Modelling the impact of non-pharmaceutical interventions on workplace transmission of SARS-CoV-2 in the home-delivery sector.

OBJECTIVE: We aimed to use mathematical models of SARS-COV-2 to assess the potential efficacy of non-pharmaceutical interventions on transmission in the parcel delivery and logistics sector. METHODS: We devloped a network-based model of workplace contacts based on data and consultations from companies in the parcel delivery and logistics sectors. We used these in stochastic simulations of disease transmission to predict the probability of workplace outbreaks in this settings. Individuals in the model have different viral load trajectories based on SARS-CoV-2 in-host dynamics, which couple to their infectiousness and test positive probability over time, in order to determine the impact of testing and isolation measures. RESULTS: The baseline model (without any interventions) showed different workplace infection rates for staff in different job roles. Based on our assumptions of contact patterns in the parcel delivery work setting we found that when a delivery driver was the index case, on average they infect only 0.14 other employees, while for warehouse and office workers this went up to 0.65 and 2.24 respectively. In the LIDD setting this was predicted to be 1.40, 0.98, and 1.34 respectively. Nonetheless, the vast majority of simulations resulted in 0 secondary cases among customers (even without contact-free delivery). Our results showed that a combination of social distancing, office staff working from home, and fixed driver pairings (all interventions carried out by the companies we consulted) reduce the risk of workplace outbreaks by 3-4 times. CONCLUSION: This work suggests that, without interventions, significant transmission could have occured in these workplaces, but that these posed minimal risk to customers. We found that identifying and isolating regular close-contacts of infectious individuals (i.e. house-share, carpools, or delivery pairs) is an efficient measure for stopping workplace outbreaks. Regular testing can make these isolation measures even more effective but also increases the number of staff isolating at one time. It is therefore more efficient to use these isolation measures in addition to social distancing and contact reduction interventions, rather than instead of, as these reduce both transmission and the number of people needing to isolate at one time.

Modelling the impact of repeat asymptomatic testing policies for staff on SARS-CoV-2 transmission potential.

Repeat asymptomatic testing in order to identify and quarantine infectious individuals has become a widely-used intervention to control SARS-CoV-2 transmission. In some workplaces, and in particular health and social care settings with vulnerable patients, regular asymptomatic testing has been deployed to staff to reduce the likelihood of workplace outbreaks. We have developed a model based on data available in the literature to predict the potential impact of repeat asymptomatic testing on SARS-CoV-2 transmission. The results highlight features that are important to consider when modelling testing interventions, including population heterogeneity of infectiousness and correlation with test-positive probability, as well as adherence behaviours in response to policy. Furthermore, the model based on the reduction in transmission potential presented here can be used to parameterise existing epidemiological models without them having to explicitly simulate the testing process. Overall, we find that even with different model paramterisations, in theory, regular asymptomatic testing is likely to be a highly effective measure to reduce transmission in workplaces, subject to adherence. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Contact patterns of UK home delivery drivers and their use of protective measures during the COVID-19 pandemic: a cross-sectional study.

OBJECTIVES: To quantify contact patterns of UK home delivery drivers and identify protective measures adopted during the pandemic. METHODS: We conducted a cross-sectional online survey to measure the interactions of 170 UK delivery drivers during a working shift between 7 December 2020 and 31 March 2021. RESULTS: Delivery drivers had a mean number of 71.6 (95% CI 61.0 to 84.1) customer contacts per shift and 15.0 (95% CI 11.2 to 19.2) depot contacts per shift. Maintaining physical distancing with customers was more common than at delivery depots. Prolonged contact (more than 5 min) with customers was reported by 5.4% of drivers on their last shift. We found 3.0% of drivers had tested positive for SARS-CoV-2 since the start of the pandemic and 16.8% of drivers had self-isolated due to a suspected or confirmed case of COVID-19. In addition, 5.3% (95% CI 2.3% to 10.2%) of participants reported having worked while ill with COVID-19 symptoms, or with a member of their household having a suspected or confirmed case of COVID-19. CONCLUSION: Delivery drivers had a large number of face-to-face customer and depot contacts per shift compared with other working adults during this time. However, transmission risk may be curtailed as contact with customers was of short duration. Most drivers were unable to maintain physical distance with customers and at depots at all times. Usage of protective items such as face masks and hand sanitiser was widespread.

