Comparison of the 2021 COVID-19 Roadmap Projections against Public Health Data in England (preprint)

Control and mitigation of the COVID-19 pandemic in England has relied on a combination of vaccination and non-pharmaceutical interventions (NPIs). Some of these NPIs are extremely costly (economically and socially), so it was important to relax these promptly without overwhelming already burdened health services. The eventual policy was a Roadmap of four relaxation steps throughout 2021, taking England from lock-down to the cessation of all restrictions on social interaction. In a series of six Roadmap documents generated throughout 2021, models assessed the potential risk of each relaxation step. Here we show that the model projections generated a reliable estimation of medium-term hospital admission trends, with the data points up to September 2021 generally lying within our 95% prediction intervals. The greatest uncertainties in the modelled scenarios came from vaccine efficacy estimates against novel variants, and from assumptions about human behaviour in the face of changing restrictions and risk.

Short-term Projections based on Early Omicron Variant Dynamics in England (preprint)

Throughout the ongoing COVID-19 pandemic, the worldwide transmission and replication of SARS-COV-2, the causative agent of COVID-19 disease, has resulted in the opportunity for multiple mutations to occur that may alter the virus transmission characteristics, the effectiveness of vaccines and the severity of disease upon infection. The Omicron variant (B.1.1.529) was first reported to the WHO by South Africa on 24 November 2021 and was declared a variant of concern by the WHO on 26 November 2021. The variant was first detected in the UK on 27 November 2021 and has since been reported in a number of countries globally where it is frequently associated with rapid increase in cases. Here we present analyses of UK data showing the earliest signatures of the Omicron variant and mathematical modelling that uses the UK data to simulate the potential impact of this variant in the UK. In order to account for the uncertainty in transmission advantage, vaccine escape and severity at the time of writing, we carry out a sensitivity analysis to assess the impact of these variant characteristics on future risk.

Waning, Boosting and a Path to Endemicity for SARS-CoV-2 (preprint)

In many countries, an extensive vaccination programme has substantially reduced the public-health impact of SARS-CoV-2, limiting the number of hospital admissions and deaths compared to an unmitigated epidemic. Ensuring a low-risk transition from the current situation to one in which SARS-CoV-2 is endemic requires maintenance of high levels of population immunity. The observed waning of vaccine efficacy over time suggests that booster doses may be required to maintain population immunity especially in the most vulnerable groups. Here, using data and models for England, we consider the dynamics of COVID-19 over a two-year time-frame, and the role that booster vaccinations can play in mitigating the worst effects. We find that boosters are necessary to suppress the imminent wave of infections that would be generated by waning vaccine efficacy. Projecting further into the future, the optimal deployment of boosters is highly sensitive to their long-term action. If protection from boosters wanes slowly (akin to protection following infection) then a single booster dose to the over 50s may be all that is needed over the next two-years. However, if protection wanes more rapidly (akin to protection following second dose vaccination) then annual or even biannual boosters are required to limit subsequent epidemic peaks an reduce the pressure on public health services.

Quantifying within-school SARS-CoV-2 transmission and the impact of lateral flow testing in secondary schools in England (preprint)

Background To control within-school SARS-CoV-2 transmission in England, secondary school pupils have been encouraged to participate in twice weekly mass testing via lateral flow device tests (LFTs) from 8th March 2021, to complement an isolation of close contacts policy in place since 31st August 2020. Strategies involving the isolation of close contacts can lead to high levels of absences, negatively impacting pupils. Methods We fit a stochastic individual-based model of secondary schools to both community swab testing data and secondary school absences data. By simulating epidemics in secondary schools from 31st August 2020 until 21st May 2021, we quantify within-school transmission of SARS-CoV-2 in secondary schools in England, the impact of twice weekly mass testing on within-school transmission, and the potential impact of alternative strategies to the isolation of close contacts in reducing pupil absences. Findings The within-school reproduction number, R school , has remained below 1 from 31st August 2020 until 21st May 2021. Twice weekly mass testing using LFTs have helped to control within-school transmission in secondary schools in England. A strategy of serial contact testing alongside mass testing substantially reduces absences compared to strategies involving isolating close contacts, with only a marginal increase in within-school transmission. Interpretation Secondary school control strategies involving mass testing have the potential to control within-school transmission while substantially reducing absences compared to an isolation of close contacts policy.

