Containing COVID-19 Among 627,386 Persons in Contact With the Diamond Princess Cruise Ship Passengers Who Disembarked in Taiwan: Big Data Analytics

Short- and Long-Term Predicted and Witnessed Consequences of Digital Surveillance During the COVID-19 Pandemic: Scoping Review

BACKGROUND

The COVID-19 pandemic has prompted the deployment of digital technologies for public health surveillance globally. The rapid development and use of these technologies have curtailed opportunities to fully consider their potential impacts (eg, for human rights, civil liberties, privacy, and marginalization of vulnerable groups).

OBJECTIVE

We conducted a scoping review of peer-reviewed and gray literature to identify the types and applications of digital technologies used for surveillance during the COVID-19 pandemic and the predicted and witnessed consequences of digital surveillance.

METHODS

Our methodology was informed by the 5-stage methodological framework to guide scoping reviews: identifying the research question; identifying relevant studies; study selection; charting the data; and collating, summarizing, and reporting the findings. We conducted a search of peer-reviewed and gray literature published between December 1, 2019, and December 31, 2020. We focused on the first year of the pandemic to provide a snapshot of the questions, concerns, findings, and discussions emerging from peer-reviewed and gray literature during this pivotal first year of the pandemic. Our review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guidelines.

RESULTS

We reviewed a total of 147 peer-reviewed and 79 gray literature publications. Based on our analysis of these publications, we identified a total of 90 countries and regions where digital technologies were used for public health surveillance during the COVID-19 pandemic. Some of the most frequently used technologies included mobile phone apps, location-tracking technologies, drones, temperature-scanning technologies, and wearable devices. We also found that the literature raised concerns regarding the implications of digital surveillance in relation to data security and privacy, function creep and mission creep, private sector involvement in surveillance, human rights, civil liberties, and impacts on marginalized groups. Finally, we identified recommendations for ethical digital technology design and use, including proportionality, transparency, purpose limitation, protecting privacy and security, and accountability.

CONCLUSIONS

A wide range of digital technologies was used worldwide to support public health surveillance during the COVID-19 pandemic. The findings of our analysis highlight the importance of considering short- and long-term consequences of digital surveillance not only during the COVID-19 pandemic but also for future public health crises. These findings also demonstrate the ways in which digital surveillance has rendered visible the shifting and blurred boundaries between public health surveillance and other forms of surveillance, particularly given the ubiquitous nature of digital surveillance.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-https://doi.org/10.1136/bmjopen-2021-053962.

Using ChatGPT-Like Solutions to Bridge the Communication Gap Between Patients With Rheumatoid Arthritis and Health Care Professionals

The communication gap between patients and health care professionals has led to increased disputes and resource waste in the medical domain. The development of artificial intelligence and other technologies brings new possibilities to solve this problem. This viewpoint paper proposes a new relationship between patients and health care professionals-"shared decision-making"-allowing both sides to obtain a deeper understanding of the disease and reach a consensus during diagnosis and treatment. Then, this paper discusses the important impact of ChatGPT-like solutions in treating rheumatoid arthritis using methotrexate from clinical and patient perspectives. For clinical professionals, ChatGPT-like solutions could provide support in disease diagnosis, treatment, and clinical trials, but attention should be paid to privacy, confidentiality, and regulatory norms. For patients, ChatGPT-like solutions allow easy access to massive amounts of information; however, the information should be carefully managed to ensure safe and effective care. To ensure the effective application of ChatGPT-like solutions in improving the relationship between patients and health care professionals, it is essential to establish a comprehensive database and provide legal, ethical, and other support. Above all, ChatGPT-like solutions could benefit patients and health care professionals if they ensure evidence-based solutions and data protection and collaborate with regulatory authorities and regulatory evolution.

Effective contact tracing for COVID-19: A systematic review

Contact tracing is commonly recommended to control outbreaks of COVID-19, but its effectiveness is unclear. Following PRISMA guidelines, we searched four databases using a range of terms related to contact tracing effectiveness for COVID-19. We found 343 papers; 32 were included. All were observational or modelling studies. Observational studies (n = 14) provided consistent, very-low certainty evidence that contact tracing (alone or in combination with other interventions) was associated with better control of COVID-19 (e.g. in Hong Kong, only 1084 cases and four deaths were recorded in the first 4.5 months of the pandemic). Modelling studies (n = 18) provided consistent, high-certainty evidence that under assumptions of prompt and thorough tracing with effective quarantines, contact tracing could stop the spread of COVID-19 (e.g. by reducing the reproduction number from 2.2 to 0.57). A cautious interpretation indicates that to stop the spread of COVID-19, public health practitioners have 2-3 days from the time a new case develops symptoms to isolate the case and quarantine at least 80% of its contacts.