Estimation of reproduction numbers in real time: Conceptual and statistical challenges.

The reproduction number R has been a central metric of the COVID-19 pandemic response, published weekly by the UK government and regularly reported in the media. Here, we provide a formal definition and discuss the advantages and most common misconceptions around this quantity. We consider the intuition behind different formulations of R , the complexities in its estimation (including the unavoidable lags involved), and its value compared to other indicators (e.g. the growth rate) that can be directly observed from aggregate surveillance data and react more promptly to changes in epidemic trend. As models become more sophisticated, with age and/or spatial structure, formulating R becomes increasingly complicated and inevitably model-dependent. We present some models currently used in the UK pandemic response as examples. Ultimately, limitations in the available data streams, data quality and time constraints force pragmatic choices to be made on a quantity that is an average across time, space, social structure and settings. Effectively communicating these challenges is important but often difficult in an emergency.

Simplified within-host and Dose-response Models of SARS-CoV-2.

Understanding the mechanistic dynamics of transmission is key to designing more targeted and effective interventions to limit the spread of infectious diseases. A well-described within-host model allows explicit simulation of how infectiousness changes over time at an individual level. This can then be coupled with dose-response models to investigate the impact of timing on transmission. We collected and compared a range of within-host models used in previous studies and identified a minimally-complex model that provides suitable within-host dynamics while keeping a reduced number of parameters to allow inference and limit unidentifiability issues. Furthermore, non-dimensionalised models were developed to further overcome the uncertainty in estimates of the size of the susceptible cell population, a common problem in many of these approaches. We will discuss these models, and their fit to data from the human challenge study (see Killingley et al. (2022)) for SARS-CoV-2 and the model selection results, which has been performed using ABC-SMC. The parameter posteriors have then used to simulate viral-load based infectiousness profiles via a range of dose-response models, which illustrate the large variability of the periods of infection window observed for COVID-19.

Evaluation of outpatient treatment for non-hospitalised patients with COVID-19: The experience of a regional centre in the UK.

INTRODUCTION: Antivirals, such as molnupiravir, and SARS-CoV-2 neutralising monoclonal antibodies (nMAbs), such as sotrovimab, reduced the risk of hospitalisation and death in clinical trials of high-risk non-hospitalised patients with Covid-19. However, the real-world benefits of these drugs are unclear. AIMS: To evaluate the characteristics and outcomes of high-risk patients referred for outpatient antiviral or nMAb treatment for symptomatic Covid-19. METHODS: The records of patients referred to a large UK Covid Medicines Delivery Unit (CMDU) over nine weeks (December 2021-February 2022) were reviewed. Data were collected on demographics, referral indications, vaccination, deprivation, treatment, complications, hospital admission, and mortality. RESULTS: 1820 patients were referred to the CMDU, with 604 (33.2%) suitable for further assessment. 169 patients received sotrovimab, 80 patients received molnupiravir, 70 patients declined treatment, and 266 were ineligible for treatment because of resolving symptoms. There were trends towards higher proportions of female and white patients, lower deprivation scores, and malignancy- or transplant-related indications in the groups receiving treatment compared with untreated patients. Covid-19-related hospitalisations occurred in 1.2% of the treated group and 3.0% of the untreated group indicating a potential treatment effect, however Covid-related hospitalisations were lower than reported in the original clinical trials (2.2% compared with 7-10%). CONCLUSION: The referral pathways for outpatient treatment of Covid-19 are inefficient, and the UK system may not be serving all groups equitably. Hospitalisation with Covid-19 was rare regardless of treatment. Ongoing service evaluation is required to ensure efficient use of resources for the outpatient management of Covid-19.

