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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.
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Since its emergence in late 2019, SARS-CoV-2 has diversified into a large number of lineages and globally caused multiple waves of infection. Lineages have the potential to spread rapidly and internationally if they have higher intrinsic transmissibility and/or can evade host immune responses, as has been seen with the Alpha, Delta, and Omicron variants of concern (VoCs). Phylogenetic methods provide the gold standard for representing the global diversity of SARS-CoV-2 and to identify newly emerging lineages. However, these methods are computationally expensive, struggle when datasets get too large, and require manual curation to designate new lineages. These issues will only intensify as the vast number of SARS-CoV-2 genomes already available continues to grow. It will therefore be beneficial to develop complementary methods that can incorporate all of the genetic data available, without down sampling, to extract meaningful information rapidly and with minimal curation. Here, we demonstrate the utility of using algorithmic approaches based on word-statistics to represent whole sequences, bringing speed, scalability, and interpretability to the construction of genetic topologies, and that can be used to augment traditional classification practice based on phylogeny.
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ObjectivesTo quantify contact patterns of UK home delivery drivers and identify protective measures adopted during the pandemic. MethodsWe 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. ResultsDelivery drivers had a mean number of 71.6 (95% Confidence Interval (CI) 61.0 to 84.1) customer contacts per shift and 15.0 (95%CI 11.19 to 19.20) depot contacts per shift. Maintaining physical distancing with customers was more common than at delivery depots. Prolonged contact (more than 5 minutes) 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. Additionally, 5.3% (95%CI 2.3% to 10.2%) of participants reported having worked whilst ill with COVID-19 symptoms, or with a member of their household having a suspected or confirmed case of COVID-19. ConclusionDelivery drivers had a large number of face-to-face customer and depot contacts per shift compared to 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 sanitizer was widespread.
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Within-host models have been used to successfully describe the dynamics of multiple viral infections, however, the dynamics of SARS-CoV-2 virus infection remain poorly understood. A greater understanding of how the virus interacts with the host can contribute to more realistic epidemiological models and help evaluate the effect of antiviral therapies and vaccines. Here, we present a within-host model to describe SARS-CoV-2 viral dynamics in the upper respiratory tract of individuals enrolled in the UK COVID-19 Human Challenge Study. Using this model, we investigate the viral dynamics and provide timescales of infection that independently verify key epidemiological parameters important in the management of an epidemic. In particular, we estimate that an infected individual is first capable of transmitting the virus after approximately 2.1 days, remains infectious for a further 8.3 days, but can continue to test positive using a PCR test for up to 27 days.
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ObjectiveWe 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. MethodsWe developed 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. ResultsThe 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 large-item delivery 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. ConclusionThis 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. IMPORTANCEDuring the COVID-19 pandemic the home-delivery sector was vital to maintaining peoples access to certain goods, and sustaining levels of economic activity for a variety of businesses. However, this important work necessarily involved contact with a large number of customers as well as colleagues. This means that questions have often been raised about whether enough was being done to keep customers and staff safe. Estimating the potential risk to customers and staff is complex, but here we tackle this problem by building a model of workplace and customer contacts, from which we simulate SARS-CoV-2 transmission. By involving industry representatives in the development of this model, we have simulated interventions that have either been applied or considered, and so the findings of this study are relevant to decisions made in that sector. Furthermore, we can learn generic lessons from this specific case study which apply to many types of shared workplace as well as highlighting implications of the highly stochastic nature of disease transmission in small populations.
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BackgroundThe 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. MethodsWe 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 UKs logistics sector, including takeaway food delivery, large and small goods delivery and home appliance installation, and logistics technology providers. Inductive thematic analysis was completed using NVivo12 to generate emerging themes and subthemes. Themes/subthemes relevant to interventions were mapped deductively onto an adapted Hierarchy of Control (HoC) framework, focusing on delivery workers. Themes/subthemes relevant to the process of implementation were analyzed to understand the barriers and facilitators of rapid responses. ResultsHoC 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. In addition, a broad range of measures were implemented, including 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, overwhelming 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. ConclusionsThis 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. Contributions to the fieldO_LIThe study offered important insights into the process of the UK logistics sectors response to the COVID-19 pandemic. C_LIO_LIThe Hierarchy of Control (HoC) framework was adapted for the evaluation of a collection of non-pharmaceutical interventions (NPIs) implemented in a non-healthcare sector. C_LIO_LIThematic analysis of qualitative data generated themes that were relevant to the process of rapid implementation of NPIs during a public health emergency. C_LIO_LIBarriers, facilitators and sustainability issues of the sectors rapid response to the COVID-19 pandemic have been highlighted to inform future research on implementation strategies. C_LI
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BackgroundFrom January to May 2021 the alpha variant (B.1.1.7) of SARS-CoV-2 was the most commonly detected variant in the UK, but since then the Delta variant (B.1.617.2), first detected in India, has become the predominant variant. The UK COVID-19 vaccination programme started on 8th December 2020. Most vaccine effectiveness studies to date have 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 infection with respect to the Delta variant in a UK setting. MethodsWe 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. ResultsWe 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. ConclusionVaccine 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 would advocate for completing the full course programme to maximise individual protection and reduce transmission.
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As the SARS-CoV-2 virus mutates, mutations harboured in patients become increasingly diverse. Patients classified into two strains may have overlapping non-variant-defining mutations. Mutation calling by sequencing is relative to a reference genome. As SARS-CoV-2 mutates, tracking emerging mutant strains may become increasingly problematic if the reference genome remains Wuhan-Hu-1, because the comparison then becomes indirect: current dominant strain relative to Wuhan-Hu-1 versus emerging strain relative to Wuhan-Hu-1. The original Thermo Fishers TaqPath PCR test, on which the UK has standardized national testing of SARS-CoV-2 primarily, targets Wuhan-Hu-1. PCR targets appear readily updated, as TaqPath 2.0 now targets both currently known and future SARS-CoV-2 mutations, probing the N gene and ORF1ab but not the S gene, with 8 probes instead of the original 3 probes. Going forward, our statistical method can more directly compare current wildtype versus emerging mutants, since our new method can use any pair of probes updated to probe the current wildtype and anticipated mutations. The fact that patients harbour mixtures of mutations allows our statistical methods to potentially catch emerging mutants. Given a PCR test which targets the current dominant strain (current wildtype), our statistical method can potentially directly differentiate the current wildtype from an emerging strain.
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We explore strategies of contact tracing, case isolation and quarantine of exposed contacts to control the SARS-CoV-2 epidemic using a branching process model with household structure. This structure reflects higher transmission risks among household members than among non-household members, and is also the level at which physical distancing policies have been applied. We explore implementation choices that make use of household structure, and investigate strategies including two-step tracing, backwards tracing, smartphone tracing and tracing upon symptom report rather than test results. The primary model outcome is the effect on the growth rate of the epidemic under contact tracing in combination with different levels of physical distancing, and we investigate epidemic extinction times to indicate the time period over which interventions must be sustained. We consider effects of non-uptake of isolation/quarantine, non-adherence, and declining recall of contacts over time. We find that compared to self-isolation of cases but no contact tracing, a household-based contact tracing strategy allows for some relaxation of physical distancing measures; however, it is unable to completely control the epidemic in the absence of other measures. Even assuming no imported cases and sustainment of moderate distancing, testing and tracing efforts, the time to bring the epidemic to extinction could be in the order of months to years.
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BackgroundTo estimate excess mortality for care home residents during the COVID-19 pandemic in England, exploring associations with care home characteristics. MethodsDaily number of deaths in all residential and nursing homes in England notified to the Care Quality Commission (CQC) from 1st January 2017 to 7th August 2020. Care home level data linked with CQC care home register to identify homes characteristics: client type (over 65s/children and adults), ownership status (for-profit/not-for-profit; branded/independent), and size (small/medium/large). Excess deaths computed as the difference between observed and predicted deaths using local authority fixed-effect Poisson regressions on pre-pandemic data. Fixed-effect logistic regressions were used to model odds of experiencing COVID-19 suspected/confirmed deaths. FindingsUp to 7th August 2020 there were 29,542 (95%CI: 25,176 to 33,908) excess deaths in all care homes. Excess deaths represented 6.5% (95%CI: 5.5% to 7.4%) of all care home beds, higher in nursing (8.4%) than residential (4.6%) homes. 64.7% (95%CI: 56.4% to 76.0%) of the excess deaths were confirmed/suspected COVID-19. Almost all excess deaths were recorded in the quarter (27.4%) of homes with any COVID-19 fatalities. The odds of experiencing COVID-19 attributable deaths were higher in homes providing nursing services (OR: 1.8, 95%CI: 1.6 to 2.0); to older people and/or with dementia (OR: 5.5, 95%CI: 4.4 to 6.8); among larger (vs. small) homes (OR: 13.3, 95%CI: 11.5 to 15.4); belonging to a large provider/brand (OR: 1.2, 95%CI: 1.1 to 1.3). There was no significant association with for-profit status of providers. InterpretationTo limit excess mortality, policy should be targeted at care homes to minimise the risk of ingress of disease and limit subsequent transmission. Our findings provide specific characteristic targets for further research on mechanisms and policy priority. FundingNIHR. Summary boxO_ST_ABSEvidence before this studyC_ST_ABSGlobally, residents in care homes have experienced disproportionately high morbidity and mortality from COVID-19. Excess mortality incorporates all direct and indirect mortality effects of the pandemic. We searched MEDLINE for published literature, pre-publication databases (medRxiv and Lancet pre-print) and grey literature (ONS and Google) for care homes AND COVID-19 AND mortality, to 31st October 2020. We screened for evidence on excess deaths in care homes in England, and international evidence of the association of COVID-19 deaths and outbreaks with care home characteristics. Official estimates from England and Wales have reported aggregated excess deaths by place of occurrence, but we identified no peer-reviewed excess deaths study in this setting. These aggregates, however, do not account for care home residents dying in other settings (e.g. hospital), nor provide sufficient information to reflect on the impacts of enacted policies over the period, or to inform new policies for future virus waves. Previous peer-reviewed and pre-publication studies have also shown the heterogeneous effects of COVID-19 by care home characteristics in other countries. Particularly important from the current literature appears to be care home size, with larger care homes tending to be associated with more negative outcomes in studies with smaller sample sizes. A study from the Lothian region of Scotland additionally found excess deaths concentrated in a minority of homes that experienced an outbreak. However, a national breakdown of excess deaths by care home characteristics is largely lacking from the current literature in England, with a specific market structure and policy context. Added value of this studyWe use nationally representative administrative data from all care homes in England to estimate overall excess deaths and by care home characteristics: setting type (nursing or residential home), client types (offering services for people aged 65+ and/or people with dementia or offering services to children and adults), ownership status (whether not-for-profit - charity/NHS/LA-run homes - or for-profit), whether known to be affiliated to a large provider/brand or independent, and classification according to their registered maximum bed capacity (small, medium and large). We then used multivariable logistic regression to estimate the adjusted odds of a care home experiencing a suspected or confirmed COVID-19 death across these characteristics. We found that only 65% of excess deaths were flagged as officially confirmed/suspected COVID-19 attributed. However, almost all excess deaths occurred in the roughly quarter of care homes that reported at least one suspected/confirmed COVID-19 death. After adjusting for other care home characteristics, larger care homes (vs. small) had the highest odds of experiencing at least one suspected/confirmed COVID-19 death. These findings confirm those from the previous literature, in a unique policy context and with national data. Implications of all the available evidenceThe fact that nearly all excess deaths occurred in care homes with at least one COVID-19 attributed death suggests that directly-attributed deaths are very likely to be under-recorded. It also suggests that any indirect mortality effect, of COVID-19 and any enacted policies, were predominantly constrained to those homes experiencing an outbreak. Larger homes are likely to experience higher footfall in general, and so higher probability of contact with an infected individual, which is likely a contributing factor to the association. Furthermore, it might be easier to ensure person-centred protocols in small care homes due to the scale. There is an urgent need for further research to explore the mechanisms in relation to care home characteristics. Also, to empirically test effective interventions, in consideration of additional impacts on quality of life and psychological wellbeing. However, until this is possible, prioritising existing resources, such as testing and PPE equipment, for care homes to prevent ingress of disease is key to preventing large excess mortality.
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BackgroundInfection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in children is associated with better outcomes than in adults. The inflammatory response to COVID-19 infection in children remains poorly characterised. MethodsWe retrospectively analysed the medical records of 127 laboratory-confirmed COVID-19 patients aged 1 month to 16 years from Wuhan and Jingzhou of Hubei Province. Patients presented between January 25th and March 24th 2020. Information on clinical features, laboratory results, plasma cytokines/chemokines and lymphocyte subsets were analysed. FindingsChildren admitted to hospital with COVID-19 were more likely to be male (67.7%) and the median age was 7.3 [IQR 4.9] years. All but one patient with severe disease was aged under 2 and the majority (5/7) had significant co-morbidities. Despite 53% having viral pneumonia on CT scanning only 2 patients had low lymphocyte counts and no differences were observed in the levels of plasma proinflammatory cytokines, including interleukin (IL)-2, IL-4, IL-6, tumour necrosis factor (TNF)-, and interferon (IFN)-{gamma} between patients with mild, moderate or severe disease. InterpretationsWe demonstrated that the immune responses of children to COVID-19 infection is significantly different from that seen in adults. Our evidence suggests that SARS-CoV-2 does not trigger a robust inflammatory response or cytokine storm in children with COVID-19, and this may underlie the generally better outcomes seen in children with this disease. These data also imply anti-cytokine therapies may not be effective in children with moderate COVID-19. FundingThis study was funded by National Natural Foundation of China (No. 81970653). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed without language restriction for studies published until June 25, 2020, using the search terms "SARS-CoV-2" or "novel coronavirus" or "COVID-19" and "immune responses" or "innate immunity" or "cytokine" or "subset of lymphocyte" and "children" or "adolescent". Previously published research describes that severe and fatal cases in children are relatively rare. However, the inflammatory responses to COVID-19 infection in children remains poorly characterised. Added value of this studyWe analysed data from 127 laboratory-confirmed COVID-19 patients aged 1 month to 16 years in Hubei province to explore the immune responses to SARS-CoV-2 infection presenting to hospital with COVID-19. Cell numbers of CD3+, CD4+, CD8+ and natural killer T cells were within mostly normal limits even in more severe cases, and the levels of immunoglobulins, and proinflammatory cytokines, including interleukin (IL)-2, IL-4, IL-6, tumour necrosis factor (TNF)-, and interferon (IFN)-{gamma} were not generally elevated regardless of disease severity. Implications of all the available evidenceThe immune response to SARS-CoV-2 infection of children is significantly different from that seen in adults. The inflammatory responses seen even in children with viral pneumonia on CT are relatively mild and do not trigger the "cytokine storm" seen in some adults with COVID-19. This implies anti-cytokine therapies may not be effective in children with COVID-19.
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Enclosed societies (i.e. locations that are connected to wider community only by subgroups of their population and that are dominated by within society transmission) have the potential, upon establishment of a respiratory disease, to suffer a large proportion of the population within becoming infected. Care homes are particularly susceptible to COVID19 outbreaks and suffer high mortality due to vulnerable population within. Recent data on the number of new outbreak reports in care homes to Public Health England shows an initial increase then plateau perhaps associated with an SIS model dynamic. Without change in policy moving forward a high prevalence in such setting is predicted of around 75%. Action is needed to support staff in such settings.
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The unconstrained growth rate of COVID-19 is crucial for measuring the impact of interventions, assessing worst-case scenarios, and calibrating mathematical models for policy planning. However, robust estimates are limited, with scientific focus on the time-insensitive basic reproduction number R0. Using multiple countries, data streams and methods, we consistently estimate that European COVID-19 cases doubled every three days when unconstrained, with the impact of physical distancing interventions typically seen about nine days after implementation, during which time cases grew eight-fold. The combination of fast growth and long detection delays explains the struggle in countries response better than large values of R0 alone, and warns against relaxing physical distancing measures too quickly. Testing and tracing are fundamental in shortening such delays, thus preventing cases from escalating unnoticed.