Community-Based Digital Contact Tracing of Emerging Infectious Diseases: Design and Implementation Study With Empirical COVID-19 Cases

BACKGROUND

Contact tracing for containing emerging infectious diseases such as COVID-19 is resource intensive and requires digital transformation to enable timely decision-making.

OBJECTIVE

This study demonstrates the design and implementation of digital contact tracing using multimodal health informatics to efficiently collect personal information and contain community outbreaks. The implementation of digital contact tracing was further illustrated by 3 empirical SARS-CoV-2 infection clusters.

METHODS

The implementation in Changhua, Taiwan, served as a demonstration of the multisectoral informatics and connectivity between electronic health systems needed for digital contact tracing. The framework incorporates traditional travel, occupation, contact, and cluster approaches and a dynamic contact process enabled by digital technology. A centralized registry system, accessible only to authorized health personnel, ensures privacy and data security. The efficiency of the digital contact tracing system was evaluated through a field study in Changhua.

RESULTS

The digital contact tracing system integrates the immigration registry, communicable disease report system, and national health records to provide real-time information about travel, occupation, contact, and clusters for potential contacts and to facilitate a timely assessment of the risk of COVID-19 transmission. The digitalized system allows for informed decision-making regarding quarantine, isolation, and treatment, with a focus on personal privacy. In the first cluster infection, the system monitored 665 contacts and isolated 4 (0.6%) cases; none of the contacts (0/665, 0%) were infected during quarantine. The estimated reproduction number of 0.92 suggests an effective containment strategy for preventing community-acquired outbreak. The system was also used in a cluster investigation involving foreign workers, where none of the 462 contacts (0/462, 0%) tested positive for SARS-CoV-2.

CONCLUSIONS

By integrating the multisectoral database, the contact tracing process can be digitalized to provide the information required for risk assessment and decision-making in a timely manner to contain a community-acquired outbreak when facing the outbreak of emerging infectious disease.

Harnessing a real-time location system for contact tracing in a busy emergency department

BACKGROUND

With the persistent threat of emerging infectious diseases (EIDs), digital contact tracing (CT) tools can augment conventional CT for the prevention of healthcare-associated infectious disease transmission. However, their performance has yet to be evaluated comprehensively in the fast-paced emergency department (ED) setting.

OBJECTIVE

This study compared the CT performance of a radiofrequency identification (RFID)-based real-time location system (RTLS) with conventional electronic medical record (EMR) review against continuous direct observation of close contacts ('gold standard') in a busy ED during the coronavirus disease 2019 pandemic period.

METHODS

This cross-sectional study was conducted at the ED of a large tertiary care hospital in Singapore from December 2020 to April 2021. CT performance [sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and kappa] of the RTLS, EMR review and a combination of the two approaches (hybrid CT) was compared with direct observation. Finally, the mean absolute error (MAE) in the duration of each contact episode found via the RTLS and direct observation was calculated.

RESULTS

In comparison with EMR review, both the RTLS and the hybrid CT approach had higher sensitivity (0.955 vs 0.455 for EMR review) and a higher NPV (0.997 vs 0.968 for EMR review). The RTLS had the highest PPV (0.777 vs 0.714 for EMR review vs 0.712 for hybrid CT). The RTLS had the strongest agreement with direct observation (kappa=0.848). The MAE between contact durations of 80 direct observations and their respective RTLS contact times was 1.81 min.

CONCLUSION

The RTLS was validated to be a high-performing CT tool, with significantly higher sensitivity than conventional CT via EMR review. The RTLS can be used with confidence in time-strapped EDs for time-sensitive CT for the prevention of healthcare-associated transmission of EIDs.