Residual Lung Abnormalities Following COVID-19 Hospitalization: Interim Analysis of the UKILD Post-COVID Study.

RATIONALE: Shared symptoms and genetic architecture between COVID-19 and lung fibrosis suggests SARS-CoV-2 infection may lead to progressive lung damage. OBJECTIVES: The UKILD Post-COVID study interim analysis was planned to estimate the prevalence of residual lung abnormalities in people hospitalized with COVID-19 based on risk strata. METHODS: The Post-HOSPitalisation COVID Study (PHOSP-COVID) was used for capture of routine and research follow-up within 240 days from discharge. Thoracic CTs linked by PHOSP-COVID identifiers were scored for percentage of residual lung abnormalities (ground glass opacities and reticulations). Risk factors in linked CT were estimated with Bayesian binomial regression and risk strata were generated. Numbers within strata were used to estimate post-hospitalization prevalence using Bayesian binomial distributions. Sensitivity analysis was restricted to participants with protocol driven research follow-up. MEASUREMENTS AND MAIN RESULTS: The interim cohort comprised 3700 people. Of 209 subjects with linked CTs (median 119 days, interquartile range 83-155), 166 people (79.4%) had >10% involvement of residual lung abnormalities. Risk factors included abnormal chest X-ray (RR 1·21 95%CrI 1·05; 1·40), percent predicted DLco<80% (RR 1·25 95%CrI 1·00; 1·56) and severe admission requiring ventilation support (RR 1·27 95%CrI 1·07; 1·55). In the remaining 3491 people, moderate to very-high risk of residual lung abnormalities was classified in 7·8%, post-hospitalization prevalence was estimated at 8.5% (95%CrI 7.6%; 9.5%) rising to 11.7% (95%CrI 10.3%; 13.1%) in sensitivity analysis. CONCLUSIONS: Residual lung abnormalities were estimated in up to 11% of people discharged following COVID-19 related hospitalization. Health services should monitor at-risk individuals to elucidate long-term functional implications. This article is open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

Novel methods for estimating the instantaneous and overall COVID-19 case fatality risk among care home residents in England.

The COVID-19 pandemic has had high mortality rates in the elderly and frail worldwide, particularly in care homes. This is driven by the difficulty of isolating care homes from the wider community, the large population sizes within care facilities (relative to typical households), and the age/frailty of the residents. To quantify the mortality risk posed by disease, the case fatality risk (CFR) is an important tool. This quantifies the proportion of cases that result in death. Throughout the pandemic, CFR amongst care home residents in England has been monitored closely. To estimate CFR, we apply both novel and existing methods to data on deaths in care homes, collected by Public Health England and the Care Quality Commission. We compare these different methods, evaluating their relative strengths and weaknesses. Using these methods, we estimate temporal trends in the instantaneous CFR (at both daily and weekly resolutions) and the overall CFR across the whole of England, and dis-aggregated at regional level. We also investigate how the CFR varies based on age and on the type of care required, dis-aggregating by whether care homes include nursing staff and by age of residents. This work has contributed to the summary of measures used for monitoring the UK epidemic.

EpiBeds: Data informed modelling of the COVID-19 hospital burden in England.

The first year of the COVID-19 pandemic put considerable strain on healthcare systems worldwide. In order to predict the effect of the local epidemic on hospital capacity in England, we used a variety of data streams to inform the construction and parameterisation of a hospital progression model, EpiBeds, which was coupled to a model of the generalised epidemic. In this model, individuals progress through different pathways (e.g. may recover, die, or progress to intensive care and recover or die) and data from a partially complete patient-pathway line-list was used to provide initial estimates of the mean duration that individuals spend in the different hospital compartments. We then fitted EpiBeds using complete data on hospital occupancy and hospital deaths, enabling estimation of the proportion of individuals that follow the different clinical pathways, the reproduction number of the generalised epidemic, and to make short-term predictions of hospital bed demand. The construction of EpiBeds makes it straightforward to adapt to different patient pathways and settings beyond England. As part of the UK response to the pandemic, EpiBeds provided weekly forecasts to the NHS for hospital bed occupancy and admissions in England, Wales, Scotland, and Northern Ireland at national and regional scales.

