High Acceptance of COVID-19 Tracing Technologies in Taiwan: A Nationally Representative Survey Analysis

Smart healthcare: A prospective future medical approach for COVID-19

COVID-19 has greatly affected human life for over 3 years. In this review, we focus on smart healthcare solutions that address major requirements for coping with the COVID-19 pandemic, including (1) the continuous monitoring of severe acute respiratory syndrome coronavirus 2, (2) patient stratification with distinct short-term outcomes (eg, mild or severe diseases) and long-term outcomes (eg, long COVID), and (3) adherence to medication and treatments for patients with COVID-19. Smart healthcare often utilizes medical artificial intelligence (AI) and cloud computing and integrates cutting-edge biological and optoelectronic techniques. These are valuable technologies for addressing the unmet needs in the management of COVID. By leveraging deep learning/machine learning capabilities and big data, medical AI can perform precise prognosis predictions and provide reliable suggestions for physicians' decision-making. Through the assistance of the Internet of Medical Things, which encompasses wearable devices, smartphone apps, internet-based drug delivery systems, and telemedicine technologies, the status of mild cases can be continuously monitored and medications provided at home without the need for hospital care. In cases that develop into severe cases, emergency feedback can be provided through the hospital for rapid treatment. Smart healthcare can possibly prevent the development of severe COVID-19 cases and therefore lower the burden on intensive care units.

Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand

With the constant mutation of COVID-19 variants, the need to reduce the spread should be explored. MorChana is a mobile application utilized in Thailand to help mitigate the spread of the virus. This study aimed to explore factors affecting the actual use (AU) of the application through the use of machine learning algorithms (MLA) such as Random Forest Classifier (RFC) and Artificial Neural Network (ANN). An integrated Protection Motivation Theory (PMT) and the Unified Theory of Acceptance and Use of Technology (UTAUT) were considered. Using convenience sampling, a total of 907 valid responses from those who answered the online survey were voluntarily gathered. With 93.00% and 98.12% accuracy from RFC and ANN, it was seen that hedonic motivation and facilitating conditions were seen to be factors affecting very high AU; while habit and understanding led to high AU. It was seen that when people understand the impact and causes of the COVID-19 pandemic’s aftermath, its severity, and also see a way to reduce it, it would lead to the actual usage of a system. The findings of this study could be used by developers, the government, and stakeholders to capitalize on using the health-related applications with the intention of increasing actual usage. The framework and methodology used presented a way to evaluate health-related technologies. Moreover, the developing trends of using MLA for evaluating human behavior-related studies were further justified in this study. It is suggested that MLA could be utilized to assess factors affecting human behavior and technology used worldwide.

Public Perspectives on Exposure Notification Apps: A Patient and Citizen Co-Designed Study

Canada deployed a digital exposure notification app (COVID Alert) as a strategy to support manual contact tracing. Our aims are to (1) assess the use, knowledge, and concerns of the COVID Alert app, (2) identify predictors of app downloads, and (3) develop strategies to promote social acceptability. A 36-item questionnaire was co-designed by 12 citizens and patients partnered with 16 academic researchers and was distributed in the province of Québec, Canada, from May 27 to 28 June 2021. Of 959 respondents, 43% had downloaded the app. Messaging from government sources constituted the largest influence on app download. Infrequent social contacts and perceived app inefficacy were the main reasons not to download the app. Cybersecurity, data confidentiality, loss of privacy, and geolocation were the most frequent concerns. Nearly half of the respondents inaccurately believed that the app used geolocation. Most respondents supported citizen involvement in app development. The identified predictors for app uptake included nine characteristics. In conclusion, this project highlights four key themes on how to promote the social acceptability of such tools: (1) improved communication and explanation of key app characteristics, (2) design features that incentivize adoption, (3) inclusive socio-technical features, and (4) upstream public partnership in development and deployment.

Papers please: Factors affecting national and international COVID-19 immunity and vaccination passport uptake as determined by representative national surveys

BACKGROUND

In response to the COVID-19 pandemic, countries are introducing digital passports that allow citizens to return to normal activities if they were previously infected with (immunity passport) or vaccinated against (vaccination passport) SARS-CoV-2. To be effective, policy decision makers must know whether these passports will be widely accepted by the public, and under what conditions? This study focuses attention on immunity passports, as these may prove useful in countries both with and without an existing COVID-19 vaccination program, however, our general findings also extend to vaccination passports.

OBJECTIVE

We aimed to assess attitudes towards the introduction of immunity passports in six countries, and determine what social, personal and contextual factors predicted their support.

METHODS

We collected 13,678 participants through online representative sampling across six countries - Australia, Japan, Taiwan, Germany, Spain, and the United Kingdom - during April to May of the 2020 COVID-19 pandemic, and assessed attitudes and support for the introduction of immunity passports.

RESULTS

Immunity passport support was moderate-to-low, being highest in Germany (51%; 775 of 1507 participants) and the United Kingdom (51%; 759 of 1484), followed by Taiwan (47%; 2841 of 5989), Australia (46%; 963 of 2086) and Spain (46%; 693 of 1491), and lowest in Japan (22%; 241 of 1081). Bayesian generalized linear mixed-effects modelling assessed predictive factors for immunity passport support across countries. International results showed neoliberal world views (odds ratio, OR = 1.17, CI[1.13:1.22]), personal concern (OR = 1.07, CI[1:1.16]) and perceived virus severity (OR = 1.07, CI[1.01:1.14]), the fairness of immunity passports (OR = 2.51, CI[2.36:2.66]), liking immunity passports (OR = 2.77, CI[2.61:2.94]), and a willingness to become infected to gain an immunity passport (OR = 1.6, CI[1.51:1.68]), were all predictive factors of immunity passport support. By contrast, gender (woman; OR = 0.9, CI[0.82:0.98]), immunity passport concern (OR = 0.61, CI[0.57:0.65]), and risk of harm to society (OR = 0.71, CI[0.67:0.76]) predicted a decrease in support for immunity passports. Minor differences in predictive factors were found between countries and results were modelled separately to provide national accounts of these data.

CONCLUSIONS

Our research suggests that support for immunity passports is predicted by the personal benefits and societal risks they confer. These findings generalized across six countries and may also prove informative for the introduction of vaccination passports, helping policy-makers to introduce effective COVID-19 passport policies in these six countries and around the world.

CLINICALTRIAL