Optimal health and economic impact of non-pharmaceutical intervention measures prior and post vaccination in England: a mathematical modelling study (preprint)

Background Even with good progress on vaccination, SARS-CoV-2 infections in the UK may continue to impose a high burden of disease and therefore pose substantial challenges for health policy decision makers. Stringent government-mandated physical distancing measures (lockdown) have been demonstrated to be epidemiologically effective, but can have both positive and negative economic consequences. The duration and frequency of any intervention policy could, in theory, could be optimised to maximise economic benefits while achieving substantial reductions in disease. Methods Here we use a pre-existing SARS-CoV-2 transmission model to assess the health and economic implications of different strengths of control through time in order to identify optimal approaches to non-pharmaceutical intervention stringency in the UK, considering the role of vaccination in reducing the need for future physical distancing measures. The model is calibrated to the COVID-19 epidemic in England and we carry out retrospective analysis of the optimal timing of precautionary breaks in 2020 and the optimal relaxation policy from the January 2021 lockdown, considering the willingness to pay for health improvement. Results We find that the precise timing and intensity of interventions is highly dependent upon the objective of control. As intervention measures are relaxed, we predict a resurgence in cases, but the optimal intervention policy can be established dependent upon the willingness to pay (WTP) per QALY loss avoided. Our results show that establishing an optimal level of control can result in a reduction in net monetary loss of billions of pounds, dependent upon the precise WTP value. Conclusions It is vital, as the UK emerges from lockdown, but continues to face an on-going pandemic, to accurately establish the overall health and economic costs when making policy decisions. We demonstrate how some of these can be quantified, employing mechanistic infectious disease transmission models to establish optimal levels of control for the ongoing COVID-19 pandemic.

Coughs, Colds and “Freshers’ Flu” Survey in the University of Cambridge, 2007-2008 (preprint)

Universities provide many opportunities for the spread of infectious respiratory illnesses. Students are brought together into close proximity from all across the world and interact with one another in their accommodation, through lectures and small group teaching and in social settings. The COVID-19 global pandemic has highlighted the need for sufficient data to help determine which of these factors are important for infectious disease transmission in universities and hence control university morbidity as well as community spillover. We describe the data from a previously unpublished self-reported university survey of coughs, colds and flu-like symptoms collected in Cambridge, UK, during winter 2007-2008. The online survey collected information on symptoms and socio-demographic, academic and lifestyle factors. There were 1076 responses, 97% from University of Cambridge students (5.7% of the total university student population), 3% from staff and <1% from other participants, reporting onset of symptoms between September 2007 and March 2008. Undergraduates are seen to report symptoms earlier in the term than postgraduates; differences in reported date of symptoms are also seen between subjects and accommodation types, although these descriptive results could be confounded by survey biases. Despite the historic and exploratory nature of the study, this is one of few recent detailed datasets of flu-like infection in a university context and is especially valuable to share now to improve understanding of potential transmission dynamics in universities during the current COVID-19 pandemic.

An analysis of school absences in England during the Covid-19 pandemic (preprint)

The introduction of SARS-CoV-2, the virus that causes COVID-19 infection, in the UK in early 2020, resulted in the UK government introducing several control policies in order to reduce the spread of disease. As part of these restrictions, schools were closed to all pupils in March (except for vulnerable and key worker children), before re-opening to certain year groups in June. Finally all school children returned to the classroom in September. In this paper, we analyse the data on school absences from September 2020 to December 2020 as a result of COVID-19 infection and how that varied through time as other measures in the community were introduced. We utilise data from the Educational Settings database compiled by the Department for Education and examine how pupil and teacher absences change in both primary and secondary schools. Our results show that absences as a result of COVID-19 infection rose steadily following the re-opening of schools in September. Cases in teachers were seen to decline during the November lockdown, particularly in those regions that had previously been in tier 3, the highest level of control at the time. Cases in secondary school pupils increased for the first two weeks of the November lockdown, before decreasing. Since the introduction of the tier system, the number of absences owing to confirmed infection in primary schools was observed to be significantly lower than in secondary schools across all regions and tiers. In December, we observed a large rise in the number of absences per school in secondary school settings in the South East and Greater London, but such rises were not observed in other regions or in primary school settings. We conjecture that the increased transmissibility of the new variant in these regions may have contributed to this rise in cases in secondary schools. Finally, we observe a positive correlation between cases in the community and cases in schools in most regions, with weak evidence suggesting that cases in schools lag behind cases in the surrounding community. We conclude that there is not significant evidence to suggest that schools are playing a significant role in driving spread in the community and that careful monitoring may be required as schools re-open to determine the effect associated with open schools upon community incidence.

Vaccination and Non-Pharmaceutical Interventions: When Can the UK Relax About COVID-19? (preprint)

Background: The announcement of efficacious vaccine candidates against SARS-CoV-2 has been met with worldwide acclaim and relief. Many countries already have detailed plans for vaccine targeting based on minimising severe illness, death and healthcare burdens. Normally, relatively simple relationships between epidemiological parameters, vaccine efficacy and vaccine uptake predict the success of any immunisation programme. However, the dynamics of vaccination against SARS-CoV-2 is made more complex by age-dependent factors, changing levels of infection and the potential relaxation of non-pharmaceutical interventions (NPIs) as the perceived risk declines. Methods: In this study we use an age-structured mathematical model, matched to a range of epidemiological data, which also captures the roll-out of a two-dose vaccination programme targeted at specific age groups. Findings: We consider the interaction between the UK vaccination programme and future re- laxation (or removal) of NPIs. Our predictions highlight the population-level risks of early relaxation leading to a pronounced wave of infection, hospital admissions and deaths. Only vaccines that offer high transmission-blocking efficacy with high uptake in the general population allow relaxation of NPIs without a huge surge in deaths. Interpretation: While the novel vaccines against SARS-CoV-2 offer a potential exit strategy for this outbreak, this is highly contingent on the transmission blocking action of the vaccine and the population uptake, both of which need to be carefully monitored as vaccine programmes are rolled out in the UK and other countries.Funding Statement: This research was funded by the National Institute for Health Research (NIHR) [Policy Research Programme, Mathematical & Economic Modelling for Vaccination and Immunisation Evaluation, and Emergency Response; NIHR200411], the Medical Research Council through the COVID- 19 Rapid Response Rolling Call [grant number MR/V009761/1] and through the JUNIPER modelling consortium [grant number EP/V030477/1]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Declaration of Interests: All authors declare that they have no competing interests.Ethics Approval Statement: The data were supplied from the CHESS database after anonymisation under strict data protection protocols agreed between the University of Warwick and Public Health England. The ethics of the use of these data for these purposes was agreed by Public Health England with the Government’s SPI-M(O) / SAGE committees.

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.

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support (preprint)

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

The N-terminal domain of spike glycoprotein mediates SARS-CoV-2 infection by associating with L-SIGN and DC-SIGN (preprint)

The widespread occurrence of SARS-CoV-2 has had a profound effect on society and a vaccine is currently being developed. Angiotensin-converting enzyme 2 (ACE2) is the primary host cell receptor that interacts with the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. Although pneumonia is the main symptom in severe cases of SARS-CoV-2 infection, the expression levels of ACE2 in the lung is low, suggesting the presence of another receptor for the spike protein. In order to identify the additional receptors for the spike protein, we screened a receptor for the SARS-CoV-2 spike protein from the lung cDNA library. We cloned L-SIGN as a specific receptor for the N-terminal domain (NTD) of the SARS-CoV-2 spike protein. The RBD of the spike protein did not bind to L-SIGN. In addition, not only L-SIGN but also DC-SIGN, a closely related C-type lectin receptor to L-SIGN, bound to the NTD of the SARS-CoV-2 spike protein. Importantly, cells expressing L-SIGN and DC-SIGN were both infected by SARS-CoV-2. Furthermore, L-SIGN and DC-SIGN induced membrane fusion by associating with the SARS-CoV-2 spike protein. Serum antibodies from infected patients and a patient-derived monoclonal antibody against NTD inhibited SARS-CoV-2 infection of L-SIGN or DC-SIGN expressing cells. Our results highlight the important role of NTD in SARS-CoV-2 dissemination through L-SIGN and DC-SIGN and the significance of having anti-NTD neutralizing antibodies in antibody-based therapeutics.

Comparison of SARS-CoV-2 infection in two non-human primate species: rhesus and cynomolgus macaques (preprint)

SARS-CoV-2 is a coronavirus that sparked the current COVID-19 pandemic. To stop the shattering effect of COVID-19, effective and safe vaccines, and antiviral therapies are urgently needed. To facilitate the preclinical evaluation of intervention approaches, relevant animal models need to be developed and validated. Rhesus macaques (Macaca mulatta) and cynomolgus macaques (Macaca fascicularis) are widely used in biomedical research and serve as models for SARS-CoV-2 infection. However, differences in study design make it difficult to compare and understand potential species-related differences. Here, we directly compared the course of SARS-CoV-2 infection in the two genetically closely-related macaque species. After inoculation with a low passage SARS-CoV-2 isolate, clinical, virological, and immunological characteristics were monitored. Both species showed slightly elevated body temperatures in the first days after exposure while a decrease in physical activity was only observed in the rhesus macaques and not in cynomolgus macaques. The virus was quantified in tracheal, nasal, and anal swabs, and in blood samples by qRT-PCR, and showed high similarity between the two species. Immunoglobulins were detected by various enzyme-linked immunosorbent assays (ELISAs) and showed seroconversion in all animals by day 10 post-infection. The cytokine responses were highly comparable between species and computed tomography (CT) imaging revealed pulmonary lesions in all animals. Consequently, we concluded that both rhesus and cynomolgus macaques represent valid models for evaluation of COVID-19 vaccine and antiviral candidates in a preclinical setting.

Human Identical Sequences of SARS-CoV-2 Promote Clinical Progression of COVID-19 by Upregulating Hyaluronan via NamiRNA-Enhancer Network (preprint)

The COVID-19 pandemic is a widespread and deadly public health crisis. The pathogen SARS-CoV-2 replicates in the lower respiratory tract and causes fatal pneumonia. Although tremendous efforts have been put into investigating the pathogeny of SARS-CoV-2, the underlying mechanism of how SARS-CoV-2 interacts with its host is largely unexplored. Here, by comparing the genomic sequences of SARS-CoV-2 and human, we identified five fully conserved elements in SARS-CoV-2 genome, which were termed as "human identical sequences (HIS)". HIS are also recognized in both SARS-CoV and MERS-CoV genome. Meanwhile, HIS-SARS-CoV-2 are highly conserved in the primate. Mechanically, HIS-SARS-CoV-2 RNA directly binds to the targeted loci in human genome and further interacts with host enhancers to activate the expression of adjacent and distant genes, including cytokines gene and angiotensin converting enzyme II (ACE2), a well-known cell entry receptor of SARS-CoV-2, and hyaluronan synthase 2 (HAS2), which further increases hyaluronan formation. Noteworthily, hyaluronan level in plasma of COVID-19 patients is tightly correlated with severity and high risk for acute respiratory distress syndrome (ARDS) and may act as a predictor for the progression of COVID-19. HIS antagomirs, which downregulate hyaluronan level effectively, and 4-Methylumbelliferone (MU), an inhibitor of hyaluronan synthesis, are potential drugs to relieve the ARDS related ground-glass pattern in lung for COVID-19 treatment. Our results revealed that unprecedented HIS elements of SARS-CoV-2 contribute to the cytokine storm and ARDS in COVID-19 patients. Thus, blocking HIS-involved activating processes or hyaluronan synthesis directly by 4-MU may be effective strategies to alleviate COVID-19 progression.

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research (preprint)

Motivation: In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, host-pathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Results: Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19. Availability: https://neo4covid19.ncats.io . Keywords: SARS-CoV-2, COVID-19, network pharmacology, graph database, Neo4j, data integration, drug repositioning

Modelling SARS-CoV-2 transmission in a UK university setting (preprint)

Background: The higher education system in the United Kingdom comprises a large student population. Around 40% of school leavers attend university and individual universities generally host thousands of students each academic year. In the setting of the COVID-19 pandemic, bringing together these student communities presents questions regarding the strength of interventions required to control transmission. Prior modelling analysis of SARS-CoV-2 transmission within universities has usually adopted a compartmental modelling approach, whose projections suggest an almost inevitable likelihood of outbreaks. Methods: We constructed a network-based model to capture the interactions of a student population in different settings (housing, social and study). For a representative campus-based university, we ran a susceptible-latent-infectious-recovered type epidemic process, parameterised according to available estimates for SARS-CoV-2. Over the course of a single academic term, we investigated the impact on infection control of adherence to (or effectiveness of) isolation, test and trace measures, the additional use of room isolation as an intervention and supplementary mass testing. Results: Incorporating uncertainty in the fraction of cases that are asymptomatic and their associated infectivity, in the absence of interventions our model estimated that 16% (2% - 38%) of the student population could be infected during the autumn term. In contrast, with full adherence to isolation measures and engagement with test-and-trace, predictions of the cumulative infection count were lower, 1.4% (0.4% - 5%). Irrespective of the adherence to isolation measures, on average a higher proportion of students resident on-campus became infected compared with students resident off-campus. Widespread adherence of interventions led to reductions in the average fraction of time those individuals adhering to measures were expected to be isolated, with room isolation as an additional intervention generating minimal benefits. The model found that a one-off instance of mass testing would not drastically reduce the term-long case load or end-of-term prevalence, but regular weekly or fortnightly testing could reduce both measures by more than 50% (compared to having no mass testing). Conclusions: Our findings suggest SARS-CoV-2 may readily transmit amongst a student population within a university setting if there is limited adherence to nonpharmaceutical interventions and there are delays present in receiving test results. Following isolation guidance and effective contact tracing both curbed transmission and reduced the expected time an adhering student would spend in isolation. Additionally, widespread adherence throughout the term suppresses the amount of unwitting asymptomatic transmission to family and community members in the students' domicile regions at the end of term.

Precautionary breaks: planned, limited duration circuit breaks to control the prevalence of COVID-19 (preprint)

The COVID-19 pandemic in the UK has been characterised by periods of exponential growth and decline, as different non-pharmaceutical interventions (NPIs) are brought into play. During the early uncontrolled phase of the outbreak (early March 2020) there was a period of prolonged exponential growth with epidemiological observations such as hospitalisation doubling every 3-4 days (growth rate r{approx}0.2). The enforcement of strict lockdown measures led to a noticeable decline in all epidemic quantities (r{approx}-0.06) that slowed during the summer as control measures were relaxed (r{approx}-0.02). Since August, infections, hospitalisations and deaths have been rising (precise estimation of the cur-rent growth rate is difficult due to extreme regional heterogeneity and temporal lags between the different epidemiological observations) and various NPIs have been applied locally throughout the UK in response. Controlling any rise in infection is a compromise between public health and societal costs, with more stringent NPIs reducing cases but damaging the economy and restricting freedoms. Currently, NPI imposition is made in response to the epidemiological state, are of indefinite length and are often imposed at short notice, greatly increasing the negative impact. An alternative approach is to consider planned, limited duration periods of strict NPIs aiming to purposefully reduce prevalence before such emergency NPIs are required. These 'precautionary breaks' may offer a means of keeping control of the epidemic, while their fixed duration and the forewarning may limit their society impact. Here, using simple analysis and age-structured models matched to the unfolding UK epidemic, we investigate the action of precautionary breaks. In particular we consider their impact on the prevalence of infection, as well as the total number of predicted hospitalisations and deaths. We find that precautionary breaks provide the biggest gains when the growth rate is low, but offer a much needed brake on increasing infection when the growth rate is higher, potentially allowing other measures (such as contact tracing)to regain control.

Modelling optimal vaccination strategy for SARS-CoV-2. (preprint)

The COVID-19 outbreak has highlighted our vulnerability to novel infections. Faced with this threat and no effective treatment, most countries adopted some form of enforced social distancing (lockdown) to reduce transmission - in most cases successfully reducing the reproductive number,R, below one. However, given the large pool of susceptible individuals that remain, complete relaxation of controls is likely to generate a substantial second wave. Vaccination remains the only foreseeable means of both containing the infection and returning to normal interactions and behaviour. Here, we consider the optimal targeting of vaccination with the aim of minimising future deaths or quality adjusted life year (QALY) losses. We show that, for a range of assumptions on the action and efficacy of the vaccine, targeting older age groups first is optimal and can avoid a second wave if the vaccine prevents transmission as well as disease.

Predictions of COVID-19 dynamics in the UK: short-term forecasting and analysis of potential exit strategies (preprint)

BackgroundEfforts to suppress transmission of SARS-CoV-2 in the UK have seen non-pharmaceutical interventions being invoked. The most severe measures to date include all restaurants, pubs and cafes being ordered to close on 20th March, followed by a "stay at home" order on the 23rd March and the closure of all non-essential retail outlets for an indefinite period. Government agencies are presently analysing how best to develop an exit strategy from these measures and to determine how the epidemic may progress once measures are lifted. Mathematical models are currently providing short and long term forecasts regarding the future course of the COVID-19 outbreak in the UK to support evidence-based policymaking. MethodsWe present a deterministic, age-structured transmission model that uses real-time data on confirmed cases requiring hospital care and mortality to provide up-to-date predictions on epidemic spread in ten regions of the UK. The model captures a range of age-dependent heterogeneities, reduced transmission from asymptomatic infections and produces a good fit to the key epidemic features over time. We simulated a suite of scenarios to assess the impact of differing approaches to relaxing social distancing measures from 7th May 2020, on the estimated number of patients requiring inpatient and critical care treatment, and deaths. With regard to future epidemic outcomes, we investigated the impact of reducing compliance, ongoing shielding of elder age groups, reapplying stringent social distancing measures using region based triggers and the role of asymptomatic transmission. FindingsWe find that significant relaxation of social distancing measures from 7th May onwards can lead to a rapid resurgence of COVID-19 disease and the health system being quickly overwhelmed by a sizeable, second epidemic wave. In all considered age-shielding based strategies, we projected serious demand on critical care resources during the course of the pandemic. The reintroduction and release of strict measures on a regional basis, based on ICU bed occupancy, results in a long epidemic tail, until the second half of 2021, but ensures that the health service is protected by reintroducing social distancing measures for all individuals in a region when required. DiscussionOur work confirms the effectiveness of stringent non-pharmaceutical measures in March 2020 to suppress the epidemic. It also provides strong evidence to support the need for a cautious, measured approach to relaxation of lockdown measures, to protect the most vulnerable members of society and support the health service through subduing demand on hospital beds, in particular bed occupancy in intensive care units.