Growth, reproduction numbers and factors affecting the spread of SARS-CoV-2 novel variants of concern in the UK from October 2020 to July 2021: a modelling analysis

ObjectivesImportations of novel variants of concern (VOC), particularly B.1.617.2, have become the impetus behind recent outbreaks of SARS-CoV-2. Concerns around the impact on vaccine effectiveness, transmissibility and severity are now driving the public health response to these variants. This paper analyses the patterns of growth in hospitalisations and confirmed cases for novel VOCs by age groups, geography and ethnicity in the context of changing behaviour, non-pharmaceutical interventions (NPIs) and the UK vaccination programme. We seek to highlight where strategies have been effective and periods that have facilitated the establishment of new variants.DesignWe have algorithmically linked the most complete testing and hospitalisation data in England to create a data set of confirmed infections and hospitalisations by SARS-CoV-2 genomic variant. We have used these linked data sets to analyse temporal, geographic and demographic distinctions.Setting and participantsThe setting is England from October 2020 to July 2021. Participants included all COVID-19 tests that included RT-PCR CT gene target data or underwent sequencing and hospitalisations that could be linked to these tests.MethodsTo calculate the instantaneous growth rate for VOCs we have developed a generalised additive model fit to multiple splines and varying day of the week effects. We have further modelled the instantaneous reproduction number Rt for the B.1.1.7 and B.1.617.2 variants and included a doubly interval censored model to temporally adjust the confirmed variant cases.ResultsWe observed a clear replacement of the predominant B.1.1.7 by the B.1.617.2 variant without observing sustained exponential growth in other novel variants. Modelled exponential growth of RT PCR gene target triple-positive cases was initially detected in the youngest age groups, although we now observe across all ages a very small doubling time of 10.7 (95% CI 9.1 to 13.2) days and 8 (95% CI 6.9 to 9.1) days for cases and hospitalisations, respectively. We observe that growth in RT PCR gene target triple-positive cases was first detected in the Indian ethnicity group in late February, with a peak of 0.06 (95% CI 0.07 to 0.05) in the instantaneous growth rate, but is now maintained by the white ethnicity groups, observing a doubling time of 6.8 (95% CI 4.9 to 11) days. Rt analysis indicates a reproduction number advantage of 0.45 for B.1.617.2 relative to B.1.1.7, with the Rt value peaking at 1.85 for B.1.617.2.ConclusionsOur results illustrate a clear transmission advantage for the B.1.617.2 variant and the growth in hospitalisations illustrates that this variant is able to maintain exponential growth within age groups that are largely doubly vaccinated. There are concerning signs of intermittent growth in the B.1.351 variant, reaching a 28-day doubling time peak in March 2021, although this variant is presently not showing any evidence of a transmission advantage over B.1.617.2. Step 1b of the UK national lockdown easing was sufficient to precipitate exponential growth in B.1.617.2 cases for most regions and younger adult age groups. The final stages of NPI easing appeared to have a negligible impact on the growth of B.1.617.2 with every region experiencing sustained exponential growth from step 2. Nonetheless, early targeted local NPIs appeared to markedly reduced growth of B.1.617.2. Later localised interventions, at a time of higher prevalence and greater geographic dispersion of this variant, appeared to have a negligible impact on growth.

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Interim estimates of increased transmissibility, growth rate, and reproduction number of the Covid-19 B.1.617.2 variant of concern in the United Kingdom

10.1101/2021.06.03.21258293 ◽

2021 ◽

Author(s):

John S Dagpunar

Keyword(s):

Growth Rate ◽

Simple Model ◽

Exponential Growth ◽

Reproduction Number ◽

Actual Distribution ◽

Sanger Institute ◽

The United Kingdom ◽

Point Estimates ◽

Reproduction Numbers ◽

The Uk

This paper relates to data from the Wellcome Sanger Institute, UK, regarding Covid-19 genomic surveillance. We use a simple model to give point estimates of the effective reproduction numbers of the B.1.617.2 and B.1.1.7 lineages in England, from sequenced data as at 15 May 2021. Comparison with the estimated reproduction number of B.1.1.7 enables an estimate of the increased transmissibility of B.1.617.2. We conclude that it is almost certain that there is increased transmissibility that will rapidly lead to B.1.617.2 becoming the prevailing variant in the UK. The derived estimates of increased transmissibility have uncertainty relating to the actual distribution of the generation interval, but they do point, under present conditions of vaccination coverage and NPIs, to exponential growth of positive cases.