Digital health and care: emerging from pandemic times

In 2020, we published an editorial about the massive disruption of health and care services caused by the COVID-19 pandemic and the rapid changes in digital service delivery, artificial intelligence and data sharing that were taking place at the time. Now, 3 years later, we describe how these developments have progressed since, reflect on lessons learnt and consider key challenges and opportunities ahead by reviewing significant developments reported in the literature. As before, the three key areas we consider are digital transformation of services, realising the potential of artificial intelligence and wise data sharing to facilitate learning health systems. We conclude that the field of digital health has rapidly matured during the pandemic, but there are still major sociotechnical, evaluation and trust challenges in the development and deployment of new digital services.

One Health in a Digital World: Technology, Data, Information and Knowledge

OBJECTIVES

To describe the origins and growth of the One Health concept and its recent application in One Digital Health.

METHODS

Bibliometric review and critical discussion of emergent themes derived from co-occurrence of MeSH keywords.

RESULTS

The fundamental interrelationship between human health, animal health and the wider environment has been recognized since ancient times. One Health as a distinct term originated in 2004 and has been a rapidly growing concept of interest in the biomedical literature since 2017. One Digital Health has quickly established itself as a unifying construct that highlights the critical role of technology, data, information and knowledge to facilitate the interdisciplinary collaboration that One Health requires. The principal application domains of One Digital Health to date are in FAIR data integration and analysis, disease surveillance, antimicrobial stewardship and environmental monitoring.

CONCLUSIONS

One Health and One Digital Health offer powerful lenses to examine and address crises in our living world. We propose thinking in terms of Learning One Health Systems that can dynamically capture, integrate, analyse and monitor application of data across the biosphere.

Employing digital technologies for effective governance: Taiwan's experience in COVID-19 prevention

Objectives

In the digital era, it is important to harness digital technologies to implement effective governance. This paper aims to propose a conceptual framework for the digital governance roadmap. It mainly is the meaningful integration of digital technologies into drafting policy accompanied with comprehensive planning and flexible strategy for better governance. The high-quality, timely, and reliable database is one of the key digital infrastructures for the meaningful employment of digital technologies.

Methods

Taiwan's experience in COVID-19 pandemic prevention is employed as the case to explore the roadmap of digital governance. The Taiwan government and civil society harnessed the power of its National Health Insurance (NHI) database, and further employed data science and GIS to develop the Face-mask distribution system and QR code registration system. Comprehensive planning and flexible strategy were conducted to address public concerns, such as data privacy and digital divide.

Results

Harnessing the NHI database's power, the GIS-based Face-mask distribution system and QR code registration system contributed to reducing the infections, panics, and public concerns including data privacy and digital divide for pandemic prevention.

Conclusions

While exploring the systematic digital governance roadmap, it is necessary to fulfill three basic criteria: (1) comprehensive planning, (2) flexible strategies, and (3) the meaningful employment of digital technologies. As one of the key digital infrastructures for the employment of digital technologies, the high-quality, timely, and reliable database is essential to release the power of data-driven for cross-domain collaborations, multiple engagement, innovative applications, and digital empowerment, towards achieving effective governance.

Public Interest Summary

This paper proposes a conceptual framework for the roadmap of digital governance, which highlights the importance of the meaningful integration of digital technologies into drafting policies accompanied with comprehensive planning and flexible strategy to achieve effective governance. During the process, the high-quality, timely, and reliable database acts as a key role in facilitating the operation of digital infrastructure for the employment of digital technologies.Taiwan's experience in COVID-19 prevention by harnessing the power of the NHI database and developing the GIS-based Face-mask distribution system and QR code registration system effectively addressed the public concerns on data privacy and digital divide towards effective pandemic prevention. This could be an example provided for other countries to balance public concerns and effective governance.

Use of YouTube by academic medical centres during the COVID-19 pandemic: an observational study in Taiwan

OBJECTIVES

YouTube has been of immense importance in conveying essential information on COVID-19 and promoting the latest healthcare policies during the outbreak. However, there have been few studies that have focused on how healthcare organisations have used YouTube to communicate with the public and increase their awareness during the pandemic, as well as its effectiveness.

DESIGN

A nationwide observational study.

SETTINGS

We analysed all YouTube video posts culled from the official accounts of all medical centres in Taiwan from December 2019 to August 2021.

PARTICIPANTS

All YouTube videos were categorised as either COVID-19 or non-COVID-19 related. The COVID-19-related videos were divided into five categories, and detailed metrics for each video were recorded. For comparison, we also surveyed all YouTube video posts placed by the Ministry of Health and Welfare and the Taiwan Centers for Disease Control (TCDC).

RESULTS

We analysed official YouTube channels from 17 academic medical centres, involving a total of 943 videos. We found a relationship between the quantity of YouTube videos uploaded by the TCDC and the trend of confirmed cases (Pearson's correlation coefficient was 0.25, p=0.02). Data from private hospitals revealed that they posted more COVID-19 videos (103 vs 56) when compared with public hospitals. In addition, multivariate linear regression showed that more 'likes' (estimate 41.1, 95% CI 38.8 to 43.5) and longer lengths (estimate 10 800, 95% CI 6968.0 to 14 632.0) of COVID-19-related videos correlated significantly with an increased number of 'views'.

CONCLUSIONS

This nationwide observational study, performed in Taiwan, demonstrates well the trend and effectiveness of academic medical centres in promoting sound healthcare advice regarding COVID-19 through YouTube due to the channel's easy accessibility and usability.

How Asymptomatic Transmission Influences Mitigation and Suppression Strategies during a Pandemic

Asymptomatic transmission complicates any public health strategies to combat a pandemic, which proved especially accurate in the case of COVID-19. Although asymptomatic cases are not unique to COVID-19, the high asymptomatic case rate raised many problems for developing effective public health interventions. The current modeling effort explored how asymptomatic transmission might impact pandemic responses in four key areas: isolation procedures, changes in reproduction rate, the potential for reduced transmission from asymptomatic cases, and social adherence to public health measures. A high rate of asymptomatic cases effectively requires large-scale public health suppression and mitigation procedures given that quarantine procedures alone could not prevent an outbreak for a virus such as SARS-CoV-2. This problem only becomes worse without lowering the effective reproduction rate, and even assuming the potential for reduced transmission, any virus with a high degree of asymptomatic transmission will likely produce a pandemic. Finally, there is a concern that asymptomatic individuals will also refuse to adhere to public health guidance. Analyses indicate that, given certain assumptions, even half of the population adhering to public health guidance could reduce the peak and flatten the curve by over 90%. Taken together, these analyses highlight the importance of taking asymptomatic cases into account when modeling viral spread and developing public health intervention strategies.

Risk Factors Surrounding an Increase in Burnout and Depression Among Health Care Professionals in Taiwan During the COVID-19 Pandemic

OBJECTIVES

This study aimed to investigate the risk factors surrounding an increase in both burnout levels and depression among health care professionals in Taiwan through use of a longitudinal study design.

DESIGN

This is a 2-year observational study that took place from January 2019 to December 2020.

SETTING AND PARTICIPANTS

Data among health care professionals were extracted from the Overload Health Control System of a tertiary medical center in central Taiwan.

METHODS

Burnout was measured through use of the Chinese version of the Copenhagen Burnout Inventory (C-CBI), whereas depression was ascertained by the Taiwanese Depression Questionnaire. Each participant provided both burnout and depression measurements during a nonpandemic period (2019) as well as during the COVID pandemic era (2020). Risk factors surrounding an increase in burnout levels and depression were analyzed through a multivariate logistic regression model with adjusting confounding factors.

RESULTS

Two thousand nineteen participants completed the questionnaire over 2 consecutive years, including 132 visiting doctors, 105 resident doctors, 1371 nurses, and 411 medical technicians. After adjustments, sleeplessness, daily working hours >8, and stress due to one's workload were all found to be risk factors for an increase in depression levels, whereas sleeplessness, lack of exercise, and stress due to one's workload were all found to be risk factors for an increase in personal burnout level. Being a member of the nursing staff, a younger age, sleeplessness, and lack of exercise were all risk factors for an increase in work-related burnout levels.

CONCLUSIONS AND IMPLICATIONS

Poor sleep, lack of exercise, long working hours, and being a member of the nursing staff were risk factors regarding an increase in personal burnout, work-related burnout levels and depression among health care professionals. Leaders within the hospital should investigate the working conditions and personal habits of all medical staff regularly and systematically during the COVID-19 pandemic and take any necessary preventive measures, such as improving resilience for nursing staff, in order to best care for their employees.

Use of digital technologies for public health surveillance during the COVID-19 pandemic: A scoping review

Throughout the COVID-19 pandemic, a variety of digital technologies have been leveraged for public health surveillance worldwide. However, concerns remain around the rapid development and deployment of digital technologies, how these technologies have been used, and their efficacy in supporting public health goals. Following the five-stage scoping review framework, we conducted a scoping review of the peer-reviewed and grey literature to identify the types and nature of digital technologies used for surveillance during the COVID-19 pandemic and the success of these measures. We conducted a search of the peer-reviewed and grey literature published between 1 December 2019 and 31 December 2020 to provide a snapshot of questions, concerns, discussions, and findings emerging at this pivotal time. A total of 147 peer-reviewed and 79 grey literature publications reporting on digital technology use for surveillance across 90 countries and regions were retained for analysis. The most frequently used technologies included mobile phone devices and applications, location tracking technologies, drones, temperature scanning technologies, and wearable devices. The utility of digital technologies for public health surveillance was impacted by factors including uptake of digital technologies across targeted populations, technological capacity and errors, scope, validity and accuracy of data, guiding legal frameworks, and infrastructure to support technology use. Our findings raise important questions around the value of digital surveillance for public health and how to ensure successful use of technologies while mitigating potential harms not only in the context of the COVID-19 pandemic, but also during other infectious disease outbreaks, epidemics, and pandemics.

The insufficiency of the Malaysian contact tracing app from the perspective of Chinese tourists: preparing for international tourism in the post-COVID-19 world

Understanding tourists' feedback on using Mysejahtera is critical for tourism recovery in these destinations, and even more so for countries like Malaysia and China, where national Contact Tracing Applications (CTA) are mandatory. However, Previous surveys on CTA use have mainly focused on voluntary CTA users, using qualitative research methods. In this research, Chinese overseas students in Malaysia are included as the reference group, and Chinese tourists with experiences traveling overseas are put into the experimental group. A total of 890 questionnaires were collected and taken as the original data to carry out the Chi-squared and Mann-Whitney U tests. Meanwhile, the experiment implemented a multiple linear regression mechanism to explore the variables that may improve the app Mysejahtera, with further analysis being conducted. According to the results, language issues are the most significant barrier to Chinese visitors using MySejahtera; the inability to register with a Chinese mobile phone number and the need to register a permanent address in Malaysia have a significant negative impact on the use of MySejahtera; and visitors' trust in science positively related to MySejahtera use.

One Digital Health for more FAIRness

BACKGROUND

 One Digital Health (ODH) aims to propose a framework that merges One Health's and Digital Health's specific features into an innovative landscape. FAIR (Findable, Accessible, Interoperable, and Reusable) principles consider applications and computational agents (or, in other terms, data, metadata, and infrastructures) as stakeholders with the capacity to find, access, interoperate, and reuse data with none or minimal human intervention.

OBJECTIVES

 This paper aims to elicit how the ODH framework is compliant with FAIR principles and metrics, providing some thinking guide to investigate and define whether adapted metrics need to be figured out for an effective ODH Intervention setup.

METHODS

 An integrative analysis of the literature was conducted to extract instances of the need-or of the eventual already existing deployment-of FAIR principles, for each of the three layers (keys, perspectives and dimensions) of the ODH framework. The scope was to assess the extent of scatteredness in pursuing the many facets of FAIRness, descending from the lack of a unifying and balanced framework.

RESULTS

 A first attempt to interpret the different technological components existing in the different layers of the ODH framework, in the light of the FAIR principles, was conducted. Although the mature and working examples of workflows for data FAIRification processes currently retrievable in the literature provided a robust ground to work on, a nonsuitable capacity to fully assess FAIR aspects for highly interconnected scenarios, which the ODH-based ones are, has emerged. Rooms for improvement are anyway possible to timely deal with all the underlying features of topics like the delivery of health care in a syndemic scenario, the digital transformation of human and animal health data, or the digital nature conservation through digital technology-based intervention.

CONCLUSIONS

 ODH pillars account for the availability (findability, accessibility) of human, animal, and environmental data allowing a unified understanding of complex interactions (interoperability) over time (reusability). A vision of integration between these two worlds, under the vest of ODH Interventions featuring FAIRness characteristics, toward the development of a systemic lookup of health and ecology in a digitalized way, is therefore auspicable.

Knowledge about Hepatitis B Virus and Relevant Safety Precautions among Dental Students in Kurdistan Region, Iraq

Background

Hepatitis B virus (HBV) is still a major health problem worldwide, placing healthcare workers, medical and dental students, and professionals at higher occupational risk. The present study aimed to evaluate the level of knowledge about this virus and relevant safety precautions among dental students in the Kurdistan region of Iraq. Materials and methods. This cross-sectional study was conducted among the third, fourth, and fifth stage dental students of Hawler Medical, Sulaimani, and Duhok universities. Data on the students' demographic characteristics and their knowledge about HBV (16 close-ended questions) and safety precaution measures (10 close-ended questions) were collected by means of a questionnaire. Analysis of variance was used to compare the mean of knowledge and safety precaution scores.

Results

In total, 372 students (mean age 21.77 ± 1.31 years) completed the questionnaires. The mean scores for knowledge and safety precautions were 13.17 ± 2.09 and 8.05 ± 1.61, respectively. Respondents from Hawler Medical University showed statistically significantly higher knowledge levels than their counterparts in Sulaimani and Duhok universities (p = 0.012).

Conclusions

The majority of surveyed dental students are aware of HBV, its mode of transmission, infection, complications, vaccination, and safety precautions required to prevent the spreading of the virus. While the levels of knowledge about HBV and safety precautions among the dental students in the Kurdistan region of Iraq were generally acceptable, differences in knowledge level were identified between the universities, and these may be related to their educational and training programs.

Predicting and preventing COVID-19 outbreaks in indoor environments: an agent-based modeling study

How to mitigate the spread of infectious diseases like COVID-19 in indoor environments remains an important research question. In this study, we propose an agent-based modeling framework to evaluate facility usage policies that aim to lower the probability of outbreaks. The proposed framework is individual-based, spatially-resolved with time resolution of up to 1 s, and takes into detailed account specific floor layouts, occupant schedules and movement. It enables decision makers to compute realistic contact networks and generate risk profiles of their facilities without relying on wearable devices, smartphone tagging or surveillance cameras. Our demonstrative modeling results indicate that not all facility occupants present the same risk of starting an outbreak, where the driver of outbreaks varies with facility layouts as well as individual occupant schedules. Therefore, generic mitigation strategies applied across all facilities should be considered inferior to tailored policies that take into account individual characteristics of the facilities of interest. The proposed modeling framework, implemented in Python and now available to the public in an open-source platform, enables such strategy evaluation.

IoT Adoption and Application for Smart Healthcare: A Systematic Review

In general, the adoption of IoT applications among end users in healthcare is very low. Healthcare professionals present major challenges to the successful implementation of IoT for providing healthcare services. Many studies have offered important insights into IoT adoption in healthcare. Nevertheless, there is still a need to thoroughly review the effective factors of IoT adoption in a systematic manner. The purpose of this study is to accumulate existing knowledge about the factors that influence medical professionals to adopt IoT applications in the healthcare sector. This study reviews, compiles, analyzes, and systematically synthesizes the relevant data. This review employs both automatic and manual search methods to collect relevant studies from 2015 to 2021. A systematic search of the articles was carried out on nine major scientific databases: Google Scholar, Science Direct, Emerald, Wiley, PubMed, Springer, MDPI, IEEE, and Scopus. A total of 22 articles were selected as per the inclusion criteria. The findings show that TAM, TPB, TRA, and UTAUT theories are the most widely used adoption theories in these studies. Furthermore, the main perceived adoption factors of IoT applications in healthcare at the individual level are: social influence, attitude, and personal inattentiveness. The IoT adoption factors at the technology level are perceived usefulness, perceived ease of use, performance expectancy, and effort expectations. In addition, the main factor at the security level is perceived privacy risk. Furthermore, at the health level, the main factors are perceived severity and perceived health risk, respectively. Moreover, financial cost, and facilitating conditions are considered as the main factors at the environmental level. Physicians, patients, and health workers were among the participants who were involved in the included publications. Various types of IoT applications in existing studies are as follows: a wearable device, monitoring devices, rehabilitation devices, telehealth, behavior modification, smart city, and smart home. Most of the studies about IoT adoption were conducted in France and Pakistan in the year 2020. This systematic review identifies the essential factors that enable an understanding of the barriers and possibilities for healthcare providers to implement IoT applications. Finally, the expected influence of COVID-19 on IoT adoption in healthcare was evaluated in this study.

