Analysis and prediction of omicron infection cases daily in the United Kingdom in November and December 2021

Abstract

The omicron infection rate led many countries to lock down. The variant risk assessment and new ones have been a high uncertainty. In this study, the model training is used across all data. By this point in the modeling process, the optimal hyperparameters that gave the best performance on the outer loop validation dataset have already been identified. Specific validation data given for our measurements are taken daily and set some arguments to skip between validation data. The results showed 58 days for UK research at 9 areas in two months displayed that the Northwest Enn. It confirmed most infection cases: values in November 2021 were 11,305,9811 for n (confirmed cases), 86.8% of infection rate, confidence low was 86.14% and 87.40 % for high confidence high. Values for November 22, 2021, were 16,985, and 15,415 for high confirmed cases, and 94.4% of infection rate, confidence low was 93.99, and 94.83 for high, respectively.

Highlights

• The study is to make a model to epidemic modeling EpiNow to estimate the effective reproduction and related statistics.

• The study is to analyze spread characteristics of S-gene positivity of the omicron.

• This study is to estimate and predict the omicron data from the UK to obtain the trend in epidemiology.

• The results of this study suggest that the omicron variants have elusiveness, transmissibility, and changeability.

KEYWORDS:

• Omicron
• coronavirus
• confirmed cases
• suspected cases
• time series

1. Introduction

Omicron has been outbreaking in the world. The UK Health Security Agency (UKHSA) identified all variants and amended its variant classification system to give a clearer indication and observe potentially significant changes in biological properties compared to the current dominant variants. The S gene belongs to BA. 2. XE that has been confirmed community transmission within the UK. BA.2 was found it rarely has spike gene deletion at position 69/70 and S-gene target positive (SGTP) on diagnostic assays with targets in the area. In England there was 97.6% compared to 93.7% on 20 March 2022 [1]. Two scientists confirmed that Omicron has been transmitted and evaded vaccines and course reinfections to more than 20 countries [2]. Some researchers observed the seroprevalence impact on 7010 participants. The results found that a decoupling of hospitalizations and deaths from the infections while omicron was circulating [3]. Some researchers explained that if the infection of SAR-CoV-2, the risk of severe outcomes is substantially lower for omicron than for delta, with higher reductions for more severe endpoints and significant variation with age [4]. A report has confirmed that after the first detection of omicron, the outbreak threshold within 5–10 days, and other omicron variants came to 14 days and up to 35 days [5].

Time-series analysis of COVID-19 cases and forecasting future trends have been made in fewer professional journals. A researcher tried to use a Bayesian structure time-series model to forecast the COVID-19 cases in the following time and made some progressive results [6]. A study from a researcher used the Arima method to analyze the omicron data and obtained satisfactory effects [7]. Some scientists applied regression models to analyze the association between symptomatic infection with the Omicron or Delta variants and vaccination by comparing the odds of prior 3 dose vaccination vs unvaccinated obtained good results [8]. Thus, we think that using time-series analysis of the omicron and its variant needs to be a strengthening area of the weakness.

The study of this article aims to estimate the omicron properties, its irregular nature of changeability, and susceptibility as well as the impact of omicron on communities from the density of population.

2. Methods

2.1. Data

We obtained the data from the UK Health Security Agency which is responsible for pre-testing every member of every community from the impact of infectious infectious diseases. It includes 9 areas in the UK. The variables contain UKHSA region, specimen date, n, per cent, sgtf, total, low conference interval, and high conference interval between 1 November and 28 December 2021. The total number of observations is 943, including adults and children. The UKHSA region represents 9 areas in the UK Health Security Agency: Landon, East Midlands, East of England, Northeast, Northwest, Southeast, Southwest, West Midlands, Yorkshire, and Humber. ‘Specimen date’ indicates the date that collected cases. ‘total’ represents the total number of cases daily that suspected Omicron with the symptoms. ‘n’ in the dataset indicates the number of people who were confirmed as Omicron infection daily. ‘percent’ is the value that equals n/total. ‘sgtf’ means the Omicron S-gene target failure as a percentage of cases. Typically, England data reported are up to 6 pm on 30 December, Northern Ireland data collected are up to 4 pm on 30 December; Scotland and Wales’ data are up to 5 pm on 30 December. Hospitalization and vaccination data are up to 30 December and might be lagged up to 48 hours [9].