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SARS-CoV-2 lineage dynamics in England from January to March 2021 inferred from representative community samples

10.1101/2021.05.08.21256867 ◽

2021 ◽

Author(s):

Oliver Eales ◽

Andrew Page ◽

Sonja N. Tang ◽

Caroline E. Walters ◽

Haowei Wang ◽

...

Keyword(s):

Reproduction Number ◽

Sequence Data ◽

Community Sample ◽

Age Groups ◽

Health Interventions ◽

Wild Type ◽

Rt Pcr ◽

Community Surveys ◽

Community Transmission ◽

Middle Aged Adults

Genomic surveillance for SARS-CoV-2 lineages informs our understanding of possible future changes in transmissibility and vaccine efficacy. However, small changes in the frequency of one lineage over another are often difficult to interpret because surveillance samples are obtained from a variety of sources. Here, we describe lineage dynamics and phylogenetic relationships using sequences obtained from a random community sample who provided a throat and nose swab for rt-PCR during the first three months of 2021 as part of the REal-time Assessment of Community Transmission-1 (REACT-1) study. Overall, diversity decreased during the first quarter of 2021, with the B.1.1.7 lineage (first identified in Kent) predominant, driven by a 0.3 unit higher reproduction number over the prior wild type. During January, positive samples were more likely B.1.1.7 in younger and middle-aged adults (aged 18 to 54) than in other age groups. Although individuals infected with the B.1.1.7 lineage were no more likely to report one or more classic COVID-19 symptoms compared to those infected with wild type, they were more likely to be antibody positive 6 weeks after infection. Viral load was higher in B.1.1.7 infection as measured by cycle threshold (Ct) values, but did not account for the increased rate of testing positive for antibodies. The presence of infections with non-imported B.1.351 lineage (first identified in South Africa) during January, but not during February or March, suggests initial establishment in the community followed by fade-out. However, this occurred during a period of stringent social distancing and targeted public health interventions and does not immediately imply similar lineages could not become established in the future. Sequence data from representative community surveys such as REACT-1 can augment routine genomic surveillance.

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Early Phylogenetic Estimate Of The Effective Reproduction Number Of 2019-nCoV

10.1101/2020.02.19.20024851 ◽

2020 ◽

Cited By ~ 6

Author(s):

Alessia Lai ◽

Annalisa Bergna ◽

Carla Acciarri ◽

Massimo Galli ◽

Gianguglielmo Zehender

Keyword(s):

Exponential Growth ◽

Doubling Time ◽

Evolutionary Dynamics ◽

Evolutionary Rate ◽

Reproduction Number ◽

R Value ◽

Phylogenetic Estimate ◽

Novel Coronavirus ◽

Birth Death

ABSTRACTTo reconstruct the evolutionary dynamics of the 2019 novel coronavirus, 52 2019-nCOV genomes available on 04 February 2020 at GISAID were analysed.The two models used to estimate the reproduction number (coalescent-based exponential growth and a birth-death skyline method) indicated an estimated mean evolutionary rate of 7.8 × 10−4 subs/site/year (range 1.1×10−4–15×10−4).The estimated R value was 2.6 (range 2.1-5.1), and increased from 0.8 to 2.4 in December 2019. The estimated mean doubling time of the epidemic was between 3.6 and 4.1 days.This study proves the usefulness of phylogeny in supporting the surveillance of emerging new infections even as the epidemic is growing.

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

10.1101/2020.10.13.20211813 ◽

2020 ◽

Cited By ~ 5

Author(s):

Matt J. Keeling ◽

Glen Guyver-Fletcher ◽

Alex Holmes ◽

Louise Dyson ◽

Michael J. Tildesley ◽

...