Factors influencing stress during the second imposed of COVID-19 social restrictions in Indonesia

Background

The spread of the COVID-19 virus has had a significant impact on the governmental and social aspects, as well as to the psychological status of the population. In Indonesia, social restriction was a strategy to limit people's mobility to reduce virus transmission. As social beings, the imposition of social restriction makes them fall into stress due to feeling lonely, and some cannot earn money. The aim of the study was to assess the factor association of stress level and resilience of the Indonesian people during the imposition of the second social restriction due to the COVID-19 pandemic.

Design and method

A cross-sectional study was performed on respondents aged at least 18 years on 29 July-16 August 2021, when the social restriction was enforced for the second time. The online survey was conducted through several social media platforms (Facebook, Instagram, Twitter, and WhatsApp) to 256 respondents. The analysis was carried out descriptively and analytically using the chi-square and binary logistic regression.

Results

This study shows that there is a significant relationship between age (p < 0.001), marital status (p < 0.001), occupation (p < 0.001), income before and during the pandemic (p < 0.001) and resilience level to stress level among Indonesian. Resilience level is the only protective factor for people to not get stressed. Being normal resilience put them at 0.05 times (95% CI 0.01-0.76) more likely to have low perceived stress than low resilience.

Conclusion

Resilience level is the only factor that influence stress level among people in Indonesia during the second imposed social restriction in Indonesia.

Anti-Pandemic Policies in Estonia and Taiwan: Digital Power, Sovereignty and Biopolitics

Taiwan and Estonia are known as digital democracies facing threats from neighbors exploiting the vulnerabilities stemming from their degree of digitalization. Nevertheless, in their responses to the COVID-19 pandemic, Taiwan and Estonia have highlighted the strengths of digital democracy in combating a non-traditional security threat without employing the strong-arm tactics of authoritarian states. The goal of the article is to distinguish between vulnerability in cyberspace and digital power and put forward a conception of digital power to explain how Estonia and Taiwan were using their digital prowess to combat COVID-19. We argue that on one hand, their reliance on cybertechnology makes them particularly vulnerable to cyberattacks, but on the other their digital power enhances their global stature and domestic capacity to address threats such as COVID-19. The article starts by engaging with the ongoing academic debate on the concept of digital power and its political core. In the next section we adapt this concept to the policy practices of digital governance in Estonia and Taiwan. Lastly, we look more specifically at how investments in the IT sphere and e-governance were helpful for the two countries during the initial stage of the COVID-19 crisis. In conclusion, we highlight the paradox of two democracies choosing to extend the reach of the state into society through the use of digital tools to combat COVID-19. We further note that the pandemic provides a new biopolitical understanding of vulnerability and power in the digital realm.

Multi‐mode standardization under extreme time‐pressure – the case of COVID‐19 contact‐tracing apps

The present study investigates the standardization process of contact tracing apps during the COVID‐19 pandemic. Due to the epidemiological urgency, and differing from classical examples in the literature, this process is characterized by a compressed timeframe. In this setting, we investigate the role of different standard‐setting modes and their interaction through the lens of multi‐mode standardization. We find that the processes of standard setting through market competition or inclusive multi‐stakeholder committees proved time‐consuming and inefficient in addressing the immediate needs during this major global health crisis. Multi‐mode standardization between committees, market players, and governments equally proved unable to coordinate a standard. Ultimately, a so far neglected actor, namely platform owners, proved to be pivotal in coordinating a widely‐adopted standard. Our research extends multi‐mode standardization with platform owners as a further standardization actor of proliferating importance given the increasing pervasiveness of platforms in numerous contexts. The present article provides implications for the interplay between different modes of standard setting in general, and the setting of technological standards in crises in particular.

CN, F.V. J, P. V, K. J. Mobile technologies for contact tracing and prevention of COVID-19 positive cases: a cross- sectional study

Purpose

The purpose of this paper is to review the techniques for versatile advancements in contact tracing for the coronavirus disease 2019 (COVID-19) positive cases in this pandemic and to introduce the way of using the mobile location information collected within the country India. As the method, an exploratory review of current measures was conducted for confirmed COVID-19 contact tracing after understanding the current situation of the world. This paper has examined the way of using free locational information in an innovative way to reduce the spread of COVID-19 spread.