2.2. Omicron variant characteristics

The Omicron variant classification was based on the UKHSA amendment for the variant classification system to give a clear indication that variants have significantly generated change in biological features that differentiate from the current dominant variants. Current variants were classified as Omicron sub-lineage BA.1 VOC-21NOV-01, Omicron sub-lineage BA.1 VOC-22 NOV-01, V-22APR-02(XE), V-22APR-03(Omicron sub-lineage BA.4), V-22APR-04(Omicron sub-lineage BA.5), V-22OCT-01(AY.4.2), V-20DEC-01(Alpha, B.1.1.7), V-21APR-02(Delta, and all sub-lineages). Also, XD is the lineage that has the Omicron S gene and combines a Delta genome which is mainly found in the France and UK. XE is a BA.1/BA.2 recombinant that includes the S gene belonging to BA.2. It could be transmitted within England. The overall proportion of SGTP caused by the relevant assay in England on 3 April 2022 for 97.6%. The assay that detects S gene target failure processed the number of samples and it has fallen which will affect the reliability of estimation.

2.3. Modeling for this data

2.3.1. The modeling epidemic dynamic characteristics

In the epidemic modeling there are three basic models: SIS endemic, SIR epidemic, and SIR endemic as the spread of infectious diseases. This study considers the susceptible exposes-infectious-recovered (SEIRD) epidemic model. If we assume the total population of the areas as the analyzed subject is T. Also, we assume that only some infected persons at the onset of groups in this area had been transmitted. So, susceptible person P would be exposed to E with the force of infection I. Then, the transmissive model could come into I. We supposed that unobserved (N) and observed (A) infectious persons spread the infectious groups as follows: $\text{I}=\frac{(N+\mathrm{\gamma A})}{\mathrm{\rho T}}\mathrm{\varnothing }$ where $\mathrm{\varnothing }$ is the infectious rate, and the effective population sizes should be $\rho =\frac{T-X}{T}$, where $T$ is the total population size, and the number $X$ is isolated persons who were able to be infected by infectious patients within their contracted groups. $X$ included those whose persons separated infected ones for social contacts and self-isolating persons. Also, we assumed that the contact proportion for observed infectious is a factor ($\gamma$). Then, if a fraction $Y$ of exposed persons was introduced to the observed infected class ($C$) with the rate ${ϵ}_1$. If those persons come from the infectious class, that is${ϵ}_2,$ which is a faction 1-d to regaining and would have deleting class (m), and the remainder(d). on the other hand, for unobserved ones, individuals come to the deleting state by rate $\theta$ [10].

Since the reproduction number R is a measure of the transmissibility for the epidemic disorder, sometimes it represents basic reproduction values such as $R_0$. If there exists susceptible individuals for a population change, we often use $R_t$to express value that reflects various response measures. Thus, EpiNow2 can provide some important advantages over other packages. For example, it could delay reporting and estimate $R_t$, especially as data are not complete. Also, we can estimate $R_t$ for dates of infection in that it represents a change in $R_t.$ In this article, the omicron infection over space and time has happened in the world. The UK omicron incidence characteristics have some differences from other regions in the world. The author used epidemic modeling EpiNow to estimate the effective reproduction and related statistics such as the doubling time, and future incidence. Figure 2 presents the omicron incidence numbers, report cases on different dates, epidemic curve, estimate R, and explored Sl distribution, respectively. After that, the ggplot function displays 9-panel observation values in the UK in Figures 3 and 4. The last modeling is to forecast omicron trends and optimal hyperparameters in the UK as time-series modeling professes [11] We generate a model of the expected reproduction number at the level of upper-tier local authorities l during a single day m starting on November 1, 2021, which includes local restrictions, mobility indicators, residual temporal variation, and proportion of positive test S-gene positive. The function is as follows: $R_{l,m}=(1+\alpha f_{l,m})\text{ exp}\left(p(m)+\sum _q{\beta }_q{Q}_{l,q{m}_{}}+\sum _w\mathrm{\gamma w}{H}_{l,w,m}+\log R_l\right)$ where $R_l$ is an intercept in the level of upper-tier local authorities that corresponds to $R_l$while the UK omicron infection in November and December 2021, $Q_{l,q{m}_{}}$is 1 when there is intervention $q$ is in place and 0 otherwise. $H_{l,w,m}$refers to the relative mobility in context (such as home, job, and public place) at time $m$ in the level of upper-tier local authorities in which $l$ as measured via Google, and $p(m)$is a time-varying component, modeled either as a region-specific thin-plate regression spline, and there is the sum of a static regional parameter and the UK spline. In this paper, we suppose the baseline model in a static regional parameter, when this model got the most explicable parameter estimates, and we can assume reproducibility of numbers with the relaxation steps and spread of S-gene positivity. Also, the spline version is referred to as capture confounding with unmeasured covariates over time, and it was as lower bounds on effect estimates. At last, we reached a fitted model in each $m$ separately. On the other hand, we want to point out that the important parameter is $\alpha$, which is the relative change in reproduction number in the presence of positive s-gene; $f_{l,m}$ is the proportion out of all positive tests for COVID-19 where the S-gene was tested positive, and the reproduction number in any given level of upper-tier local authorities.

