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

2021 ◽  
Vol 256 ◽  
pp. 110-126
Author(s):  
Andrew Harvey ◽  
Paul Kattuman ◽  
Craig Thamotheram

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.

2021 ◽  
Vol 18 (182) ◽  
Author(s):  
Andrew Harvey ◽  
Paul Kattuman

The time-dependent reproduction number, R t , is a key metric used by epidemiologists to assess the current state of an outbreak of an infectious disease. This quantity is usually estimated using time-series observations on new infections combined with assumptions about the distribution of the serial interval of transmissions. Bayesian methods are often used with the new cases data smoothed using a simple, but to some extent arbitrary, moving average. This paper describes a new class of time-series models, estimated by classical statistical methods, for tracking and forecasting the growth rate of new cases and deaths. Very few assumptions are needed and those that are made can be tested. Estimates of R t , together with their standard deviations, are obtained as a by-product.


BMJ Open ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. e056636
Author(s):  
Thomas Ward ◽  
Alex Glaser ◽  
Alexander Johnsen ◽  
Feng Xu ◽  
Ian Hall ◽  
...  

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.


2015 ◽  
Vol 18 (4) ◽  
pp. 455-471
Author(s):  
William Miles ◽  

Since the recent turmoil in UK housing, there has been controversy over whether house prices in the past decade have entered a bubble. While there are numerous techniques employed to investigate the presence of bubbles, testing the significance of breaks in the dynamics of prices has been utilized in other research to detect such bubbles. This is important in itself, as changing parameters in housing time series models make forecasting and portfolio management more difficult. We examine thirteen regions of the UK as well as the national home prices. The results indicate that while there were some breaks over the 2000s, more regions (and the UK as a whole) experienced breaks over the late 1980s and early 1990s. These results indicate that while there have been large price swings over the past decade, the late 1980s/early 1990s, which followed sharp changes in housing, monetary and fiscal policies, appear to be the larger boom-bust episode.


Author(s):  
Greg Dropkin

AbstractIntroductionThe first reported UK case of COVID-19 occurred on 31 January 2020, and a lockdown of unknown duration began on 24 March. One model to forecast disease spread depends on clinical parameters and transmission rates. Output includes the basic reproduction number R0 and the log growth rate r in the exponential phase.MethodsUK data on reported deaths is used to estimate r. A likelihood for the transmission parameters is defined from a gaussian density for r using the mean and standard error of the estimate. Parameter samples from the Metropolis-Hastings algorithm lead to an estimate and credible interval for R0 and forecasts for severe and critical cases, and deaths during a lockdown.ResultsIn the exponential phase, the UK growth rate for log(deaths) is r = 0.224 with s.e. 0.005. R0 = 5.81 with 90% CI (5.08, 6.98). In a 12 week lockdown from 24 March with transmission parameters reduced to 20% of their previous values, around 63,000 severely ill patients will need hospitalisation by mid June, 37,000 critically ill will need intensive care, whilst over 81,000 are expected to die. Had the lockdown begun on 17 March around 16,500 severe, 9,250 critical cases and 18,500 deaths would be expected by early June. With 10% transmission, severe and critical cases peak in April.DiscussionThe R0 estimate is around twice the value quoted by the UK government. The NHS faces extreme pressures, even if transmission is reduced ten-fold. An earlier lockdown could have saved many lives.


2012 ◽  
Vol 09 ◽  
pp. 390-397 ◽  
Author(s):  
THULASYAMMAL RAMIAH PILLAI ◽  
MAHENDRAN SHITAN

Forestry is the art and science of managing forests, tree plantations, and related natural resources. The main goal of forestry is to create and implement systems that allow forests to continue a sustainable provision of environmental supplies and services. Forest area is land under natural or planted stands of trees, whether productive or not. Forest area of Malaysia has been observed over the years and it can be modeled using time series models. A new class of GARMA models have been introduced in the time series literature to reveal some hidden features in time series data. For these models to be used widely in practice, we illustrate the fitting of GARMA (1, 1; 1, δ) model to the Annual Forest Area data of Malaysia which has been observed from 1987 to 2008. The estimation of the model was done using Hannan-Rissanen Algorithm, Whittle's Estimation and Maximum Likelihood Estimation.


2018 ◽  
Vol 7 (2) ◽  
pp. 81-86
Author(s):  
Abhishek Roy ◽  
Gautam Dutta ◽  
Prabir Kumar Das

The purpose of this study is to investigate the current status of e-transactions time series growth rate in the state of Jharkhand from January 2013 to 20th May, 2018 to understand the citizen adoption pattern of various e-government services. Government spends lots of money in developing and implementing e-services, but despite spending huge money the adoption rate is low. This study will analyze the adoption rate by using time series models and find the adoption pattern to improve the future adoption rate of e-services. Besides this, the paper will help us to understand whether current e-transaction methods are user friendly or not. Therefore, identify the best model to evaluate the growth rate of e-transactions in the context of government electronic transactions. In this regard, various existing time series models have been evaluated to obtain the result of this study. The paper draws-up the emergent model derived from the analysis. Finally, a framework is suggested to select Brown’s Exponential Smoothing Model as an ideal model for evaluating the growth rate of government e-transaction for the state of Jharkhand. This will help government to recommend and strategize better e-services plan for the state. This research can be used in policy making, strategizing and finding the key to user acceptance of innovation in technology in government electronic transaction with the help of Brown’s Exponential Smoothing as it can reduce the impacts of seasonal factors.


2021 ◽  
Author(s):  
John S Dagpunar

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.


Marketing ZFP ◽  
2010 ◽  
Vol 32 (JRM 1) ◽  
pp. 24-29
Author(s):  
Marnik G. Dekimpe ◽  
Dominique M. Hanssens

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.


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