Epidemiological drivers of transmissibility and severity of SARS-CoV-2 in England (preprint)

As the SARS-CoV-2 pandemic progressed, distinct variants emerged and dominated in England. These variants, Wildtype, Alpha, Delta, and Omicron were characterized by variations in transmissibility and severity. We used a robust mathematical model and Bayesian inference framework to analyse epidemiological surveillance data from England. We quantified the impact of non-pharmaceutical interventions (NPIs), therapeutics, and vaccination on virus transmission and severity. Each successive variant had a higher intrinsic transmissibility. Omicron (BA.1) had the highest basic reproduction number at 8.1 (95% credible interval (CrI) 6.8-9.3). Varying levels of NPIs were crucial in controlling virus transmission until population immunity accumulated. Immune escape properties of Omicron decreased effective levels of protection in the population by a third. Furthermore, in contrast to previous studies, we found Alpha had the highest basic infection fatality ratio (2.8%, 95% CrI 2.3-3.2), followed by Delta (2.0%, 95% CrI 1.5-2.4), Wildtype (1.2%, 95% CrI 1.0-1.3), and Omicron (0.6%, 95% CrI 0.4-0.8). Our findings highlight the importance of continued surveillance. Long-term strategies for monitoring and maintaining effective immunity against SARS-CoV-2 are critical to inform the role of NPIs to effectively manage future variants with potentially higher intrinsic transmissibility and severe outcomes.

Quantifying the impact of delaying the second COVID-19 vaccine dose in England: a mathematical modelling study (preprint)

Background: The UK was the first country to start national COVID-19 vaccination programmes, initially administering doses 3-weeks apart. However, early evidence of high vaccine effectiveness after the first dose and the emergence of the Alpha variant prompted the UK to extend the interval between doses to 12-weeks. In this study, we quantify the impact of delaying the second vaccine dose on the epidemic in England. Methods: We used a previously described model of SARS-CoV-2 transmission and calibrated the model to English surveillance data including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data using a Bayesian evidence synthesis framework. We modelled and compared the epidemic trajectory assuming that vaccine doses were administered 3-weeks apart against the real vaccine roll-out schedule. We estimated and compared the resulting number of daily infections, hospital admissions, and deaths. A range of scenarios spanning a range of vaccine effectiveness and waning assumptions were investigated. Findings: We estimate that delaying the interval between the first and second COVID-19 vaccine doses from 3- to 12-weeks prevented an average 64,000 COVID-19 hospital admissions and 9,400 deaths between 8th December 2020 and 13th September 2021. Similarly, we estimate that the 3-week strategy would have resulted in more infections and deaths compared to the 12-week strategy. Across all sensitivity analyses the 3-week strategy resulted in a greater number of hospital admissions. Interpretation: England's delayed second dose vaccination strategy was informed by early real-world vaccine effectiveness data and a careful assessment of the trade-offs in the context of limited vaccine supplies in a growing epidemic. Our study shows that rapidly providing partial vaccine-induced protection to a larger proportion of the population was successful in reducing the burden of COVID-19 hospitalisations and deaths. There is benefit in carefully considering and adapting guidelines in light of new emerging evidence and the population in question. Funding: National Institute for Health Research, UK Medical Research Council, Jameel Institute, Wellcome Trust, and UK Foreign, Commonwealth and Development Office, National Health and Medical Research Council.

COVID-19 in Japan: insights from the first three months of the epidemic (preprint)

Background Understanding the characteristics and natural history of novel pathogens is crucial to inform successful control measures. Japan was one of the first affected countries in the COVID-19 pandemic reporting their first case on 14 January 2020. Interventions including airport screening, contact tracing, and cluster investigations were quickly implemented. Here we present insights from the first 3 months of the epidemic in Japan based on detailed case data. Methods We conducted descriptive analyses based on information systematically extracted from individual case reports from 13 January to 31 March 2020 including patient demographics, date of report and symptom onset, symptom progression, travel history, and contact type. We analysed symptom progression and estimated the time-varying reproduction number, Rt, correcting for epidemic growth using an established Bayesian framework. Key delays and the age-specific probability of transmission were estimated using data on exposures and transmission pairs. Results The corrected fitted mean onset-to-reporting delay after the peak was 4 days (standard deviation: {+/-}2 days). Early transmission was driven primarily by returning travellers with Rt peaking at 2.4 (95%CrI:1.6, 3.3) nationally. In the final week of the trusted period, Rt accounting for importations diverged from overall Rt at 1.1 (95% CrI: 1.0, 1.2) compared to 1.5 (95% CrI: 1.3, 1.6) respectively. Household (39.0%) and workplace (11.6%) exposures were the most frequently reported potential source of infection. The estimated probability of transmission was assortative by age. Across all age groups, cases most frequently onset with cough, fever, and fatigue. There were no reported cases of patients <20 years old developing pneumonia or severe respiratory symptoms. Conclusions Information collected in the early phases of an outbreak are important in characterising any novel pathogen. Timely recognition of key symptoms and high-risk settings for transmission can help to inform response strategies. The data analysed here were the result of robust and timely investigations and demonstrate the improvements to epidemic control as a result of such surveillance.