2.3.2. Statistical analysis

We estimated the daily suspected and confirmed cases. And then, we analyzed and forecasted these variables using time-series regression with a forecast machine learning model. This technique included train-test split, which created the models on data train and evaluated their out-of-sample performance on data test; data preparation, which set up a list of datasets for model training, and there is one forecast horizon; windows establishing that partitioned the training data in the outer loop of nested cross-validation. The validation was in contiguous blocks of window length so that we forecast over multi-step ahead forecast horizons; historical data forecasting that took some numbers from trained models of train model and a list of used defined forecast functions. Model selection to estimate the LASSO model more stable and accurate and retrain the model by the entire training dataset to get our final models-1 for forecast horizon. To overview the cases in the UK, we used a pie chart that was based on the data frame entries.

3. Results

UK’s population is 68.4 million of which 1.77 persons day daily [12]. Omicron is a variant of the changeable and contagious epidemiology. In many countries and areas, the omicron outbreak presents suddenly increasing numbers daily. This paper analyzed detailed variant surveillance that made the variant risk assessments and designation of new SARS-CoV-2 variants such as Omicron variants S-gene and another confirmed SGFT. We observed 943 cases in 9 UK omicron infections in November and December the suspected and confirmed cases in Northwest reached our highest values (Table 1) in two single days.

Table 1. The most Omicron suspected and confirmed cases in each area in the UK were in November and December 2021.

UKHSA region	Specimen date	N	Pct	SGTF	Total	Conf_low	Conf_high
East Midlands	27/12/2021	506	6.52	Cases with S-gene	7761	5.99	7.10
East of England	22/12/2021	627	10.8	Cases with S-gene	5799	10.0	11.6
London	21/12/2021	1034	7.99	Cases with S-gene	12,939	7.53	8.48
Northeast	27/12/2021	451	9.67	Cases with S-gene	4662	8.85	10.6
Northwest	22/12/2021	1570	9.24	Cases with S-gene	16,985	8.81	9.69
Southeast	21/12/2021	1029	17.1	Cases with S-gene	6016	16.2	18.1
Southwest	21/12/2021	422	15.8	Cases with S-gene	2665	14.5	17.3
West Midland	22/12/2021	773	12.2	Cases with S-gene	6352	11.4	13.0
Yorkshire Humber	27/12/2021	664	6.53	Cases with S-gene	10,175	6.06	7.03

Notes: Each area indicates the highest values for suspected and confirmed cases within a single day for all age groups combined in a total of 943 observations. The results indicated that there was a narrow interval between confidence low and confidence high.

In addition, we found that the Omicron variant’s S-gene and another confirmed SGFT existed in 9 UK areas and cases rapidly increased to a higher value in a couple of days. Northwest and London were the most significant areas. In addition, the author analyzed UK omicron by using epidemic modeling. In Figure 1, we can see that the results displayed the cases of omicron would be increased trends: the new confirmed cases by infection increased (5806 cases). The rate of growth was 0. 032. These suggested that omicron variants are strongly contagious and abruptly increased.

