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

Disclosure statement

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

Data availability and materials statement

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

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.