Agility and Sustainability: A Qualitative Evaluation of COVID-19 Non-pharmaceutical Interventions in the UK Logistics Sector.

Background: The emergence of SARS-CoV-2 triggered a chain of public health responses that radically changed our way of living and working. Non-healthcare sectors, such as the logistics sector, play a key role in such responses. This research aims to qualitatively evaluate the non-pharmaceutical interventions (NPIs) implemented in the UK logistics sector during the COVID-19 pandemic. Methods: We conducted nine semi-structured interviews in July-August 2020 and May-June 2021. In total 11 interviewees represented six companies occupying a range of positions in the UK's logistics sector, including takeaway food delivery, large and small goods delivery and home appliance installation, and logistics technology providers. Thematic analysis was completed using NVivo12. Codes relevant to NPIs were grouped into themes and mapped deductively onto an adapted Hierarchy of Control (HoC) framework, focusing on delivery workers. Codes relevant to the implementation process of NPIs were grouped into themes/subthemes to identify key characteristics of rapid responses, and barriers and facilitators. Results: HoC analysis suggests the sector has implemented a wide range of risk mitigation measures, with each company developing their own portfolio of measures. Contact-free delivery was the most commonly implemented measure and perceived effective. The other implemented measures included social distancing, internal contact tracing, communication and collaboration with other key stakeholders of the sector. Process evaluation identified facilitators of rapid responses including capacity to develop interventions internally, localized government support, strong external mandates, effective communication, leadership support and financial support for self-isolation, while barriers included unclear government guidance, shortage of testing capacity and supply, high costs and diversified language and cultural backgrounds. Main sustainability issues included compliance fatigue, and the possible mental health impacts of a prolonged rapid response. Conclusions: This research identified drivers and obstacles of rapid implementation of NPIs in response to a respiratory infection pandemic. Existing implementation process models do not consider speed to respond and the absence or lack of guidance in emergency situations such as the COVID-19. We recommend the development of a rapid response model to inform the design of effective and sustainable infection prevention and control policies and to focus future research priorities.

Effectiveness of the BNT162b2 (Pfizer-BioNTech) and the ChAdOx1 nCoV-19 (Oxford-AstraZeneca) vaccines for reducing susceptibility to infection with the Delta variant (B.1.617.2) of SARS-CoV-2.

BACKGROUND: From January to May 2021 the alpha variant (B.1.1.7) of SARS-CoV-2 was the most commonly detected variant in the UK. Following this, the Delta variant (B.1.617.2) then became the predominant variant. The UK COVID-19 vaccination programme started on 8th December 2020. Prior to the Delta variant, most vaccine effectiveness studies focused on the alpha variant. We therefore aimed to estimate the effectiveness of the BNT162b2 (Pfizer-BioNTech) and the ChAdOx1 nCoV-19 (Oxford-AstraZeneca) vaccines in preventing symptomatic and asymptomatic infection with respect to the Delta variant in a UK setting. METHODS: We used anonymised public health record data linked to infection data (PCR) using the Combined Intelligence for Population Health Action resource. We then constructed an SIR epidemic model to explain SARS-CoV-2 infection data across the Cheshire and Merseyside region of the UK. Vaccines were assumed to be effective after 21 days for 1 dose and 14 days for 2 doses. RESULTS: We determined that the effectiveness of the Oxford-AstraZeneca vaccine in reducing susceptibility to infection is 39% (95% credible interval [34, 43]) and 64% (95% credible interval [61, 67]) for a single dose and a double dose respectively. For the Pfizer-BioNTech vaccine, the effectiveness is 20% (95% credible interval [10, 28]) and 84% (95% credible interval [82, 86]) for a single-dose and a double dose respectively. CONCLUSION: Vaccine effectiveness for reducing susceptibility to SARS-CoV-2 infection shows noticeable improvement after receiving two doses of either vaccine. Findings also suggest that a full course of the Pfizer-BioNTech provides the optimal protection against infection with the Delta variant. This reinforces the need to complete the full course programme to maximise individual protection and reduce transmission.