Non-pharmaceutical interventions, vaccination and the Delta variant: epidemiological insights from modelling England's COVID-19 roadmap out of lockdown (preprint)

BackgroundEnglands COVID-19 "roadmap out of lockdown" set out the timeline and conditions for the stepwise lifting of non-pharmaceutical interventions (NPIs) as vaccination roll-out continued. Here we assess the roadmap, the impact of the Delta variant, and potential future epidemic trajectories. MethodsWe extended a model of SARS-CoV-2 transmission to incorporate vaccination and multi-strain dynamics to explicitly capture the emergence of the Delta variant. We calibrated the model to English surveillance data using a Bayesian evidence synthesis framework, then modelled the potential trajectory of the epidemic for a range of different schedules for relaxing NPIs. FindingsThe roadmap was successful in offsetting the increased transmission resulting from lifting NPIs with increasing population immunity through vaccination. However due to the emergence of Delta, with an estimated transmission advantage of 73% (95%CrI: 68-79) over Alpha, fully lifting NPIs on 21 June 2021 as originally planned may have led to 3,400 (95%CrI: 1,300-4,400) peak daily hospital admissions under our central parameter scenario. Delaying until 19 July reduced peak hospitalisations by three-fold to 1,400 (95%CrI: 700-1,500) per day. There was substantial uncertainty in the epidemic trajectory, with particular sensitivity to estimates of vaccine effectiveness and the intrinsic transmissibility of Delta. InterpretationOur findings show that the risk of a large wave of COVID hospitalisations resulting from lifting NPIs can be substantially mitigated if the timing of NPI relaxation is carefully balanced against vaccination coverage. However, with Delta, it may not be possible to fully lift NPIs without a third wave of hospitalisations and deaths, even if vaccination coverage is high. Variants of concern, their transmissibility, vaccine uptake, and vaccine effectiveness must be carefully monitored as countries relax pandemic control measures. FundingNational Institute for Health Research, UK Medical Research Council, Wellcome Trust, UK Foreign, Commonwealth & Development Office. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed up to 23 July 2021 with no language restrictions using the search terms: (COVID-19 or SARS-CoV-2 or 2019-nCoV or "novel coronavirus") AND (vaccine or vaccination) AND ("non pharmaceutical interventions" OR "non-pharmaceutical interventions) AND (model*). We found nine studies that analysed the relaxation of controls with vaccination roll-out. However, none explicitly analysed real-world evidence balancing lifting of interventions, vaccination, and emergence of the Delta variant. Added value of this studyOur data synthesis approach combines real-world evidence from multiple data sources to retrospectively evaluate how relaxation of COVID-19 measures have been balanced with vaccination roll-out. We explicitly capture the emergence of the Delta variant, its transmissibility over Alpha, and quantify its impact on the roadmap. We show the benefits of maintaining NPIs whilst vaccine coverage continues to increase and capture key uncertainties in the epidemic trajectory after NPIs are lifted. Implications of all the available evidenceOur study shows that lifting interventions must be balanced carefully and cautiously with vaccine roll-out. In the presence of a new, highly transmissible variant, vaccination alone may not be enough to control COVID-19. Careful monitoring of vaccine uptake, effectiveness, variants, and changes in contact patterns as restrictions are lifted will be critical in any exit strategy.

Global predictions of short- to medium-term COVID-19 transmission trends : a retrospective assessment (preprint)

From 8th March to 29th November 2020, we produced weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for 81 countries with evidence of sustained transmission. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. We evaluated the robustness of the forecasts using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3\% and 45.6\% of the observations lying in the 50\% Credible Interval in 1-week and 4-week ahead forecasts respectively. We could accurately characterise the overall phase of the epidemic up to 4-weeks ahead in 84.9\% of country-days. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures.

