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

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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Early Transmission Dynamics of Novel Coronavirus (COVID-19) in Nigeria

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17093054 ◽

2020 ◽

Vol 17 (9) ◽

pp. 3054 ◽

Cited By ~ 14

Author(s):

Oyelola A. Adegboye ◽

Adeshina I. Adekunle ◽

Ezra Gayawan

Keyword(s):

Doubling Time ◽

Reproduction Number ◽

Infected Individual ◽

Credible Interval ◽

World Health ◽

Time Interval ◽

Serial Interval ◽

Imported Cases ◽

Novel Coronavirus ◽

Health Organization

On 31 December 2019, the World Health Organization (WHO) was notified of a novel coronavirus disease in China that was later named COVID-19. On 11 March 2020, the outbreak of COVID-19 was declared a pandemic. The first instance of the virus in Nigeria was documented on 27 February 2020. This study provides a preliminary epidemiological analysis of the first 45 days of COVID-19 outbreak in Nigeria. We estimated the early transmissibility via time-varying reproduction number based on the Bayesian method that incorporates uncertainty in the distribution of serial interval (time interval between symptoms onset in an infected individual and the infector), and adjusted for disease importation. By 11 April 2020, 318 confirmed cases and 10 deaths from COVID-19 have occurred in Nigeria. At day 45, the exponential growth rate was 0.07 (95% confidence interval (CI): 0.05–0.10) with a doubling time of 9.84 days (95% CI: 7.28–15.18). Separately for imported cases (travel-related) and local cases, the doubling time was 12.88 days and 2.86 days, respectively. Furthermore, we estimated the reproduction number for each day of the outbreak using a three-weekly window while adjusting for imported cases. The estimated reproduction number was 4.98 (95% CrI: 2.65–8.41) at day 22 (19 March 2020), peaking at 5.61 (95% credible interval (CrI): 3.83–7.88) at day 25 (22 March 2020). The median reproduction number over the study period was 2.71 and the latest value on 11 April 2020, was 1.42 (95% CrI: 1.26–1.58). These 45-day estimates suggested that cases of COVID-19 in Nigeria have been remarkably lower than expected and the preparedness to detect needs to be shifted to stop local transmission.

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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

BMJ Open ◽

10.1136/bmjopen-2021-056636 ◽

2021 ◽

Vol 11 (11) ◽

pp. e056636

Author(s):

Thomas Ward ◽

Alex Glaser ◽

Alexander Johnsen ◽

Feng Xu ◽

Ian Hall ◽

...

Keyword(s):

Growth Rate ◽

Exponential Growth ◽

Doubling Time ◽

Reproduction Number ◽

Age Groups ◽

Instantaneous Growth Rate ◽

Rt Pcr ◽

Gene Target ◽

Novel Variants ◽

The Uk

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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Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak

10.1101/2020.01.23.916395 ◽

2020 ◽

Cited By ~ 72

Author(s):

Shi Zhao ◽

Qianyin Lin ◽

Jinjun Ran ◽

Salihu S Musa ◽

Guangpu Yang ◽

...

Keyword(s):

Basic Reproduction Number ◽

Exponential Growth ◽

Early Phase ◽

Reproduction Number ◽

Fold Increase ◽

Reporting Rate ◽

The Mean ◽

Novel Coronavirus ◽

The Impact ◽

The Basic Reproduction Number

AbstractBackgroundsAn ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia hit a major city of China, Wuhan, December 2019 and subsequently reached other provinces/regions of China and countries. We present estimates of the basic reproduction number, R0, of 2019-nCoV in the early phase of the outbreak.MethodsAccounting for the impact of the variations in disease reporting rate, we modelled the epidemic curve of 2019-nCoV cases time series, in mainland China from January 10 to January 24, 2020, through the exponential growth. With the estimated intrinsic growth rate (γ), we estimated R0 by using the serial intervals (SI) of two other well-known coronavirus diseases, MERS and SARS, as approximations for the true unknown SI.FindingsThe early outbreak data largely follows the exponential growth. We estimated that the mean R0 ranges from 2.24 (95%CI: 1.96-2.55) to 3.58 (95%CI: 2.89-4.39) associated with 8-fold to 2-fold increase in the reporting rate. We demonstrated that changes in reporting rate substantially affect estimates of R0.ConclusionThe mean estimate of R0 for the 2019-nCoV ranges from 2.24 to 3.58, and significantly larger than 1. Our findings indicate the potential of 2019-nCoV to cause outbreaks.