Keyword(s):

Growth Rate ◽

Exponential Growth ◽

Negative Impact ◽

Control Measures ◽

Contact Tracing ◽

Societal Costs ◽

Alternative Approach ◽

Age Structured ◽

The Uk ◽

Limited Duration

AbstractThe 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 ≈ 0.2). The enforcement of strict lockdown measures led to a noticeable decline in all epidemic quantities (r ≈ −0.06) that slowed during the summer as control measures were relaxed (r ≈ −0.02). Since August, infections, hospitalisations and deaths have been rising (precise estimation of the current 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.

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Epidemic Potential of COVID-19 in Omsk Region during Anti-Epidemic Measures

Problems of Particularly Dangerous Infections ◽

10.21055/0370-1069-2020-3-36-42 ◽

2020 ◽

pp. 36-42

Author(s):

A. I. Blokh ◽

N. A. Pen’evskaya ◽

N. V. Rudakov ◽

I. I. Lazarev ◽

O. A. Mikhailova ◽

...

Keyword(s):

Growth Rate ◽

Exponential Growth ◽

Reproduction Number ◽

Herd Immunity ◽

Case Fatality ◽

Community Acquired Pneumonia ◽

Cumulative Number ◽

Exponential Growth Rate ◽

Epidemic Size ◽

Threshold Data

Aim. To study the spread of COVID-19 among the population of the Omsk Region during 24 weeks of the epidemic on the background of anti-epidemic measures.Materials and methods. A descriptive epidemiological study was carried out based on publically available data и data from the Center for Hygiene and Epidemiology in the Omsk Region on the official registration and epidemiological investigation of detected COVID-19 cases in the Omsk Region for the period from March 27 to September 10, 2020. To assess the potential of COVID-19 to spread, the following indicators were calculated: exponential growth rate (r), basic reproduction number (R0), effective reproduction number (Rt), expected natural epidemic size and herd immunity threshold. Data processing was performed using MS Excel 2010. The cartogram was built using the QGIS 3.12-Bukuresti application in the EPSG: 3576 coordinate system.Results and discussion. For the period from March 27 to September 10, 2020, a total of 9779 cases of COVID-19 were registered in the Omsk Region, the cumulative incidence was 507,6 per 100000 (95 % CI 497,5÷517,6), the case-fatality rate for completed cases was 2.9 %, for identified cases – 2.4 %. The most active spread of COVID-19 was noted in Omsk and 4 out of 32 districts of the region (Moskalensky, Azov German National, Mariyanovsky, Novovarshavsky). During the ongoing anti-epidemic measures, the exponential growth rate of the cumulative number of COVID-19 cases was 4.5 % per day, R0 – 1.4–1.5, Rt – 1.10, herd immunity threshold – 28.6 %. The expected size of the epidemic in case of sustained anti-epidemic measures can reach 58.0 % of the recovered population. A decrease in the number of detected virus carriers, incomplete detection of COVID-19 among patients with community-acquired pneumonia introduced additional risks for the latent spread of infection and complications of the epidemic situation. Maintaining restrictive measures and increasing the proportion of the immune population (over 28.6 %) may significantly reduce the risks of increasing the spread of COVID-19 in the Omsk Region.

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TRACKING THE MUTANT: FORECASTING AND NOWCASTING COVID-19 IN THE UK IN 2021

National Institute Economic Review ◽

10.1017/nie.2021.12 ◽

2021 ◽

Vol 256 ◽

pp. 110-126

Author(s):

Andrew Harvey ◽

Paul Kattuman ◽

Craig Thamotheram

Keyword(s):

Growth Rate ◽

Time Series ◽

Reproduction Number ◽

Time Series Models ◽

Daily Number ◽

New Class ◽

Bivariate Models ◽

The Uk ◽

Different Sources

A new class of time series models is used to track the progress of the COVID-19 epidemic in the UK in early 2021. Models are fitted to England and the regions, as well as to the UK as a whole. The growth rate of the daily number of cases and the instantaneous reproduction number are computed regularly and compared with those produced by SAGE. The results from figures published each day are compared with results based on figures by specimen date, which may be more accurate but are subject to substantial revisions. It is then shown how data from the two different sources can be combined in bivariate models.