Figure 1. COVID-19 Epidemic modeling of the datasets. The new confirmed cases by infection date look like 5806 (between 1536 and 18,234). The effective reproduction numbers are 1.1 for 0.64 and 1.7. The rate of growth is 0.032 between – 0.032 and 0.17. Doubling or halving time (days) is 21 days. The unsure value is 0.32.

Next, the authors analyzed the dataset by estimating from the Epidemic curve, Estimated R, and Explored SI distribution in Figure 2. In the Epidemic curve, the incidence on December 1 2021, reached 3; in Estimated R, the trend appeared from higher to lower; the explored SI distribution seemed wavy. These changes reflected the observed values that were influenced by the epidemic.

Figure 2. The Serial internal estimates from the data. It shows that it looks like to be highest incidence on December 1, 2021. The values of the mean are kpt under 4. The frequencies of the explored Sl distribution look like to be shaped waves. The highest value of frequency was 0.08.

For forecasting case trends, we used the ggplot function to estimate the values of confirmed and suspected cases no matter what the symptoms happened. In London, there were from lower, higher, and last lower. For this point, it was particularly prominent. Rather, the Omicron infection cases in other UK regions kept the values under 5000 dailies. We also found that the southwest region least increase. It reflected that more populations and more density could become an epidemiological environment.

Like suspected cases, the most increase of confirmed cases daily was in Northwest and London, and the least one was the southwest region. These showed that more populations and multiple cultural areas were susceptible to epidemic diseases (Figure 3). We should emphasize preventive measurement and health policies in these areas.

Figure 3. Overview of confirmed cases in the UK. 9 panels show the observation values, in each area in the UK. The model is a ggplot function that included specimen date (x-axis) and suspected cases of Omicron symptoms during November and December 2021 (y-axis). It looks like significant pandemic mitigation strategies. The colors are mainly used to represent 9 different research areas.

The authors also estimated and predicted the suspected cases to increase daily. It showed that, after December 28, 2021, suspected and confirmed increasing cases daily should continue to up to a higher peak (Figure 4).

Figure 4. Forecast of datasets. We allow the model training to across all data. By this point in the modeling process, the optimal hyperparameters that gave the best performance on the outer loop validation datasets have already been identified.

Incorporating the optimal hyperparameters in a final model would occur in a newly defined modeling wrapper function. The model with ‘1’ validation datasets that are given for our measurements are taken daily and set 460 arguments to skip between validation datasets. Also, custom validation windows were defined with vectors of start and stop dates. The blue panel represents feature, the green one indicates forecast, and the yellow one expresses no feature. The outside research dates, after December 28 2021, seem to have more infections and suspected cases in the UK.

4. Discussion and limitations

The UK Health Security Agency conducted an amendment for its variant classification system to give a clearer indication. This study is to explore some methods to find some epidemiological characteristics in the UK. The results suggest that the regions Omicron spread rapidly out may occur in higher population density, or big cities, people’s activity frequency. Our results show that Omicron infection cases were climbing up in the same UK area in a single day. Also, our result findings included the highest values of suspected and confirmed cases had most Omicron infection frequency came to 4000–4800, the median value was about 4500; the median value of confirmed cases/suspected cases was 70%; moreover, there was a narrow difference between confidence low and confidence high; the out of research time, after December 28 2021, forecasting values may be increasing. These findings have epidemiological significance. The study for Omicron analysis is currently a few reported in professional journals. Poland researchers reported that the Omicron variant (B.1.1.529) could be the exponential growth over four-week periods in the two most populated of South Africa’s provinces [13]. An England researcher reported a Friday’s daily data was 12,395 patients [14]. Some researchers analyzed 5 impacted countries including the USA, India, France, the UK, and Italy and found rapid growth in these countries in 2.5–4 days [15]. A couple of researchers used fit Cox proportional hazard models to compare time to any hospital admission and the association with new-onset respiratory symptoms, etc. The results found that 16,982 cases were infected as non-SGFT (Delta [B.1.617.2]) in southern California in the United States [16].