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Estimation of the basic reproduction number of novel coronavirus (COVID-19) in Bangladesh: A 65-day outbreak data-driven analysis

10.21203/rs.3.rs-32412/v1 ◽

2020 ◽

Author(s):

Md. Hasan ◽

Akhtar Hossain ◽

Wasimul Bari ◽

Syed Shariful Islam

Keyword(s):

Growth Model ◽

Basic Reproduction Number ◽

Exponential Growth ◽

Reproduction Number ◽

Fold Increase ◽

Reporting Rate ◽

Basic Reproductive Number ◽

Exponential Growth Model ◽

Novel Coronavirus ◽

The Basic Reproduction Number

Abstract BackgroundThe outbreak of novel coronavirus disease (COVID-19), started from Wuhan, China, at the end of December 2019, hits almost the entire world. In Bangladesh, the first case was officially reported on March 8, 2020. We estimated the basic reproductive number, R0, of COVID-19 for Bangladesh using the first 65-day data of the outbreak.MethodsWith time-varying disease reporting rate, epidemic curves were estimated using the exponential growth model utilizing daily COVID-19 diagnosis data in Bangladesh from March 8 to May 11, 2020. We estimated R0 using the estimated intrinsic growth rate (γ). Serial intervals (SI) have been used from two well-known coronaviruses’ outbreaks, SARS and MERS; and the early estimate of SI of COVID-19 in Wuhan, China.ResultsThe COVID-19 epidemic in Bangladesh followed an exponential growth model. We found the R0 to be 1.84 [95% CI: 1.82–1.86], 1.82 [95% CI: 1.81–1.84], and 1.94 [95% CI: 1.92–1.96], for MERS, COVID-19, and SARS SI respectively without adjusting reporting rate. With the adjusted reporting rate, R0 reduced to 1.63 [95% CI: 1.62–1.65], 1.62 [95% CI: 1.61–1.64], and 1.71 [95% CI: 1.70–1.73] for a five-fold increase. Inverse association between the reporting rate and the basic reproduction number was observed.ConclusionThe R0 was found to be 1.87 for existing cases and was reduced to 1.65 for the five-fold increase of the early reporting rate. Findings suggest a continued COVID-19 outbreak in Bangladesh and immediate steps need to be taken to control.

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Novel Coronavirus in Nigeria: Epidemiological analysis of the first 45 days of the pandemic

10.1101/2020.04.14.20064949 ◽

2020 ◽

Cited By ~ 1

Author(s):

Oyelola A. Adegboye ◽

Adeshina I. Adekunle ◽

Ezra Gayawan

Keyword(s):

Doubling Time ◽

Reproduction Number ◽

Infected Individual ◽

World Health ◽

Time Interval ◽

Epidemiological Analysis ◽

Serial Interval ◽

Novel Coronavirus ◽

Health Organization ◽

Local Transmission

AbstractBackgroundOn December 31, 2019, the World Health Organization (WHO) was notified of a novel coronavirus in China that was later named COVID-19. On March 11, 2020, the outbreak of COVID-19 was declared a pandemic. The first instance of the virus in Nigeria was documented on February 27, 2020.MethodsThis study provides a preliminary epidemiological analysis of the first 45 days of COVID-19 outbreak in Nigeria quantifying. We estimated the early transmissibility via time-varying reproduction number based on Bayesian method that incorporates uncertainty in the distribution of serial interval (time interval between symptoms onset in an infected individual and the infector) and adjusted for disease importation.FindingsBy April 11, 2020, 318 confirmed cases and 10 deaths from COVID-19 have occurred in Nigeria. At day 45, the exponential growth rate was 0.07 (95% Confidence Interval (CI): 0.05 – 0.10) with doubling time of 9.84 days (95% CI: 7.28 – 15.18). Separately for travel related and local cases the doubling time was 12.88 days and 2.86 days, respectively. Furthermore, we estimated the reproduction number for each day of the outbreak using three-weekly window while adjusting for travel related cases. The estimated reproduction number was 4.98 (95% CrI: 2.65 – 8.41) at day 22 (March 19, 2020), peaking at 5.61 (95% CrI: 3.83 –7.88) at day 25 (March 22, 2020). The median reproduction number over the study period was 2.71 and the latest value at April 11, 2020 was 1.42 (95% CI: 1.26 – 1.58).InterpretationThese 45-day estimates suggested that cases of COVID-19 in Nigeria have been remarkably lower than expected and the preparedness to detect needs to be shifted to stop local transmission.FundingNone