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EFECTO DE LA SALINIDAD SOBRE EL CRECIMIENTO POBLACIONAL Y EL RENDIMIENTO DEL ROTIFERO BRACHIONUS PLICATILIS (CEPA CIENAGA GRANDE DE SANTA MARTA)

Bulletin of Marine and Coastal Research ◽

10.25268/bimc.invemar.1993.22.0.411 ◽

2016 ◽

Vol 22 ◽

Author(s):

Adriana Vallejo I. ◽

Federico Newmark ◽

María Mercedes Criaies

Keyword(s):

Growth Rate ◽

Population Growth ◽

Laboratory Experiments ◽

Doubling Time ◽

Brachionus Plicatilis ◽

Growth And Yield ◽

Instantaneous Growth Rate ◽

Instantaneous Growth ◽

Adaptive Capability

The effect of salinity on the population growth and yield of a strain of Brachionus plicatiiis (O.F. Muller, 1786) was determined by two laboratory experiments. The rotifer B. plicatiiis was isolated from the plankton of the Ciénaga Grande de Santa Marta, Colombia, and cultivated in laboratory under different salinity conditions. The local microalgae Chiorella sp., also isolated in laboratory, was used as food for the rotifers. Salinity values ranged from 3 to 24 o/oo in the first experiment and 30 to 45 o/oo in the second. It was found that these rotifers grow and exhibit a" high adaptive capability when cultured at salinities ranging from 16 to 40o/oo. The rotifer curves of population growth were negative at salinities under 16 o/oo. At salinities of 30, 36, and 40 o/oo the maximum densities were similar in the ninth day. Although maximum densities were somewhat lower at 45 o/oo salinity, no significant statistical differences were found. Maximum values of density (d), yield (y), instantaneous growth rate (k) and doubling time (td) corresponded to a salinity of 36o/oo.

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Loss of smell and taste in combination with other symptoms is a strong predictor of COVID-19 infection

10.1101/2020.04.05.20048421 ◽

2020 ◽

Cited By ~ 37

Author(s):

Cristina Menni ◽

Ana M Valdes ◽

Maxim B Freidin ◽

Sajaysurya Ganesh ◽

Julia S El-Sayed Moustafa ◽

...

Keyword(s):

Abdominal Pain ◽

General Population ◽

Strong Predictor ◽

Age Groups ◽

List Type ◽

Rt Pcr ◽

Presenting Symptoms ◽

Persistent Cough ◽

Loss Of Appetite ◽

The Uk

AbstractImportanceA strategy for preventing further spread of the ongoing COVID-19 epidemic is to detect infections and isolate infected individuals without the need of extensive bio-specimen testing.ObjectivesHere we investigate the prevalence of loss of smell and taste among COVID-19 diagnosed individuals and we identify the combination of symptoms, besides loss of smell and taste, most likely to correspond to a positive COVID-19 diagnosis in non-severe cases.DesignCommunity survey.Setting and ParticipantsSubscribers of RADAR COVID-19, an app that was launched for use among the UK general population asking about COVID-19 symptoms.Main ExposureLoss of smell and taste.Main Outcome MeasuresCOVID-19.ResultsBetween 24 and 29 March 2020, 1,573,103 individuals reported their symptoms via the app; 26% reported suffering from one or more symptoms of COVID-19. Of those, n=1702 reported having had a RT-PCR COVID-19 test and gave full report on symptoms including loss of smell and taste; 579 were positive and 1123 negative. In this subset, we find that loss of smell and taste were present in 59% of COVID-19 positive individuals compared to 18% of those negative to the test, yielding an odds ratio (OR) of COVID-19 diagnosis of OR[95%CI]=6.59[5.25; 8.27], P= 1.90×10−59. We also find that a combination of loss of smell and taste, fever, persistent cough, fatigue, diarrhoea, abdominal pain and loss of appetite is predictive of COVID-19 positive test with sensitivity 0.54[0.44; 0.63], specificity 0.86[0.80; 0.90], ROC-AUC 0.77[0.72; 0.82] in the test set, and cross-validation ROC-AUC 0.75[0.72; 0.77]. When applied to the 410,598 individuals reporting symptoms but not formally tested, our model predicted that 13.06%[12.97%;13.15] of these might have been already infected by the virus.Conclusions and RelevanceOur study suggests that loss of taste and smell is a strong predictor of having been infected by the COVID-19 virus. Also, the combination of symptoms that could be used to identify and isolate individuals includes anosmia, fever, persistent cough, diarrhoea, fatigue, abdominal pain and loss of appetite. This is particularly relevant to healthcare and other key workers in constant contact with the public who have not yet been tested for COVID-19.Key pointsWhat is already known on this topicThe spread of COVID-19 can be reduced by identifying and isolating infected individuals but it is not possible to test everyone and priority has been given in most countries to individuals presenting symptoms of the disease.COVID-19 symptoms, such as fever, cough, aches, fatigue are common in many other viral infectionsThere is therefore a need to identify symptom combinations that can rightly pinpoint to infected individualsWhat this study addsAmong individuals showing symptoms severe enough to be given a COVID-19 RT-PCR test in the UK the prevalence of loss of smell (anosmia) was 3-fold higher (59%) in those positive to the test than among those negative to the test (18%).We developed a mathematical model combining symptoms to predict individuals likely to be COVID-19 positive and applied this to over 400,000 individuals in the general population presenting some of the COVID-19 symptoms.We find that ∼13% of those presenting symptoms are likely to have or have had a COVID-19 infection. The proportion was slightly higher in women than in men but is comparable in all age groups, and corresponds to 3.4% of those who filled the app report.

