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Eur J Public Health ; 2021 Sep 16.
Article in English | MEDLINE | ID: covidwho-1413557


BACKGROUND: As most COVID-19 transmission occurs locally, targeted measures where the likelihood of infection and hospitalisation may be a prudent risk management strategy. To date, in the Republic of Ireland, a regional comparison of COVID-19 cases and hospitalisations has not been completed. Here we investigate (1) the variation in rates of confirmed infection and hospital admissions within geographical units of the Republic of Ireland, and (2) frequency of deviations in risk of infection or risk of hospitalisation. METHODS: We analysed routinely-collected, publicly-available data available from the national Health Protection and Surveillance Centre (HPSC) and Health Service Executive (HSE) from nine geographical units, known as Community Health Organisation (CHO) areas. The observational period included 206 14-day periods (1 Sept 2020 - 15 Apr 2021). RESULTS: A total of 206,844 laboratory confirmed cases and 7,721 hospitalisations were reported. The national incidence of confirmed infections 4508 (95% CI 4489-4528) per 100,000 people. The risk of hospital admission among confirmed cases was 3.7% (95% CI 3.5-3.9). Across geographical units, the likelihood that rolling 14-day risk of infection or hospitalisation exceeded national levels was 9-86% and 0-88%, respectively. In the most affected regions, we estimate this resulted in an excess of 15,180 infections and 1,920 hospitalisations. CONCLUSIONS: Responses to future COVID-19 outbreaks should consider the risk and harm of infection posed to people living in specific regions. Given the recent surges of COVID-19 cases in Europe, every effort should be made to strengthen local surveillance and to tailor community-centered measures to control transmission.

54th Annual Hawaii International Conference on System Sciences, HICSS 2021 ; 2020-January:64-73, 2021.
Article in English | Scopus | ID: covidwho-1283089


Considering the economic changes of recent times, financial literacy arises as a focal point of interest. COVID-19, coupled with the culmination of other societal issues, underlines the importance of understanding sensible personal finance. Nationwide lockdown and other economic constraints put us in immobilised positions to confide in safe and accessible entertainment havens such as games. Herein lies an interesting research opportunity to progress personal wellbeing and capability despite the extant issues of recent times. The paper demonstrates the design and implementation of an evolving serious game that supports lifelong learning and decision making relating to personal finance. The example is a useful account of serious games' evolutionary potential to incrementally support users through lifelong learning. The game's holistic design incorporates autonomy, motivation, and support structures to ensure that lifelong learning and decision making is effectively managed through an evolving system. The corresponding implementation evidences the sheer potential of serious games. © 2021 IEEE Computer Society. All rights reserved.

10th International Conference on the Internet of Things, IoT 2020 ; 2020.
Article in English | Scopus | ID: covidwho-901451


World Health Organisation (WHO) advises that humans must try to avoid touching their eye, nose and mouth, which is an effective way to stop the spread of viral diseases. This has become even more prominent with the widespread coronavirus (COVID-19), resulting in a global pandemic. However, we humans on average touch our face (eye, nose and mouth) 10-20 times an hour [22] [12], which is often the primary source [15] of getting infected by a variety of viral infections including seasonal Influenza, Coronavirus, Swine flu, Ebola virus, etc. Touching our face all day long is a quirk of human nature [13] and it is extremely difficult to train people to avoid touching their face. However, wearable devices and technology can help to continuously monitor our movements and trigger a timely event reminding people to avoid touching their face. In this work, we have collected a hand-to-face multi-sensor 3D motion dataset and named it COVID-away dataset. Using our dataset, we trained models that can continuously monitor human arm/hand movement using a wearable device and trigger a timely notification (e.g. vibration) to warn the device users when their hands are moved (unintentionally) towards their face. Our trained COVID-away models can be easily integrated into an app for smartwatches or fitness bands. Our evaluation shows that the Minimum Covariance Determinant (MCD) model produces the highest F1-score (0.93) using just the smartwatch's accelerometer data (39 features). Both the dataset and trained models are openly available on the Web at © 2020 ACM.