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Evolutionary Dynamics And Geographic Dispersal Of Beta Coronaviruses In African Bats

10.1101/2020.05.14.056085 ◽

2020 ◽

Author(s):

Babatunde Olanrewaju Motayo ◽

Olukunle Oluwapamilerin Oluwasemowo ◽

Paul Akiniyi Akinduti

Keyword(s):

Evolutionary Dynamics ◽

Evolutionary Rate ◽

Sequence Data ◽

Reservoir Host ◽

South American ◽

The Novel ◽

Long Distance ◽

Evolutionary Origins ◽

Novel Coronavirus ◽

African Bats

ABSTRACTBats have been shown to serve as reservoir host of various viral agents including coronaviruses. They have also been associated with the novel coronavirus SARS-CoV-2. This has made them an all important agent for CoV evolution and transmission. Our objective in this study was to investigate the dispersal, phylogenomics and evolution of betacoronavirus (βCoV) among African bats. We retrieved sequence data from established databases such as GenBank and Virus Pathogen Resource, covering the partial RNA dependent RNA polymerase (RdRP) gene of Bat coronaviruses from eight African, three Asian, five European, two South American countries and Australia. We analyzed for Phylogeographic information relating to genetic diversity and evolutionary dynamics. Our study revealed that majority of the African strains fell within Norbecovirus subgenera, with an Evolutionary rate of 1.301 × 10−3, HPD (1.064 × 10−3 – 1.434 × 10−3) subs/site/year. The African strains diversified into three main subgenera, Norbecovirus, Hibecovirus and Marbecovirus. The time to most common recent ancestor for Norbecovirus strains was 1968, and 2010, for the African Marbecovirus strains. There was evidence of inter species transmission of Norbecovirus among bats in Cameroun and DRC. Phlylogeography showed that there were inter-continental spread of Bt-CoV from Europe, China and Hong Kong into Central and Southern Africa, highlighting the possibility of long distance transmission. Our study has elucidated the possible evolutionary origins of βCoV among African bats, we therefore advocate for broader studies of whole genome sequences of BtCoV to further understand the drivers for their emergence and zoonotic spillovers into human population.

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Estimation of the Basic Reproduction Number of SARS-CoV-2 in Bangladesh Using Exponential Growth Method

10.1101/2020.09.29.20203885 ◽

2020 ◽

Author(s):

Riaz Mahmud ◽

H. M. Abrar Fahim Patwari

Keyword(s):