However, due to the limitation of these research data, we cannot further explore the impact of different environments, different age groups, different religions, and different policies of preventive measurement on Omicron variants. For example, how to analyze the prevalence of symptoms for an omicron infection from those of the delta [17]. How to figure out the problem of prevention if omicron infects children [18]? Currently, based on the data available, our study did not further estimate the impact of Omicron and its variants on communities. In addition, how to decrease the odds of hospitalization GGTF versus non-SGTF infection [19]. Second, how to identify omicron variant mutation in clinical samples? When there are several variants with the original wild type of strain [20]. Third, in our study of 943 observations with 58-day omicron infection cases, but time series was not long enough. The nonlinear association between the confidence interval and SGTF was unknown.

Further analysis should have more detailed information such as the presence of a relationship between the Omicron variants and different age groups whether they have been vaccinated [21], or they have obtained chronic diseases. Unfortunately, data in this study lack age groups (Please see the Appendix), we cannot analyze that age groups affect the omicron variants. Also, we hope that future research will include longer and newer time series.

In summary, in terms of the current study of time series of omicron infection cases daily in the UK, we obtained the results including that the population living in density regions existed at high risk of omicron infections, even more, possible to multiple time infections; the existence of a significant relationship between narrow confidence interval and high total numbers infected and confirmed with Omicron variants; the association of sharp increasing cases daily with strong contagious omicron variant mutation; in 58-day time-series data the suspected and confirmed cases in the future could be continued to have increasing trends in the UK. We predict the data will have a trend of increasing status in the future (Figure 4).

5. Conclusion

In this study, we analyzed the Omicron in THE UK November and December outbreaks present suddenly increasing numbers daily and estimated omicron variants S gene and SGFT. In forecasting case trends, we used the ggplot function to estimate the values of confirmed and suspected cases no matter what symptoms happened. We think that in London, there was from lower, higher, and last lower. For this point, it was particularly prominent. Rather, the Omicron infection cases in other UK regions kept the values under 5000 dailies. We also found that the southwest region least increases. These results remind us that public health in the areas where there is a population should be improved and strengthened to prevent epidemics in the future.

Data availability and materials statement

https://www.gov.uk/government/publications/covid-19-omicron-daily-overview.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Liming Xie

Liming Xie is a PhD student in the department of statistics at North Dakota State University. 1340 Administration Ave, Fargo, ND 58108, USA.

Xiyu Deng

Xiyu Deng is an MD and professor in the department of internal medicine, The First Hospital of Wuchang, Wuhan, Hubei 430060, China.

Xiaoyan Xie

Xiaoyan Xie is a Charge Nurse and RN in the department of preventive Disease, Wuchang Center of Disease Control and Prevention, Wuhan, Hubei 430061, China.

Shanshan Hu

Shanshan Hu receiced a master's in data Science program at Rutgers University School of Arts and Sciences, New Brunswick, NJ 08901. Her favorite research topics cover the statistical modeling of epidemiological and genomic data.

Appendix

Table

UKHSA region	Specimen Data	Confirmed	Percent	sgtf	Total	Conference low	Conference high
East Midlands	1/11/2021	874	100	Cases with confirmed S-gene	874	99.4545	100
East Midlands	2/11/2021	724	99.86	Cases with confirmed S-gene	725	99.10912	99.992
East Midlands	2/11/2021	1	0.14	confirmed SGTF	725	0.00720	0.8908
East Midlands	3/11/2021	757	100	confirmed S-gene	757	99.3705	100
East Midlands	4/11/2021	598	100	confirmed S-gene	598	99.2042	100
East Midlands	5/11/2021	567	99.82	confirmed S-gene	568	98.8646	99.99.8
East Midlands	5/11/2021	1	0.18	confirmed SGTF	568	0.0091	1.1353
East Midlands	6/11/2021	975	100	confirmed S-gene	975	99.5107	100
East Midlands	7/11/2021	907	100	confirmed S-gene	907	99.4742	100
East Midlands	8/11/2021	1578	99.75	confirmed S-gene	1582	99.6066	99.9189
East Midlands	8/11/2021	4	0.25	confirmed SGTF	1582	0.0810	0.6933
East Midlands	9/11/2021	1526	100	confirmed S-gene	1526	99.6869	100
East Midlands	10/11/2021	1524	99.74	confirmed S-gene	1528	99.2822	99.9160
East Midlands	10/11/2021	4	0.26	confirmed SGTF	1528	0.0838	0.7177
East Midlands	11/11/2021	1531	99.93	confirmed S-gene	1532	99.5771	99.9965
East Midlands	11/11/2021	1	0.07	confirmed SGTF	1532	0.0034	0.4228
East Midlands	12/11/2021	1663	99.94	confirmed S-gene	1664	99.6106	99.9968
East Midlands	12/11/2021	1	0.06	confirmed SGTF	1664	0.0031	0.3893
East Midlands	13/11/2021	1207	100	confirmed S-gene	1207	99.6044	100
East Midlands	14/11/2021	1233	99.84	confirmed S-gene	1235	99.3490	99.9719
East Midlands	14/11/2021	2	0.16	confirmed SGTF	1235	0.0280	0.6509
East Midlands	15/11/2021	2374	99.83	confirmed S-gene	2378	99.5382	99.9461
East Midlands	15/11/2021	4	0.17	confirmed SGTF	2378	0.0538	0.4617
East Midlands	16/11/2021	2031	100	confirmed S-gene	2031	99.7646	100
East Midlands	17/11/2021	2133	99.86	confirmed S-gene	2136	99.5533	99.9637
East Midlands	17/11/2021	3	0.14	confirmed SGTF	2136	0.0362	0.4466
East Midlands	18/11/2021	2027	99.9	confirmed S-gene	2029	99.6032	99.9639
East Midlands	18/11/2021	2	0.1	confirmed SGTF	2029	0.0170	0.3967
East Midlands	19/11/2021	2105	99.76	confirmed S-gene	2110	99.4135	99.9127
East Midlands	19/11/2021	5	0.24	confirmed SGTF	2110	0.0872	0.5864
East Midlands	20/11/2021	1958	100	confirmed S-gene	1958	99.7558	100
East Midlands	21/11/2021	2057	99.71	confirmed S-gene	2063	99.3339	99.9817
East Midlands	21/11/2021	6	0.29	confirmed SGTF	2063	0.1182	0.6660
East Midlands	22/11/2021	2772	99.96	confirmed S-gene	2773	99.7661	99.9981
East Midlands	22/11/2021	1	0.04	confirmed SGTF	2773	0.0018	0.2338
East Midlands	23/11/2021	2733	99.85	confirmed S-gene	2737	99.5967	99.9531
East Midlands	23/11/2021	4	0.15	confirmed SGTF	2737	0.0466	0.4012
East Midlands	24/11/2021	1895	99.79	confirmed S-gene	1889	99.4421	99.9325
East Midlands	24/11/2021	4	0.21	confirmed SGTF	1889	0.0674	0.5778
East Midlands	25/11/2021	2078	99.86	confirmed S-gene	2081	99.5415	99.9627
East Midlands	25/11/2021	3	0.14	confirmed SGTF	2081	0.0372	0.4584
East Midlands	26/11/2021	1292	99.54	confirmed S-gene	1292	98.9428	99.8119
East Midlands	26/11/2021	6	0.46	confirmed SGTF	1298	0.1880	1.0571
East Midlands	27/11/2021	1039	99.62	confirmed S-gene	1043	98.9495	98.8707
East Midlands	27/11/2021	4	0.38	confirmed SGTF	1043	0.1229	1.0500
East Midlands	28/11/2021	1140	99.74	confirmed S-gene	1143	99.1668	99.9321
East Midlands	28/11/2021	3	0.26	confirmed SGTF	1143	0.0678	0.8331
