Metabolomic and gut microbiome profiles across the spectrum of community-based COVID and non-COVID disease: A COVID-19 Biobank study (preprint)

Whilst many with SARS-CoV-2 infection have mild disease, managed in the community, individuals with cardiovascular risk factors experienced often more severe acute disease, requiring hospitalisation. Increasing concern has also developed over long symptom duration in many individuals, including the majority who managed acutely in the community. Risk factors for long symptom duration, including biological variables, are still poorly defined. We examine post-illness metabolomic and gut-microbiome profiles, in community-dwelling participants with SARS-CoV-2, ranging from asymptomatic illness to Post-COVID Syndrome, and participants with prolonged non-COVID-19 illnesses. We also assess a pre-established metabolomic biomarker score for its association with illness duration. We found an atherogenic-dyslipidaemic metabolic profile, and greater biomarker scores, associated with longer illness, both in individuals with and without SARS-CoV-2 infection. We found no association between illness duration and gut microbiome in convalescence. Findings: highlight the potential role of cardiometabolic dysfunction to the experience of long illness duration, including after COVID-19.

Factors associated with COVID-19 related hospitalisation, critical care admission and mortality using linked primary and secondary care data (preprint)

BackgroundTo identify risk factors associated with increased risk of hospitalisation, intensive care unit (ICU) admission and mortality in inner North East London (NEL) during the first UK COVID-19 wave. MethodsMultivariate logistic regression analysis on linked primary and secondary care data from people aged 16 or older with confirmed COVID-19 infection between 01/02/2020-30/06/2020 determined odds ratios (OR), 95% confidence intervals (CI) and p-values for the association between demographic, deprivation and clinical factors with COVID-19 hospitalisation, ICU admission and mortality. ResultsOver the study period 1,781 people were diagnosed with COVID-19, of whom 1,195 (67%) were hospitalised, 152 (9%) admitted to ICU and 400 (23%) died. Results confirm previously identified risk factors: being male, or of Black or Asian ethnicity, or aged over 50. Obesity, type 2 diabetes and chronic kidney disease (CKD) increased the risk of hospitalisation. Obesity increased the risk of being admitted to ICU. Underlying CKD, stroke and dementia in-creased the risk of death. Having learning disabilities was strongly associated with increased risk of death (OR=4.75, 95%CI=(1.91,11.84), p=0.001). Having three or four co-morbidities increased the risk of hospitalisation (OR=2.34,95%CI=(1.55,3.54),p<0.001;OR=2.40, 95%CI=(1.55,3.73), p<0.001 respectively) and death (OR=2.61, 95%CI=(1.59,4.28), p<0.001;OR=4.07, 95% CI= (2.48,6.69), p<0.001 respectively). ConclusionsWe confirm that age, sex, ethnicity, obesity, CKD and diabetes are important determinants of risk of COVID-19 hospitalisation or death. For the first time, we also identify people with learning disabilities and multi-morbidity as additional patient cohorts that need to be actively protected during COVID-19 waves.

Determining the level of social distancing necessary to avoid a second COVID-19 epidemic wave: a modelling study for North East London (preprint)