Global predictions of short- to medium-term COVID-19 transmission trends : a retrospective assessment (preprint)

BackgroundAs of July 2021, more than 180,000,000 cases of COVID-19 have been reported across the world, with more than 4 million deaths. Mathematical modelling and forecasting efforts have been widely used to inform policy-making and to create situational awareness. Methods and FindingsFrom 8th March to 29th November 2020, we produced weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for countries with evidence of sustained transmission. The estimates and forecasts were based on an ensemble model comprising of three models that were calibrated using only the reported number of COVID-19 cases and deaths in each country. We also developed a novel heuristic to combine weekly estimates of transmissibility and potential changes in population immunity due to infection to produce forecasts over a 4-week horizon. We evaluated the robustness of the forecasts using relative error, coverage probability, and comparisons with null models. ConclusionsDuring the 39-week period covered by this study, we produced short- and medium-term forecasts for 81 countries. Both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. We could accurately characterise the overall phase of the epidemic up to 4-weeks ahead in 84.9% of country-days. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax stringent public health measures that were implemented to contain the pandemic.

Using next generation matrices to estimate the proportion of infections that are not detected in an outbreak (preprint)

Contact tracing, where exposed individuals are followed up to break ongoing transmission chains, is a key pillar of outbreak response for infectious disease outbreaks. Unfortunately, these systems are not fully effective, and infections can still go undetected as people may not remember all their contacts or contacts may not be traced successfully. A large proportion of undetected infections suggests poor contact tracing and surveillance systems, which could be a potential area of improvement for a disease response. In this paper, we present a method for estimating the proportion of infections that are not detected during an outbreak. Our method uses next generation matrices that are parameterized by linked contact tracing data and case line-lists. We validate the method using simulated data from an individual-based model and then investigate two case studies: the proportion of undetected infections in the SARS-CoV-2 outbreak in New Zealand during 2020 and the Ebola epidemic in Guinea during 2014. We estimate that only 5.26% of SARS-CoV-2 infections were not detected in New Zealand during 2020 (95% credible interval: 0.243 – 16.0%) but depending on assumptions 39.0% or 37.7% of Ebola infections were not detected in Guinea (95% credible intervals: 1.69 – 87.0% or 1.7 – 80.9%).

The 2020 SARS-CoV-2 epidemic in England: key epidemiological drivers and impact of interventions (preprint)

We fitted a model of SARS-CoV-2 transmission in care homes and the community to regional surveillance data for England. Among control measures implemented, only national lockdown brought the reproduction number below 1 consistently; introduced one week earlier it could have reduced first wave deaths from 36,700 to 15,700 (95%CrI: 8,900–26,800). Improved clinical care reduced the infection fatality ratio from 1.25% (95%CrI: 1.18%–1.33%) to 0.77% (95%CrI: 0.71%–0.84%). The infection fatality ratio was higher in the elderly residing in care homes (35.9%, 95%CrI: 29.1%–43.4%) than those residing in the community (10.4%, 95%CrI: 9.1%–11.5%). England is still far from herd immunity, with regional cumulative infection incidence to 1st December 2020 between 4.8% (95%CrI: 4.4%–5.1%) and 15.4% (95%CrI: 14.9%–15.9%) of the population. One-sentence summary We fit a mathematical model of SARS-CoV-2 transmission to surveillance data from England, to estimate transmissibility, severity, and the impact of interventions

Report 21: Estimating COVID-19 cases and reproduction number in Brazil (preprint)

Brazil is an epicentre for COVID-19 in Latin America. In this report we describe the Brazilian epidemic using three epidemiological measures: the number of infections, the number of deaths and the reproduction number. Our modelling framework requires sufficient death data to estimate trends, and we therefore limit our analysis to 16 states that have experienced a total of more than fifty deaths. The distribution of deaths among states is highly heterogeneous, with 5 states---Sao Paulo, Rio de Janeiro, Ceara, Pernambuco and Amazonas---accounting for 81% of deaths reported to date. In these states, we estimate that the percentage of people that have been infected with SARS-CoV-2 ranges from 3.3% (95% CI: 2.8%-3.7%) in Sao Paulo to 10.6% (95% CI: 8.8%-12.1%) in Amazonas. The reproduction number (a measure of transmission intensity) at the start of the epidemic meant that an infected individual would infect three or four others on average. Following non-pharmaceutical interventions such as school closures and decreases in population mobility, we show that the reproduction number has dropped substantially in each state. However, for all 16 states we study, we estimate with high confidence that the reproduction number remains above 1. A reproduction number above 1 means that the epidemic is not yet controlled and will continue to grow. These trends are in stark contrast to other major COVID-19 epidemics in Europe and Asia where enforced lockdowns have successfully driven the reproduction number below 1. While the Brazilian epidemic is still relatively nascent on a national scale, our results suggest that further action is needed to limit spread and prevent health system overload.

A sub-national analysis of the rate of transmission of COVID-19 in Italy (preprint)

Italy was the first European country to experience sustained local transmission of COVID-19. As of 1st May 2020, the Italian health authorities reported 28,238 deaths nationally. To control the epidemic, the Italian government implemented a suite of non-pharmaceutical interventions (NPIs), including school and university closures, social distancing and full lockdown involving banning of public gatherings and non essential movement. In this report, we model the effect of NPIs on transmission using data on average mobility. We estimate that the average reproduction number (a measure of transmission intensity) is currently below one for all Italian regions, and significantly so for the majority of the regions. Despite the large number of deaths, the proportion of population that has been infected by SARS-CoV-2 (the attack rate) is far from the herd immunity threshold in all Italian regions, with the highest attack rate observed in Lombardy (13.18% [10.66%-16.70%]). Italy is set to relax the currently implemented NPIs from 4th May 2020. Given the control achieved by NPIs, we consider three scenarios for the next 8 weeks: a scenario in which mobility remains the same as during the lockdown, a scenario in which mobility returns to pre-lockdown levels by 20%, and a scenario in which mobility returns to pre-lockdown levels by 40%. The scenarios explored assume that mobility is scaled evenly across all dimensions, that behaviour stays the same as before NPIs were implemented, that no pharmaceutical interventions are introduced, and it does not include transmission reduction from contact tracing, testing and the isolation of confirmed or suspected cases. New interventions, such as enhanced testing and contact tracing are going to be introduced and will likely contribute to reductions in transmission; therefore our estimates should be viewed as pessimistic projections. We find that, in the absence of additional interventions, even a 20% return to pre-lockdown mobility could lead to a resurgence in the number of deaths far greater than experienced in the current wave in several regions. Future increases in the number of deaths will lag behind the increase in transmission intensity and so a second wave will not be immediately apparent from just monitoring of the daily number of deaths. Our results suggest that SARS-CoV-2 transmission as well as mobility should be closely monitored in the next weeks and months. To compensate for the increase in mobility that will occur due to the relaxation of the currently implemented NPIs, adherence to the recommended social distancing measures alongside enhanced community surveillance including swab testing, contact tracing and the early isolation of infections are of paramount importance to reduce the risk of resurgence in transmission.

Estimates of the severity of COVID-19 disease (preprint)

Background: A range of case fatality ratio (CFR) estimates for COVID 19 have been produced that differ substantially in magnitude. Methods: We used individual-case data from mainland China and cases detected outside mainland China to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the CFR by relating the aggregate distribution of cases by dates of onset to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for the demography of the population, and age and location-based under ascertainment. We additionally estimated the CFR from individual linelist data on 1,334 cases identified outside mainland China. We used data on the PCR prevalence in international residents repatriated from China at the end of January 2020 to obtain age-stratified estimates of the infection fatality ratio (IFR). Using data on age stratified severity in a subset of 3,665 cases from China, we estimated the proportion of infections that will likely require hospitalisation. Findings: We estimate the mean duration from onset-of-symptoms to death to be 17.8 days (95% credible interval, crI 16.9,19.2 days) and from onset-of-symptoms to hospital discharge to be 22.6 days (95% crI 21.1,24.4 days). We estimate a crude CFR of 3.67% (95% crI 3.56%,3.80%) in cases from mainland China. Adjusting for demography and under-ascertainment of milder cases in Wuhan relative to the rest of China, we obtain a best estimate of the CFR in China of 1.38% (95% crI 1.23%,1.53%) with substantially higher values in older ages. Our estimate of the CFR from international cases stratified by age (under 60 or 60 and above) are consistent with these estimates from China. We obtain an overall IFR estimate for China of 0.66% (0.39%,1.33%), again with an increasing profile with age. Interpretation: These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and demonstrate a strong age-gradient in risk.