Epidemiological modelling in refugee and internally displaced people settlements: challenges and ways forward.

The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world's most vulnerable populations at risk. Epidemiological modelling is vital to guiding evidence-informed or data-driven decision making. In forced displacement contexts, and in particular refugee and internally displaced people (IDP) settlements, it meets several challenges including data availability and quality, the applicability of existing models to those contexts, the accurate modelling of cultural differences or specificities of those operational settings, the communication of results and uncertainties, as well as the alignment of strategic goals between diverse partners in complex situations. In this paper, we systematically review the limited epidemiological modelling work applied to refugee and IDP settlements so far, and discuss challenges and identify lessons learnt from the process. With the likelihood of disease outbreaks expected to increase in the future as more people are displaced due to conflict and climate change, we call for the development of more approaches and models specifically designed to include the unique features and populations of refugee and IDP settlements. To strengthen collaboration between the modelling and the humanitarian public health communities, we propose a roadmap to encourage the development of systems and frameworks to share needs, build tools and coordinate responses in an efficient and scalable manner, both for this pandemic and for future outbreaks.

Estimating the potential for global dissemination of pandemic pathogens using the global airline network and healthcare development indices.

Pandemics have the potential to incur significant health and economic impacts, and can reach a large number of countries from their origin within weeks. Early identification and containment of a newly emerged pandemic within the source country is key for minimising global impact. To identify a country's potential to control and contain a pathogen with pandemic potential, we compared the quality of a country's healthcare system against its global airline connectivity. Healthcare development was determined using three multi-factorial indices, while detailed airline passenger data was used to identify the global connectivity of all countries. Proximities of countries to a putative 'Worst Case Scenario' (extreme high-connectivity and low-healthcare development) were calculated. We found a positive relationship between a country's connectivity and healthcare metrics. We also identified countries that potentially pose the greatest risk for pandemic dissemination, notably Dominican Republic, India and Pakistan. China and Mexico, both sources of recent influenza and coronavirus pandemics were also identified as among the highest risk countries. Collectively, lower-middle and upper-middle income countries represented the greatest risk, while high income countries represented the lowest risk. Our analysis represents an alternative approach to identify countries where increased within-country disease surveillance and pandemic preparedness may benefit global health.

Modeling the factors that influence exposure to SARS-CoV-2 on a subway train carriage.

We propose the Transmission of Virus in Carriages (TVC) model, a computational model which simulates the potential exposure to SARS-CoV-2 for passengers traveling in a subway rail system train. This model considers exposure through three different routes: fomites via contact with contaminated surfaces; close-range exposure, which accounts for aerosol and droplet transmission within 2 m of the infectious source; and airborne exposure via small aerosols which does not rely on being within 2 m distance from the infectious source. Simulations are based on typical subway parameters and the aim of the study is to consider the relative effect of environmental and behavioral factors including prevalence of the virus in the population, number of people traveling, ventilation rate, and mask wearing as well as the effect of model assumptions such as emission rates. Results simulate generally low exposures in most of the scenarios considered, especially under low virus prevalence. Social distancing through reduced loading and high mask-wearing adherence is predicted to have a noticeable effect on reducing exposure through all routes. The highest predicted doses happen through close-range exposure, while the fomite route cannot be neglected; exposure through both routes relies on infrequent events involving relatively few individuals. Simulated exposure through the airborne route is more homogeneous across passengers, but is generally lower due to the typically short duration of the trips, mask wearing, and the high ventilation rate within the carriage. The infection risk resulting from exposure is challenging to estimate as it will be influenced by factors such as virus variant and vaccination rates.

