Next Article in Journal
Development of an Anthropomorphic Phantom of the Axillary Region for Microwave Imaging Assessment
Next Article in Special Issue
Nonintrusive Fine-Grained Home Care Monitoring: Characterizing Quality of In-Home Postural Changes Using Bone-Based Human Sensing
Previous Article in Journal
Generating Comfortable Navigable Space for 3D Indoor Navigation Considering Users’ Dimensions
Previous Article in Special Issue
Knowledge-Based Decision Support in Healthcare via Near Field Communication
Article

A COVID-19-Based Modified Epidemiological Model and Technological Approaches to Help Vulnerable Individuals Emerge from the Lockdown in the UK

1
School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
2
Business School, The University of Sydney, Abercrombie Building H70, Darlington, NSW 2006, Australia
3
Alan Turing Institute, 96 Euston Road, London NW1 2DB, UK
4
Jill Dando Institute, University College London (UCL), 35 Tavistock Square, London WC1H 9EZ, UK
5
Medical Technologies Innovation Facility, Nottingham Trent University, Nottingham NG11 8NS, UK
6
College of Biomedical Engineering, China Medical University, Taichung 40402, Taiwan
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(17), 4967; https://doi.org/10.3390/s20174967
Received: 28 July 2020 / Revised: 29 August 2020 / Accepted: 31 August 2020 / Published: 2 September 2020
(This article belongs to the Special Issue Smart IoT Systems for Pervasive Healthcare)
COVID-19 has shown a relatively low case fatality rate in young healthy individuals, with the majority of this group being asymptomatic or having mild symptoms. However, the severity of the disease among the elderly as well as in individuals with underlying health conditions has caused significant mortality rates worldwide. Understanding this variance amongst different sectors of society and modelling this will enable the different levels of risk to be determined to enable strategies to be applied to different groups. Long-established compartmental epidemiological models like SIR and SEIR do not account for the variability encountered in the severity of the SARS-CoV-2 disease across different population groups. The objective of this study is to investigate how a reduction in the exposure of vulnerable individuals to COVID-19 can minimise the number of deaths caused by the disease, using the UK as a case study. To overcome the limitation of long-established compartmental epidemiological models, it is proposed that a modified model, namely SEIR-v, through which the population is separated into two groups regarding their vulnerability to SARS-CoV-2 is applied. This enables the analysis of the spread of the epidemic when different contention measures are applied to different groups in society regarding their vulnerability to the disease. A Monte Carlo simulation (100,000 runs) along the proposed SEIR-v model is used to study the number of deaths which could be avoided as a function of the decrease in the exposure of vulnerable individuals to the disease. The results indicate a large number of deaths could be avoided by a slight realistic decrease in the exposure of vulnerable groups to the disease. The mean values across the simulations indicate 3681 and 7460 lives could be saved when such exposure is reduced by 10% and 20% respectively. From the encouraging results of the modelling a number of mechanisms are proposed to limit the exposure of vulnerable individuals to the disease. One option could be the provision of a wristband to vulnerable people and those without a smartphone and contact-tracing app, filling the gap created by systems relying on smartphone apps only. By combining very dense contact tracing data from smartphone apps and wristband signals with information about infection status and symptoms, vulnerable people can be protected and kept safer. View Full-Text
Keywords: COVID-19; coronavirus; infection spread modelling; epidemiological model; contact tracing; personal protective equipment COVID-19; coronavirus; infection spread modelling; epidemiological model; contact tracing; personal protective equipment
Show Figures

Figure 1

MDPI and ACS Style

Anderez, D.O.; Kanjo, E.; Pogrebna, G.; Kaiwartya, O.; Johnson, S.D.; Hunt, J.A. A COVID-19-Based Modified Epidemiological Model and Technological Approaches to Help Vulnerable Individuals Emerge from the Lockdown in the UK. Sensors 2020, 20, 4967. https://doi.org/10.3390/s20174967

AMA Style

Anderez DO, Kanjo E, Pogrebna G, Kaiwartya O, Johnson SD, Hunt JA. A COVID-19-Based Modified Epidemiological Model and Technological Approaches to Help Vulnerable Individuals Emerge from the Lockdown in the UK. Sensors. 2020; 20(17):4967. https://doi.org/10.3390/s20174967

Chicago/Turabian Style

Anderez, Dario O., Eiman Kanjo, Ganna Pogrebna, Omprakash Kaiwartya, Shane D. Johnson, and John A. Hunt 2020. "A COVID-19-Based Modified Epidemiological Model and Technological Approaches to Help Vulnerable Individuals Emerge from the Lockdown in the UK" Sensors 20, no. 17: 4967. https://doi.org/10.3390/s20174967

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop