High number of SARS-CoV-2 persistent infections uncovered through genetic analysis of samples from a large community-based surveillance study (preprint)

Persistent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections may act as viral reservoirs that could seed future outbreaks 1-5, give rise to highly divergent lineages 6-8, and contribute to cases with post-acute Coronavirus disease 2019 (COVID-19) sequelae (Long Covid) 9,10. However, the population prevalence of persistent infections, their viral load kinetics, and evolutionary dynamics over the course of infections remain largely unknown. We identified 381 infections lasting at least 30 days, of which 54 lasted at least 60 days. These persistently infected individuals had more than 50% higher odds of self-reporting Long Covid compared to the infected controls, and we estimate that 0.09-0.5% of SARS-CoV-2 infections can become persistent and last for at least 60 days. In nearly 70% of the persistent infections we identified, there were long periods during which there were no consensus changes in virus sequences, consistent with prolonged presence of non-replicating virus. Our findings also suggest reinfections with the same major lineage are rare and that many persistent infections are characterised by relapsing viral load dynamics. Furthermore, we found a strong signal for positive selection during persistent infections, with multiple amino acid substitutions in the Spike and ORF1ab genes emerging independently in different individuals, including mutations that are lineage-defining for SARS-CoV-2 variants, at target sites for several monoclonal antibodies, and commonly found in immunocompromised patients 11-14. This work has significant implications for understanding and characterising SARS-CoV-2 infection, epidemiology, and evolution.

Lineage replacement and evolution captured by the United Kingdom Covid Infection Survey (preprint)

The Office for National Statistics COVID-19 Infection Survey is a large household-based surveillance study based in the United Kingdom. Here, we report on the epidemiological and evolutionary dynamics of SARS-CoV-2 determined by analysing sequenced samples collected up until 13th November 2021. We observed four distinct sweeps or partial-sweeps, by lineages B.1.177, B.1.1.7/Alpha, B.1.617.2/Delta, and finally AY.4.2, a sublineage of B.1.617.2, with each sweeping lineage having a distinct growth advantage compared to their predecessors. Evolution was characterised by steady rates of evolution and increasing diversity within lineages, but with step increases in divergence associated with each sweeping major lineage, leading to a faster overall rate of evolution and fluctuating levels of diversity. These observations highlight the value of viral sequencing integrated into community surveillance studies to monitor the viral epidemiology and evolution of SARS-CoV-2, and potentially other pathogens, particularly as routine PCR testing is phased out or in settings where large-scale sequencing is not feasible.

EpiBeds: Data informed modelling of the COVID-19 hospital burden in England (preprint)

The first year of the COVID-19 pandemic put considerable strain on the national healthcare system in England. 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, which was coupled to a model of the generalised epidemic. We named this model EpiBeds. 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 different clinical pathways, and the reproduction number of the generalised epidemic. 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 has provided weekly forecasts to the NHS for hospital bed occupancy and admissions in England, Wales, Scotland, and Northern Ireland.

Possible future waves of SARS-CoV-2 infection generated by variants of concern with a range of characteristics (preprint)

Ongoing infection with, and associated viral reproduction of, SARS-CoV-2 provides opportunities for the virus to acquire advantageous mutations, which may alter viral transmissibility and disease severity, and allow escape from natural or vaccine-derived immunity. The number of countries reporting Variants of Concern (VOCs) with such mutations continues to rise. Here, we investigate two scenarios for third waves of the COVID pandemic: one driven by increased transmissibility, and another driven by immune escape. We do this using three mathematical models: a parsimonious susceptible-latent-infectious-recovered (SEIR) deterministic model with homogeneous mixing, an age-structured SARS-CoV-2 transmission model and a stochastic importation model. We calibrated our models to the situation in England in May 2021, although the insights will generalise to other contexts. We therefore accurately captured infection dynamics and vaccination rates, and also used these to explore the potential impact of a putative new VOC-targeted vaccine. Epidemiological trajectories for putative VOCs are wide-ranging and heavily dependent on their transmissibility, immune escape capability, and the time at which a postulated VOC-targeted vaccine may be introduced. We demonstrate that a VOC with either a substantial transmission advantage over resident variants, or the ability to evade vaccine-derived and prior immunity, is expected to generate a wave of infections and hospitalisations comparable to those seen in the winter 2020-21 wave. Moreover, a variant that is less transmissible, but shows partial immune-escape could provoke a wave of infection that would not be revealed until control measures are further relaxed.