Patient-related outcomes in patients referred to a respiratory clinic with persisting symptoms following non-hospitalised COVID-19.

Survivors of COVID-19 can present with varied and persisting symptoms, regardless of hospitalisation. We describe the ongoing symptoms, quality of life and return to work status in a cohort of non-hospitalised COVID-19 survivors with persisting respiratory symptoms presenting to clinic, who consented and completed patient-reported outcome measures. We identified fatigue, reduced quality of life and dysregulated breathing alongside the breathlessness. Those with co-existent fatigue had worse mood and quality of life and were less likely to have returned to normal working arrangements compared to those without fatigue. For non-hospitalised people with persisting symptoms following COVID-19 referred to a respiratory assessment clinic, there was a need for a wider holistic assessment, including return to work strategies.

Growth, reproduction numbers and factors affecting the spread of SARS-CoV-2 novel variants of concern in the UK from October 2020 to July 2021: a modelling analysis.

OBJECTIVES: Importations of novel variants of concern (VOC), particularly B.1.617.2, have become the impetus behind recent outbreaks of SARS-CoV-2. Concerns around the impact on vaccine effectiveness, transmissibility and severity are now driving the public health response to these variants. This paper analyses the patterns of growth in hospitalisations and confirmed cases for novel VOCs by age groups, geography and ethnicity in the context of changing behaviour, non-pharmaceutical interventions (NPIs) and the UK vaccination programme. We seek to highlight where strategies have been effective and periods that have facilitated the establishment of new variants. DESIGN: We have algorithmically linked the most complete testing and hospitalisation data in England to create a data set of confirmed infections and hospitalisations by SARS-CoV-2 genomic variant. We have used these linked data sets to analyse temporal, geographic and demographic distinctions. SETTING AND PARTICIPANTS: The setting is England from October 2020 to July 2021. Participants included all COVID-19 tests that included RT-PCR CT gene target data or underwent sequencing and hospitalisations that could be linked to these tests. METHODS: To calculate the instantaneous growth rate for VOCs we have developed a generalised additive model fit to multiple splines and varying day of the week effects. We have further modelled the instantaneous reproduction number Rt for the B.1.1.7 and B.1.617.2 variants and included a doubly interval censored model to temporally adjust the confirmed variant cases. RESULTS: We observed a clear replacement of the predominant B.1.1.7 by the B.1.617.2 variant without observing sustained exponential growth in other novel variants. Modelled exponential growth of RT PCR gene target triple-positive cases was initially detected in the youngest age groups, although we now observe across all ages a very small doubling time of 10.7 (95% CI 9.1 to 13.2) days and 8 (95% CI 6.9 to 9.1) days for cases and hospitalisations, respectively. We observe that growth in RT PCR gene target triple-positive cases was first detected in the Indian ethnicity group in late February, with a peak of 0.06 (95% CI 0.07 to 0.05) in the instantaneous growth rate, but is now maintained by the white ethnicity groups, observing a doubling time of 6.8 (95% CI 4.9 to 11) days. Rt analysis indicates a reproduction number advantage of 0.45 for B.1.617.2 relative to B.1.1.7, with the Rt value peaking at 1.85 for B.1.617.2. CONCLUSIONS: Our results illustrate a clear transmission advantage for the B.1.617.2 variant and the growth in hospitalisations illustrates that this variant is able to maintain exponential growth within age groups that are largely doubly vaccinated. There are concerning signs of intermittent growth in the B.1.351 variant, reaching a 28-day doubling time peak in March 2021, although this variant is presently not showing any evidence of a transmission advantage over B.1.617.2. Step 1b of the UK national lockdown easing was sufficient to precipitate exponential growth in B.1.617.2 cases for most regions and younger adult age groups. The final stages of NPI easing appeared to have a negligible impact on the growth of B.1.617.2 with every region experiencing sustained exponential growth from step 2. Nonetheless, early targeted local NPIs appeared to markedly reduced growth of B.1.617.2. Later localised interventions, at a time of higher prevalence and greater geographic dispersion of this variant, appeared to have a negligible impact on growth.