Standard Deviation ◽

Basic Reproduction Number ◽

Exponential Growth ◽

Generation Time ◽

Reproduction Number ◽

First Case ◽

The World ◽

Growth Method ◽

Novel Coronavirus ◽

The Basic Reproduction Number

Objectives: In December 2019, a novel coronavirus (SARS-CoV-2) outbreak emerged in Wuhan, Hubei Province, China. Soon, it has spread out across the world and become an ongoing pandemic. In Bangladesh, the first case of novel coronavirus (SARS-CoV-2) was detected on March 8, 2020. Since then, not many significant studies have been conducted to understand the transmission dynamics of novel coronavirus (SARS-CoV-2) in Bangladesh. In this study, we estimated the basic reproduction number R0 of novel coronavirus (SARS-CoV-2) in Bangladesh. Methods: The data of daily confirmed cases of novel coronavirus (SARS-CoV-2) in Bangladesh and the reported values of generation time of novel coronavirus (SARS-CoV-2) for Singapore and Tianjin, China, were collected. We calculated the basic reproduction number R0 by applying the exponential growth (EG) method. Epidemic data of the first 76 days and different values of generation time were used for the calculation. Results: The basic reproduction number R0 of novel coronavirus (SARS-CoV-2) in Bangladesh is estimated to be 2.66 [95% CI: 2.58-2.75], optimized R0 is 2.78 [95% CI: 2.69-2.88] using generation time 5.20 with a standard deviation of 1.72 for Singapore. Using generation time 3.95 with a standard deviation of 1.51 for Tianjin, China, R0 is estimated to be 2.15 [95% CI: 2.09-2.20], optimized R0 is 2.22 [95% CI: 2.16-2.29]. Conclusions: The calculated basic reproduction number R0 of novel coronavirus (SARS-CoV-2) in Bangladesh is significantly higher than 1, which indicates its high transmissibility and contagiousness.

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Fractal kinetics of COVID-19 pandemic (with update 3/1/20)

10.1101/2020.02.16.20023820 ◽

2020 ◽

Cited By ~ 36

Author(s):

Anna L. Ziff ◽

Robert M. Ziff

Keyword(s):

Power Law ◽

Exponential Growth ◽

Reproduction Number ◽

Small World ◽

Rate Equations ◽

The Novel ◽

Daily Rate ◽

Exponential Decline ◽

Power Law Kinetics ◽

Novel Coronavirus

AbstractWe give an update to the original paper posted on 2/17/20 – now (as of 3/1/20) the China deaths are rapidly decreasing, and we find an exponential decline to the power law similar to the that predicted by the network model of Vazquez [2006]. At the same time, we see non-China deaths increasing rapidly, and similar to the early behavior of the China statistics. Thus, we see three stages of the spread of the disease in terms of number of deaths: exponential growth, power-law behavior, and then exponential decline in the daily rate.(Original abstract) The novel coronavirus (COVID-19) continues to grow rapidly in China and is spreading in other parts of the world. The classic epidemiological approach in studying this growth is to quantify a reproduction number and infection time, and this is the approach followed by many studies on the epidemiology of this disease. However, this assumption leads to exponential growth, and while the growth rate is high, it is not following exponential behavior. One approach that is being used is to simply keep adjusting the reproduction number to match the dynamics. Other approaches use rate equations such as the SEIR and logistical models. Here we show that the current growth closely follows power-law kinetics, indicative of an underlying fractal or small-world network of connections between susceptible and infected individuals. Positive deviations from this growth law might indicate either a failure of the current containment efforts while negative deviations might indicate the beginnings of the end of the pandemic. We cannot predict the ultimate extent of the pandemic but can get an estimate of the growth of the disease.

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Impact of COVID-19 epidemic curtailment strategies in selected Indian states: an analysis by reproduction number and doubling time with incidence modelling

10.1101/2020.05.10.20094946 ◽

2020 ◽

Cited By ~ 2

Author(s):

Arun Mitra ◽

Abhijit P Pakhare ◽

Adrija Roy ◽

Ankur Joshi

Keyword(s):

Growth Rate ◽

State Government ◽

Doubling Time ◽

Reproduction Number ◽

Indian Government ◽

Indian States ◽

Transmission Parameters ◽

Likelihood Approach ◽

The Government ◽

Novel Coronavirus

The Government of India in networks with its state government has implemented the epidemic curtailment strategies inclusive of case-isolation, quarantine and lockdown in response to ongoing novel coronavirus (COVID-19) outbreak . In this manuscript we attempt to estimate the effect of these steps across ten Indian states using crowd-sourced data. The chosen transmission parameters are -reproduction number (R0), doubling time and growth rate during the early epidemic phase (15 days into lockdown) and 30 days into lockdown (23rd April 2020) through maximum likelihood approach. The overall analysis shows the decreasing trends in reproductive numbers and growth rate (with few exceptions) and incremental doubling time. The curtailment strategies employed by the Indian government seemed to be effective in reducing the transmission parameters of the COVID-19 epidemic. The effective reproductive numbers are still higher above the threshold of 1 and the resultant absolute numbers tend to be exponentiating fundamentally. The curtailment strategy thus may take into account these findings while formulating further course of actions.

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