Early Analysis of a potential link between viral load and the N501Y mutation in the SARS-COV-2 spike protein (preprint)

A new variant of SARS-CoV-2 has emerged which is increasing in frequency, primarily in the South East of England (lineage B.1.1.7 (1); VUI-202012/01). One potential hypothesis is that infection with the new variant results in higher viral loads, which in turn may make the virus more transmissible. We found higher (sequence derived) viral loads in samples from individuals infected with the new variant with median inferred viral loads were three-fold higher in individuals with the new variant. Most of the new variants were sampled in Kent and Greater London. We observed higher viral loads in Kent compared to Greater London for both the new variant and other circulating lineages. Outside Greater London, the variant has higher viral loads, whereas within Greater London, the new variant does not have significantly higher viral loads compared to other circulating lineages. Higher variant viral loads outside Greater London could be due to demographic effects, such as a faster variant growth rate compared to other lineages or concentration in particular age-groups. However, our analysis does not exclude a causal link between infection with the new variant and higher viral loads. This is a preliminary analysis and further work is needed to investigate any potential causal link between infection with this new variant and higher viral loads, and whether this results in higher transmissibility, severity of infection, or affects relative rates of symptomatic and asymptomatic infection Document Description and PurposeThis is an updated report submitted to NERVTAG in December 2020 as part of urgent investigations into the new variant of SARS-COV-2 (VUI-202012/01). It makes full use of (and is restricted to) all sequence data and associated metadata available to us at the time this original report was submitted and remains provisional. Under normal circumstances more genomes and metadata would be obtained and included before making this report public. We will update this preprint when more genomes and metadata are available and before submitting for peer review.

Short-term forecasts to inform the response to the COVID-19 epidemic in the UK (preprint)

Background: Short-term forecasts of infectious disease can create situational awareness and inform planning for outbreak response. Here, we report on multi-model forecasts of Covid-19 in the UK that were generated at regular intervals starting at the end of March 2020, in order to monitor expected healthcare utilisation and population impacts in real time. Methods: We evaluated the performance of individual model forecasts generated between 24 March and 14 July 2020, using a variety of metrics including the weighted interval score as well as metrics that assess the calibration, sharpness, bias and absolute error of forecasts separately. We further combined the predictions from individual models to ensemble forecasts using a simple mean as well as a quantile regression average that aimed to maximise performance. We further compared model performance to a null model of no change. Results: In most cases, individual models performed better than the null model, and ensembles models were well calibrated and performed comparatively to the best individual models. The quantile regression average did not noticeably outperform the mean ensemble. Conclusions: Ensembles of multi-model forecasts can inform the policy response to the Covid-19 pandemic by assessing future resource needs and expected population impact of morbidity and mortality.

Mental Health Status of Adolescents During the COVID-19 Pandemic: A Cross-sectional Survey among the Bangladeshi Graduate Students at Dhaka City (preprint)

ObjectivesTo identify the level of Mental Health Status of Adolescents During the COVID-19 Pandemic among the Bangladeshi Graduate Student at Dhaka MethodA cross-sectional survey was conducted with 330 students from different public and Private Universities in Dhaka, Bangladesh between April 01, 2020 and July 31, 2020 amid the COVID-19 lockdown period in Bangladesh. A standard, self-administered online questionnaire consisting of questions on socio-demographic variables, mental health status, as well as stress management sent to the respondents through social networking platforms. Data were analyzed using descriptive statistics, t-test, one-way ANOVA and correlation tests. ResultsThe mean score of mental health status was 2.08 based on four points scale. They felt problem in decision making (3.04), in doing the things well (2.92), in enjoying normal day to day life (2.88), in playing a useful part in life (2.85), in doing their task (2.75), living in perfectly well and in good health (2.70). The respondents also developed a suicidal tendency (2.55), felt nervous in strung-up (2.24), took longer time to do things (2.14), felt tightness and pressure in head (2.12), and found themselves pressurized by various stuff (2.05). This study also found a significant positive relationship between mental health status and age, living with parents, and parents attitude. Finally, this study revealed that the respondents managed their stress by chatting with their friends, parents and siblings, and by sleeping. ConclusionMental health status of adolescents was found moderate in this study. This study suggests further large-scale study including different socio-economic settings in order to figure out the real scenario of adolescents mental health status of the country during the pandemic.