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Estimation of exponential growth rate and basic reproduction number of the coronavirus disease 2019 (COVID-19) in Africa

Infectious Diseases of Poverty ◽

10.1186/s40249-020-00718-y ◽

2020 ◽

Vol 9 (1) ◽

Cited By ~ 6

Author(s):

Salihu S. Musa ◽

Shi Zhao ◽

Maggie H. Wang ◽

Abdurrazaq G. Habib ◽

Umar T. Mustapha ◽

...

Keyword(s):

Growth Rate ◽

Basic Reproduction Number ◽

Exponential Growth ◽

Reproduction Number ◽

Exponential Growth Rate

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Estimation of the basic reproduction number (R0) for the novel coronavirus disease in Sri Lanka

Virology Journal ◽

10.1186/s12985-020-01411-0 ◽

2020 ◽

Vol 17 (1) ◽

Author(s):

Samath Dharmaratne ◽

Supun Sudaraka ◽

Ishanya Abeyagunawardena ◽

Kasun Manchanayake ◽

Mahen Kothalawala ◽

...

Keyword(s):

Growth Rate ◽

Maximum Likelihood ◽

Sri Lanka ◽

Basic Reproduction Number ◽

Exponential Growth ◽

Reproduction Number ◽

Likelihood Method ◽

Exponential Growth Rate ◽

Rate Method ◽

The Basic Reproduction Number

Abstract Background The basic reproduction number (R0) is the number of cases directly caused by an infected individual throughout his infectious period. R0 is used to determine the ability of a disease to spread within a given population. The reproduction number (R) represents the transmissibility of a disease. Objectives We aimed to calculate the R0 of Coronavirus disease-2019 (COVID-19) in Sri Lanka and to describe the variation of R, with its implications to the prevention and control of the disease. Methods Data was obtained from daily situation reports of the Epidemiology Unit, Sri Lanka and a compartmental model was used to calculate the R0 using estimated model parameters. This value was corroborated by using two more methods, the exponential growth rate method and maximum likelihood method to obtain a better estimate for R0. The variation of R was illustrated using a Bayesian statistical inference-based method. Results The R0 calculated by the first model was 1.02 [confidence interval (CI) of 0.75–1.29] with a root mean squared error of 7.72. The exponential growth rate method and the maximum likelihood estimation method yielded an R0 of 0.93 (CI of 0.77–1.10) and a R0 of 1.23 (CI of 0.94–1.57) respectively. The variation of R ranged from 0.69 to 2.20. Conclusion The estimated R0 for COVID-19 in Sri Lanka, calculated by three different methods, falls between 0.93 and 1.23, and the transmissibility R has reduced, indicating that measures implemented have achieved a good control of disease.

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