East Midlands	29/11/2021	1933	99.23	confirmed S-gene	1948	98.7015	98.5521
East Midlands	29/11/2021	15	0.77	confirmed SGTF	1948	0.4478	1.2984
East Midlands	30/11/2021	1637	99.45	confirmed S-gene	1646	98.9245	99.7329
East Midlands	30/11/2021	9	0.55	confirmed SGTF	1646	0.2670	1.0754
East Midlands	1/12/2021	1584	99.44	confirmed S-gene	1593	98.8888	99.7240
East Midlands	1/12/2021	9	0.56	confirmed SGTF	1593	0.2759	1.1111
East Midlands	2/12/2021	1215	98.3	confirmed S-gene	1236	97.3680	98.9181
East Midlands	2/12/2021	21	1.7	confirmed SGTF	1236	1.0818	2.6319
East Midlands	3/12/2021	1084	96.1	confirmed S-gene	1128	94.7538	97.1196
East Midlands	3/12/2021	44	1.9	confirmed SGTF	1128	2.8803	5.2461
East Midlands	4/12/2021	1891	98.75	confirmed S-gene	1915	98.1108	98.1779
East Midlands	4/12/2021	24	1.25	confirmed SGTF	1915	0.8226	1.8891
East Midlands	5/12/2021	1778	99.16	confirmed S-gene	1793	98.5897	99.5133
East Midlands	5/12/2021	15	0.84	confirmed SGTF	1793	0.4866	1.4102
Yorkshire and Humber	14/12/2021	2203	56.4	confirmed S-gene	3906	54.8269	57.9612
Yorkshire and Humber	14/12/2021	1703	43.6	confirmed SGTF	3906	42.0387	45.1730
Yorkshire and Humber	15/12/2021	2332	47.62	confirmed S-gene	4897	46.2143	49.0313
Yorkshire and Humber	15/12/2021	2565	52.38	confirmed SGTF	4897	50.9686	53.7856
Yorkshire and Humber	16/12/2021	1983	44.55	confirmed S-gene	4451	43.0857	46.0272
Yorkshire and Humber	16/12/2021	2468	55.45	confirmed SGTF	4451	53.9727	56.9142
Yorkshire and Humber	17/12/2021	1568	40.43	confirmed S-gene	3878	38.8860	41.9995
Yorkshire and Humber	17/12/2021	2310	59.57	confirmed SGTF	3878	56.0004	61.1139
Yorkshire and Humber	18/12/2021	1305	35.1	confirmed S-gene	3718	33.5681	36.6618
Yorkshire and Humber	18/12/2021	2413	64.9	confirmed SGTF	3718	63.3381	66.4318
Yorkshire and Humber	19/12/2021	1112	29.94	confirmed S-gene	3714	28.4758	31.4474
Yorkshire and Humber	19/12/2021	2602	70.06	confirmed SGTF	3714	68.5525	71.5241
Yorkshire and Humber	20/12/2021	1530	24.51	confirmed S-gene	6242	23.4522	25.6020
Yorkshire and Humber	20/12/2021	4712	75.49	confirmed SGTF	6242	74.3979	76.5477
Yorkshire and Humber	21/12/2021	1353	18.38	confirmed S-gene	7362	17.5033	19.2662
Yorkshire and Humber	21/12/2021	6009	81.62	confirmed SGTF	7362	80.7137	82.4966
Yorkshire and Humber	22/12/2021	1092	14.55	confirmed S-gene	7505	13.7641	15.3729
Yorkshire and Humber	22/12/2021	6413	85.45	confirmed SGTF	7505	84.6270	86.2358
Yorkshire and Humber	23/12/2021	987	12.91	confirmed S-gene	7644	12.1725	13.6891
Yorkshire and Humber	23/12/2021	6657	87.09	confirmed SGTF	7644	86.3108	87.8274
Yorkshire and Humber	24/12/2021	618	10.77	confirmed S-gene	5738	9.9858	11.6078
Yorkshire and Humber	24/12/2021	5120	89.23	confirmed SGTF	5738	88.3921	90.0141
Yorkshire and Humber	25/12/2021	315	8.4	confirmed S-gene	3750	7.5415	9.3449
Yorkshire and Humber	25/12/2021	3435	91.6	confirmed SGTF	3750	90.6550	90.4584
Yorkshire and Humber	26/12/2021	547	8.29	confirmed S-gene	6596	7.6441	8.9908
Yorkshire and Humber	26/12/2021	6049	91.71	confirmed SGTF	6596	91.0091	92.3558
Yorkshire and Humber	27/12/2021	664	6.53	confirmed S-gene	10,175	6.0573	7.0273
Yorkshire and Humber	27/12/2021	9511	93.47	confirmed SGTF	10,175	92.9726	93.9426
Yorkshire and Humber	28/12/2021	310	4.42	confirmed S-gene	7008	3.9598	4.9379
Yorkshire and Humber	28/12/2021	6698	95.58	confirmed SGTF	7008	95.0620	96.0401