A retrospective cluster analysis of COVID-19 cases by county (preprint)

The COVID-19 pandemic in the U.S. has exhibited distinct waves, the first beginning in March 2020, the second beginning in early June, and additional waves currently emerging. Paradoxically, almost no county has exhibited this multi-wave pattern. We aim to answer three research questions: (1) How many distinct clusters of counties exhibit similar COVID-19 patterns in the time-series of daily confirmed cases?; (2) What is the geographic distribution of the counties within each cluster? and (3) Are county-level demographic, socioeconomic and political variables associated with the COVID-19 case patterns? We analyzed data from counties in the U.S. from March 1 to October 24, 2020. Time series clustering identified clusters in the daily confirmed cases of COVID-19. An explanatory model was used to identify demographic, socioeconomic and political variables associated the cluster patterns. Four patterns were identified from the timing of the outbreaks including counties experiencing a spring, an early summer, a late summer, and a fall outbreak. Several county-level demographic, socioeconomic, and political variables showed significant associations with the identified clusters. The timing of the outbreak is related both to the geographic location within the U.S. and several variables including age, poverty distribution, and political association. These results show that the reported pattern of cases in the U.S. is observed through aggregation of the COVID-19 cases, suggesting that local trends may be more informative. The timing of the outbreak varies by county, and is associated with important demographic, socioeconomic and geographic factors.

SARS-CoV-2 genomics beyond the consensus sequence: evidence for circulating mixed viral populations (preprint)

We gratefully acknowledge the UK COVID-19 Genomics Consortium (COG UK) for funding, and Public Health Wales / Cardiff University and MRC-University of Glasgow Centre for Virus Research for making their COG-UK sequence data publicly available. COG-UK is supported by funding from the Medical Research Council (MRC) part of UK Research & Innovation (UKRI), the National Institute of Health Research (NIHR) and Genome Research Limited, operating as the Wellcome Sanger Institute. The research was supported by the Wellcome Trust Core Award Grant Number 203141/Z/16/Z with funding from the NIHR Oxford BRC. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. We are deeply grateful to Robert Esnouf and the BMRC Research Computing team for unfailing assistance with computational infrastructure. We also thank Benjamin Carpenter and James Docker for assistance in the laboratory, and Lorne Lonie, Maria Lopopolo, Chris Allen, John Broxholme and the WHG high-throughput genomics team for sequencing and quality control. The HIV clone p92BR025.8 was obtained through the Centre For AIDS Reagents from Drs Beatrice Hahn and Feng Gao, and the UNAIDS Virus Network (courtesy of the NIH AIDS Research and Reference Reagent Program). KAL is supported by The Wellcome Trust and The Royal Society (107652/Z/15/Z). MH, LF, MdC, GMC, NO, LAD, DB, CF and TG are supported by Li Ka Shing Foundation funding awarded to CF. PS is supported by a Wellcome Investigator Award (WT103767MA). SummarySARS-CoV-2, the causative agent of COVID-19, emerged in late 2019 causing a global pandemic, with the United Kingdom (UK) one of the hardest hit countries. Rapid sequencing and publication of consensus genomes have enabled phylogenetic analysis of the virus, demonstrating SARS-CoV-2 evolves relatively slowly1, but with multiple sites in the genome that appear inconsistent with the overall consensus phylogeny2. To understand these discrepancies, we used veSEQ3, a targeted RNA-seq approach, to quantify minor allele frequencies in 413 clinical samples from two UK locations. We show that SARS-CoV-2 infections are characterised by extensive within-host diversity, which is frequently shared among infected individuals with patterns consistent with geographical structure. These results were reproducible in data from two other sequencing locations in the UK, where we find evidence of mixed infection by major circulating lineages with patterns that cannot readily be explained by artefacts in the data. We conclude that SARS-CoV-2 diversity is transmissible, and propose that geographic patterns are generated by transient co-circulation of distinct viral populations. Co-transmission of mixed populations could open opportunities for resolving clusters of transmission and understanding pathogenesis.

Challenges in control of Covid-19: short doubling time and long delay to effect of interventions (preprint)

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.

Challenges in control of Covid-19: short doubling time and long delay to effect of interventions (preprint)

Early assessments of the spreading rate of COVID-19 were subject to significant uncertainty, as expected with limited data and difficulties in case ascertainment, but more reliable inferences can now be made. Here, we estimate from European data that COVID-19 cases are expected to double initially every three days, until social distancing interventions slow this growth, and that the impact of such measures is typically only seen nine days - i.e. three doubling times - after their implementation. We argue that such temporal patterns are more critical than precise estimates of the basic reproduction number for initiating interventions. This observation has particular implications for the low- and middle-income countries currently in the early stages of their local epidemics.