COVID-19 pandemic where, despite good overall adherence to vaccination, specific communities still showed high rates of refusal. Online social media has been identified as a breeding ground for anti-vaccination discussions. In this work, we study how vaccination discussions are conducted in the discussion forum of Mumsnet, a United Kingdom based website aimed at parents. By representing vaccination discussions as networks of social interactions, we can apply techniques from network analysis to characterize these discussions, namely network comparison, a task aimed at quantifying similarities and differences between networks. Using network comparison based on graphlets -- small connected network subgraphs -- we show how the topological structure vaccination discussions on Mumsnet differs over time, in particular before and after COVID-19. We also perform sentiment analysis on the content of the discussions and show how the sentiment towards vaccinations changes over time. Our results highlight an association between differences in network structure and changes to sentiment, demonstrating how network comparison can be used as a tool to guide and enhance the conclusions from sentiment analysis.

The role of regular asymptomatic testing in reducing the impact of a COVID-19 wave (preprint)

Testing for infection with SARS-CoV-2 is an important intervention in reducing onwards transmission of COVID-19, particularly when combined with the isolation and contact-tracing of positive cases. Many countries with the capacity to do so have made use of lab-processed Polymerase Chain Reaction (PCR) testing targeted at individuals with symptoms and the contacts of confirmed cases. Alternatively, Lateral Flow Tests (LFTs) are able to deliver a result quickly, without lab-processing and at a relatively low cost. Their adoption can support regular mass asymptomatic testing, allowing earlier detection of infection and isolation of infectious individuals. In this paper we extend and apply the agent-based epidemic modelling framework Covasim to explore the impact of regular asymptomatic testing on the peak and total number of infections in an emerging COVID-19 wave. We explore testing with LFTs at different frequency levels within a population with high levels of immunity and with background symptomatic PCR testing, case isolation and contact tracing for testing. The effectiveness of regular asymptomatic testing was compared with `lockdown' interventions seeking to reduce the number of non-household contacts across the whole population through measures such as mandating working from home and restrictions on gatherings. Since regular asymptomatic testing requires only those with a positive result to reduce contact, while lockdown measures require the whole population to reduce contact, any policy decision that seeks to trade off harms from infection against other harms will not automatically favour one over the other. Our results demonstrate that, where such a trade off is being made, at moderate rates of early exponential growth regular asymptomatic testing has the potential to achieve significant infection control without the wider harms associated with additional lockdown measures.

COVID-19: An exploration of consecutive systemic barriers to pathogen-related data sharing during a pandemic (preprint)

In 2020, the COVID-19 pandemic resulted in a rapid response from governments and researchers worldwide. As of late 2023, over millions have died as a result of COVID-19, with many COVID-19 survivors going on to experience long-term effects weeks, months, or years after their illness. Despite this staggering toll, those who work with pandemic-relevant data often face significant systemic barriers to accessing, sharing or re-using this data. In this paper we report results of a study, where we interviewed data professionals working with COVID-19-relevant data types including social media, mobility, viral genome, testing, infection, hospital admission, and deaths. These data types are variously used for pandemic spread modelling, healthcare system strain awareness, and devising therapeutic treatments for COVID-19. Barriers to data access, sharing and re-use include the cost of access to data (primarily certain healthcare sources and mobility data from mobile phone carriers), human throughput bottlenecks, unclear pathways to request access to data, unnecessarily strict access controls and data re-use policies, unclear data provenance, inability to link separate data sources that could collectively create a more complete picture, poor adherence to metadata standards, and a lack of computer-suitable data formats.

Public perceptions and interactions with UK COVID-19 Test, Trace and Isolate policies, and implications for pandemic infectious disease modelling (preprint)

The efforts to contain SARS-CoV-2 and reduce the impact of COVID-19 have been supported by Test, Trace and Isolate (TTI) systems in many settings, including the United Kingdom. The mathematical models underlying policy decisions about TTI make assumptions about behaviour in the context of a rapidly unfolding and changeable emergency. This study investigates the reported behaviours of UK citizens in July 2021, assesses them against how a set of TTI processes are conceptualised and represented in models and then interprets the findings with modellers who have been contributing evidence to TTI policy. We report on testing practices, including the uses of and trust in different types of testing, and the challenges of testing and isolating faced by different demographic groups. The study demonstrates the potential of input from members of the public to benefit the modelling process, from guiding the choice of research questions, influencing choice of model structure, informing parameter ranges and validating or challenging assumptions, to highlighting where model assumptions are reasonable or where their poor reflection of practice might lead to uninformative results. We conclude that deeper engagement with members of the public should be integrated at regular stages of public health intervention modelling.

Diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets (preprint)

Through the use of cutting-edge unsupervised classification techniques from statistics and machine learning, we characterise symptom phenotypes among symptomatic SARS-CoV-2 PCR-positive community cases. We first analyse each dataset in isolation and across age bands, before using methods that allow us to compare multiple datasets. While we observe separation due to the total number of symptoms experienced by cases, we also see a separation of symptoms into gastrointestinal, respiratory and other types, and different symptom co-occurrence patterns at the extremes of age. In this way, we are able to demonstrate the deep structure of symptoms of COVID-19 without usual biases due to study design. This is expected to have implications for the identification and management of community SARS-CoV-2 cases and could be further applied to symptom-based management of other diseases and syndromes.

The impact of COVID-19 on digital data practices in museums and art galleries in the UK and the US (preprint)

In the first quarter of 2020, the doors of museums around the world shut and their operations at physical sites were reduced in line with necessary security measures. This heralded the beginning of an uncertain future for museums and galleries as the COVID-19 pandemic hit and the only means to stay ‘open’ was to turn towards the digital. In this paper, we investigate how the physical closure of museum buildings due to lockdown restrictions caused shockwaves within their digital strategies and changed their data practices potentially for good. The methodology of the research involves a review of the impact of COVID-19 on the museum sector, based on literature and desk research, with a focus on the implications for three museums and art galleries in the United Kingdom and the United States, and their mission, objectives, and digital data practices. We also present analysis of ten qualitative interviews with expert witnesses working in the sector, representing different roles and types of institutions, undertaken between April and October 2020. Our research finds that digital engagement with museum content and practices around data in institutions have changed and that digital methods for organising and accessing collections for both staff and the general public have become more important. We present evidence that strategic preparedness influenced how well institutions were able to transition during closure and that metrics data became pivotal in understanding this novel situation. Increased engagement online changed traditional audience profiles, challenging museums to find ways of accommodating new forms of engagement in order to survive and thrive in the post-pandemic environment. Our findings point to a longer term shift in the operating models for museums and the need to realise economic value and diversify income streams through digital means, which have not yet been clearly established. The research suggests that the unprecedented situation brought on by the pandemic will shape future museum audiences and their interactions with institutions virtually and physically, posing challenges to museums and their constituents that require structural changes and adaptations, but also present opportunities to successfully survive in an ever-more connected world.