Risk factors and symptom clusters for Long Covid: analysis of United Kingdom symptom tracker app data. (preprint)

Background: Long Covid, characterised by symptoms after Covid-19 infection which persist for longer than 12 weeks, is becoming an important societal and economic problem. As Long Covid was novel in 2020, there has been debate regarding its aetiology and whether it is one, or multiple, syndromes. This study assessed risk factors associated with Long Covid and examined symptom clusters that might indicate sub-types. Methods: 4,040 participants reporting for >4 months in the Covid Symptom Study App were included. Multivariate logistic regression was undertaken to identify risk factors associated with Long Covid. Cluster analysis (K-modes and hierarchical agglomerative clustering) and factor analysis were undertaken to investigate symptom clusters. Results: Long Covid affected 13.6% of participants. Significant risk factors included being female (P < 0.01), pre-existing poor health (P < 0.01), and worse symptoms in the initial illness. A model incorporating sociodemographics, comorbidities, and health status predicted Long Covid with an accuracy (AUROC) of 76%. The three clustering approaches gave rise to different sets of clusters with no consistent pattern across methods. Conclusions: Our model of risk factors may help clinicians predict patients at higher risk of Long Covid, so these patients can rest more, receive treatments, or enter clinical trials; reducing the burden of this long-term and debilitating condition. No consistent subtypes were identified.

Network analysis of England single parent household COVID-19 control policy impact; a proof-of-concept study (preprint)

Lockdowns have been a key infection control measure for many countries during the COVID-19 pandemic. In England first lockdown, children of single parent households (SPHs) were permitted to move between parental homes. By the second lockdown, SPH support bubbles between households were also permitted, enabling larger within-household networks. We investigated the combined impact of these approaches on household transmission dynamics, to inform policymaking for control and support mechanisms in a respiratory pandemic context. This network modelling study applied percolation theory to a base model of SPHs constructed with population survey estimates of SPH family size. To explore putative impact, varying estimates were applied regarding extent of bubbling and proportion of Different-parentage SPHs (DSPHs) (in which children do not share both the same parents). Results indicate that the formation of giant components (in which Covid-19 household transmission accelerates) are more contingent on DSPHs than on formation of bubbles between SPHs; and that bubbling with another SPH will accelerate giant component formation where one or both are DSPHs. Public health guidance should include supportive measures that mitigate the increased transmission risk afforded by support bubbling among DSPHs. Future network, mathematical and epidemiological studies should examine both independent and combined impact of policies.

How can risk of COVID-19 transmission be minimised in domiciliary care for older people: development, parameterisation and initial results of a simple mathematical model (preprint)

This paper proposes and analyses a stochastic model for the spread of an infec- tious disease that is transmitted between clients and care workers in the UK domicil- iary care setting. Interactions between clients and care workers are modelled using specially generated networks, with network parameters reflecting realistic patterns of care needs and visit allocation. These networks are then used to simulate and SEIR-type epidemic dynamics with different numbers of infectious and recovery stages. The results indicate that with the same overall capacity provided by care workers, the minimum peak proportion of infection, and the smallest overall size of infection are achieved for the highest proportion of overlap between visit allocation, i.e. when care workers have the highest chances of being allocated a visit to the same client they have visited before. An intuitive explanation of this is that while providing the required care coverage, maximising overlap in visit allocation reduces the possibility of an infectious care worker inadvertently spreading the infection to other clients. The model is quite generic and can be adapted to any particular directly transmitted infectious disease, such as, more recently, COVID-19, provided accurate estimates of disease parameters can be obtained from real data.

Personalised health education against health damage of COVID-19 epidemic in the elderly hungarian population (PROACTIVE-19): protocol of an adaptive randomised controlled clinical trial. (preprint)

Background: Early reports indicate that COVID-19 may require intensive care unit (ICU) admission in 5-26% and overall mortality can rise to 11% of the recognized cases, particularly affecting the elderly. There is a lack of evidence-based targeted pharmacological therapy for prevention and treatment alike. We aim to compare the effects of a World Health Organisation (WHO) recommendations’ based education and a personalised complex preventive lifestyle intervention package (based on the same WHO recommendation) on the outcomes of the COVID-19. Methods: PROACTIVE-19 is a pragmatic, randomized controlled clinical trial with adaptive “sample size re-estimation” design. Hungarian population over the age of 60 years without confirmed COVID-19 will be approached to participate in a telephone health assessment and lifestyle counselling voluntarily. Volunteers will be randomized into two groups: (A) general health education; (B) personalized health education. Participants will go through questioning and recommendation in 5 fields: (1) mental health, (2) smoking habits, (3) physical activity, (4) dietary habits, (5) alcohol consumption. Both groups A and B will receive the same line of questioning to assess habits concerning these topics. Assessment will be done weekly during the first month, every second week in the second month, then monthly. The composite primary endpoint will include the rate of ICU admission, hospital admission (longer han 48 hours) and mortality in COVID-19 positive cases. The estimated sample size is 3788 subjects per study arm. The planned duration of the follow-up is a minimum of one year. Discussion: These interventions may boost the body’s cardiovascular and pulmonary reserve capacities, leading to improved resistance against the damage caused by COVID-19. Consequently, lifestyle changes can reduce the incidence of life-threatening conditions and attenuate the detrimental effects of the pandemic seriously affecting the older population. Trial registration: The study has been approved by the Scientific and Research Ethics Committee of the Hungarian Medical Research Council (IV/2428- 2 /2020/EKU) and has been registered at clinicaltrials.gov (NCT04321928) on 25 March, 2020.