Background: As England is starting to ease lockdown restrictions in a phased manner, it is important to determine the level of social distancing compliance, quantified here as the daily number of social contacts per person, i.e. the daily contact rate, needed to maintain control of the COVID-19 epidemic and not exceed acute bed capacity in case of a secondary wave later this year. This work uses mathematical modelling to simulate the levels of COVID-19 in North East London (NEL) and inform the level of social distancing necessary to protect the public and the healthcare demand from a secondary COVID-19 wave during 2020. Methods: We used a Susceptible-Exposed-Infected-Removed (SEIR) model describing the transmission of SARS-CoV-2 in North East London (NEL), calibrated to data on confirmed COVID-19 associated hospitalisations, hospital discharges and in-hospital deaths in NEL. To account for the uncertainty in both the infectiousness period and the proportion of symptomatic infection, we simulated nine scenarios for different combinations of infectiousness period (1, 3 and 5 days) and proportion of symptomatic infection (70%, 50% and 25% of all infections). Across all scenarios, the calibrated model was used to assess the risk of occurrence and forecast the strength and timing of a second COVID-19 wave under varying levels of daily contact rate from July 04, 2020. Specifically, the daily contact rate required to suppress the epidemic and prevent resurgence of COVID-19 cases, and the daily contact rate required to stay within the acute bed capacity of the NEL system without any additional intervention measures after July 2020, were determined across the nine different scenarios. Results: Our results caution against a full relaxing of the lockdown, predicting that a return to pre-COVID-19 levels of social contact from July 04, 2020 may induce a second wave up to eight times the original wave. With different levels of social distancing continuing into next year, the second wave can be avoided or the strength of the second wave can be mitigated. Keeping the daily contact rate lower than 5 or 6, depending on scenarios, for the rest of this year, can prevent increase in the number of COVID-19 cases, could keep the effective reproduction number R below 1 and a second COVID-19 wave may be avoided in NEL. A daily contact rate between 6 and 7, across scenarios, is likely to increase R above 1 and result in a secondary COVID-19 wave with significantly increased COVID-19 cases and associated deaths, but with demand for hospital based care remaining within the bed capacity of the NEL health and care system. In contrast, an increase in daily contact rate above 8 to 9, depending on scenarios, will likely exceed the acute bed capacity in NEL and may potentially require additional lockdowns. This scenario is associated with significantly increased COVID-19 cases and deaths, and acute COVID-19 care demand is likely to require significant scaling down of the usual operation of the health and care system, and should be avoided.Conclusions: Our findings suggest that to avoid a second COVID-19 wave and to stay within the acute bed capacity of the NEL health and care system, phased relaxing of the social distancing in NEL is advised with a view to limiting the average number of social interactions in the population. Increasing the social interaction rapidly could result in a second COVID-19 wave that will likely exceed the acute bed capacity in the system, and depending on the strength of the resurgence may require additional lockdown measures. 

Modelling the Health and Economic Impacts of Population-Wide Testing, Contact Tracing and Isolation (PTTI) Strategies for COVID-19 in the UK (preprint)

Background: The COVID-19 epidemic in the UK has resulted in over 280,000 reported cases and over 40,000 deaths as of 5th June 2020. In the context of a slower increase in reported cases and deaths associated with COVID-19 over the last few weeks compared to earlier in the epidemic, the UK is starting to relax the physical restrictions (‘lockdown’) that have been imposed since 23 March 2020. This has been accompanied by the announcement of a strategy to test people for infection, trace contacts of those tested positive, and isolate positive diagnoses. While such policies are expected to be impactful, there is no conclusive evidence of which approach to this is likely to achieve the most appropriate balance between benefits and costs. This study combines mathematical and economic modelling to estimate the impact, costs, feasibility, and health and economic effects of different strategies. Methods: We provide detailed description, impact, costing, and feasibility assessment of population-scale testing, tracing, and isolation strategies (PTTI). We estimate the impact of different PTTI strategies with a deterministic mathematical model for SARS-CoV-2 transmission that accurately captures tracing and isolation of contacts of individuals exposed, infectious, and diagnosed with the virus. We combine this with an economic model to project the mortality, intensive care, hospital, and non-hospital case outcomes, costs to the UK National Health Service, reduction in GDP, and intervention costs of each strategy. Model parameters are derived from publicly available data, and the model is calibrated to reported deaths associated with COVID-19. We modelled 31 scenarios in total (Panel 2). The first 18 comprised nine with ‘triggers’ (labelled with the -Trig suffix) for subsequent lockdown periods (>40,000 new infections per day) and lockdown releases (<10,000 new infections per day), and nine corresponding scenarios without triggers, namely: no large-scale PTTI (scenario 1); scale-up of PTTI to testing the whole population every week, with May–July 2020 lockdown release (scenario 2b), or delayed lockdown release until scale-up complete on 31 August 2020 (scenario 2a); these two scenarios with mandatory use of face coverings (scenarios 3a and 3b); and scenarios 2a, 2b, 3a, 3b replacing untargeted PTTI with testing of symptomatic people only (scenarios 4a, 4b, 4c, 4d). The final 13 scenarios looked at: whole population weekly testing to suppress the epidemic with lower tracing success (scenarios 3b-Trig00, 3b-Trig10, 3b-Trig20, 3b-Trig30) and switched to targeted testing after two months when it may suppress the epidemic (scenarios 3b-Trig00-2mo and 3b-Trig30-2mo), and targeted testing with lower tracing success (scenarios 4d-Trig10, 4dTrig20, 4d-Trig30, 4d-Trig40, 4d-Trig50, 4d-Trig60, 4d-Trig70). Findings: Given that physical distancing measures have already been relaxed in the UK, scenario 4d-Trig (targeted testing of symptomatic people only, with a mandatory face coverings policy and subsequent lockdown triggered to enable PTTI to suppress the epidemic), is a strategy that will result in the fewest deaths (~52,000) and has the lowest intervention costs (~£8bn). The additional lockdown results in total reduction in GDP of ~£503bn, less than half the cost to the economy of subsequent lockdowns triggered in a scenario without PTTI (scenario 1-Trig, ~£1180bn reduction in GDP, ~105,000 deaths). In summer months, with lower cold and flu prevalence, approximately 75,000 symptomatic people per day need to be tested for this strategy to work, assuming 64% of their contacts are effectively traced (~80% traced with 80% success) within the infectious period (most within the first two days and nearly all by seven days) and all are isolated – including those without any symptoms – for 14 days. Untargeted testing of everyone every week, if it were feasible, may work without tracing, but at a higher cost (scenario 3b-Trig00). This cost could be reduced by switching to targeted testing after the epidemic is suppressed (scenario 3b-Trig30-2mo), though we note the epidemic could be suppressed with targeted testing itself providing tracing and isolation has at least a 32% success rate (scenario 4dTrig40). Interpretation: PTTI strategies to suppress the COVID-19 epidemic within the context of a relaxation of lockdown will necessitate subsequent lockdowns to keep the epidemic suppressed during PTTI scale-up. Targeted testing of symptomatic people only can suppress the epidemic if accompanied by mandated use of face coverings. The feasibility of PTTI depends on sufficient capacity, capabilities, infrastructure and integrated systems to deliver it. The political and public acceptability of alternative scenarios for subsequent lockdowns needs to take account of crucial implications for employment, personal and national debt, education, population mental health and non-COVID-19 disease. Our model is able to incorporate additional scenarios as the situation evolves. Funding: No specific funding was received in support of this study. Grant support for specific authors is as follows: WW acknowledges support from the Chief Scientist Office (COV/EDI/20/12) RR, JPG and EP are supported by the National Institute for Health Research ARC North Thames. NMcG is a recipient of an NIHR Global Health Research Professorship award (Ref: RP-2017-08-ST2-008). The views expressed in this independent research are those of the authors and not necessarily those of the National Institute for Health Research or the Department of Health and Social Care. KMG is supported by the UK Medical Research Council (MC_UU_12011/4), the National Institute for Health Research (NIHR Senior Investigator (NF-SI-0515-10042) and NIHR Southampton Biomedical Research Centre (IS-BRC-1215-20004)), British Heart Foundation (RG/15/17/3174) and the US National Institute On Aging of the National Institutes of Health (Award No. U24AG047867). GY acknowledges her research partially supported from the Newton Fund through a UK-China ARM Partnership Hub award (No:MR/S013717/1).Declaration of Interests: All authors declare no competing interests.