Risk factors associated with respiratory infectious disease-related presenteeism: a rapid review.

BACKGROUND: Workplace transmission is a significant contributor to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks. Previous studies have found that infectious illness presenteeism could contribute to outbreaks in occupational settings and identified multiple occupational and organisational risk factors. Amid the COVID-19 pandemic, it is imperative to investigate presenteeism particularly in relation to respiratory infectious disease (RID). Hence, this rapid review aims to determine the prevalence of RID-related presenteeism, including COVID-19, and examines the reported reasons and associated risk factors. METHODS: The review followed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) search approach and focused on studies published in English and Chinese. Database searches included MEDLINE, EMBASE, Web of Science, China Knowledge Resource Integrated Database (CNKI) and preprint databases MedRxiv and BioRxiv. RESULTS: The search yielded 54 studies, of which four investigated COVID-19-related presenteeism. Prevalence of work presenteeism ranged from 14.1 to 55% for confirmed RID, and 6.6 to 100% for those working with suspected or subclinical RID. The included studies demonstrated that RID-related presenteeism is associated with occupation, sick pay policy, age, gender, health behaviour and perception, vaccination, peer pressure and organisational factors such as presenteeism culture. CONCLUSIONS: This review demonstrates that presenteeism or non-adherence to isolation guidance is a real concern and can contribute to workplace transmissions and outbreaks. Policies which would support workers financially and improve productivity, should include a range of effective non-pharmaceutical inventions such as workplace testing, promoting occupational health services, reviewing pay and bonus schemes and clear messaging to encourage workers to stay at home when ill. Future research should focus on the more vulnerable and precarious occupational groups, and their inter-relationships, to develop comprehensive intervention programs to reduce RID-related presenteeism.

Understanding the burden of interstitial lung disease post-COVID-19: the UK Interstitial Lung Disease-Long COVID Study (UKILD-Long COVID).

INTRODUCTION: The COVID-19 pandemic has led to over 100 million cases worldwide. The UK has had over 4 million cases, 400 000 hospital admissions and 100 000 deaths. Many patients with COVID-19 suffer long-term symptoms, predominantly breathlessness and fatigue whether hospitalised or not. Early data suggest potentially severe long-term consequence of COVID-19 is development of long COVID-19-related interstitial lung disease (LC-ILD). METHODS AND ANALYSIS: The UK Interstitial Lung Disease Consortium (UKILD) will undertake longitudinal observational studies of patients with suspected ILD following COVID-19. The primary objective is to determine ILD prevalence at 12 months following infection and whether clinically severe infection correlates with severity of ILD. Secondary objectives will determine the clinical, genetic, epigenetic and biochemical factors that determine the trajectory of recovery or progression of ILD. Data will be obtained through linkage to the Post-Hospitalisation COVID platform study and community studies. Additional substudies will conduct deep phenotyping. The Xenon MRI investigation of Alveolar dysfunction Substudy will conduct longitudinal xenon alveolar gas transfer and proton perfusion MRI. The POST COVID-19 interstitial lung DiseasE substudy will conduct clinically indicated bronchoalveolar lavage with matched whole blood sampling. Assessments include exploratory single cell RNA and lung microbiomics analysis, gene expression and epigenetic assessment. ETHICS AND DISSEMINATION: All contributing studies have been granted appropriate ethical approvals. Results from this study will be disseminated through peer-reviewed journals. CONCLUSION: This study will ensure the extent and consequences of LC-ILD are established and enable strategies to mitigate progression of LC-ILD.