Design/methodology/approach

COVID-19 pandemic is the utmost global economic and health challenge of the century. One powerful and consistent procedure to slow down the spread and decrease the effect of COVID-19 is to track the essential and auxiliary contacts of confirmed COVID-19 positive cases by using contact-tracing innovation.

Findings

Although it takes the information from various clients, there are numerous odds in the information. The sincere measures were taken by the authors to avoid the abuse of information by any kind. A portion of the tips for keeping information from getting abused is on the whole, the information ought to be with just higher specialists, and they ought not to have the authorization to impart information to anybody.

Originality/value

This paper helps to track the COVID-19 positive cases as of now by using the field information assortment and outbreak examination stages. At the same time, mobile location information used inside the current guideline, rules for information handlers must incorporate measures to reduce the abusing of information.

The Impacts of COVID-19 on Healthcare Quality in Tertiary Medical Centers-A Retrospective Study on Data from Taiwan Clinical Performance Indicators System

To date, COVID-19 is by far the most impactful contagious disease of the 21st century and it has had a devastating effect on public health in countries around the globe. Elective medical services have declined markedly since the outbreak of the COVID-19 pandemic. Few studies have compared changes in healthcare quality before and during the outbreak of COVID-19 in Eastern Asian countries. We aimed to explore the impacts of COVID-19 on healthcare quality among medical centers in Taiwan. This was a retrospective study that collected anonymized data from the Taiwan Clinical Performance Indicator system, which was founded by the Joint Commission of Taiwan, an organization to promote, execute, and certify the nation’s healthcare quality policies. We explored quality indicators reported by more than three-quarters of medical centers in Taiwan from January 2019 to December 2020. The year 2019 was defined as the baseline period and 2020 was defined as the period after the start of the outbreak of COVID-19. Quality indicators from different regions were analyzed. Unscheduled returns of emergency patients within 72 h of their discharge, unscheduled returns of hospitalized patients within 14 days of their discharge, and unscheduled returns of surgical patients to the operating room during hospitalization all declined during the COVID-19 outbreak. Interestingly, the proportion of acute ischemic stroke patients receiving intravenous tissue-type plasminogen activator (IV-tPA) increased during outbreak of COVID-19. There were significant regional variations in healthcare quality indicators among medical centers in northern and middle/southern Taiwan. The outbreak of COVID-19 changed different patterns of healthcare systems. Although healthcare quality seemed to improve, further investigation is warranted to better understand whether those who were in need of returning to the emergency room or hospital were reluctant or were prevented from travel by the shelter-in-place policy.

Applications of GIS and geospatial analyses in COVID-19 research: A systematic review

Background: Geographic information science (GIS) has established itself as a distinct domain and incredibly useful whenever the research is related to geography, space, and other spatio-temporal dimensions. However, the scientific landscape on the integration of GIS in COVID-related studies is largely unknown. In this systematic review, we assessed the current evidence on the implementation of GIS and other geospatial tools in the COVID-19 pandemic.

Methods: We systematically retrieved and reviewed 79 research articles that either directly used GIS or other geospatial tools as part of their analysis. We grouped the identified papers under six broader thematic groups based on the objectives and research questions of the study- environmental, socio-economic, and cultural, public health, spatial transmission, computer-aided modeling, and data mining.

Results: The interdisciplinary nature of how geographic and spatial analysis was used in COVID-19 research was notable among the reviewed papers. Geospatial techniques, especially WebGIS, have even been widely used to visualize the data on a map and were critical to informing the public regarding the spread of the virus, especially during the early days of the pandemic. This review not only provided an overarching view on how GIS has been used in COVID-19 research so far but also concluded that geospatial analysis and technologies could be used in future public health emergencies along with statistical and other socio-economic modeling techniques. Our review also highlighted how scientific communities and policymakers could leverage GIS to extract useful information to make an informed decision in the future.

Conclusions:  Despite the limited applications of GIS in identifying the nature and spatio-temporal pattern of this raging pandemic, there are opportunities to utilize these techniques in handling the pandemic. The use of spatial analysis and GIS could significantly improve how we understand the pandemic as well as address the underserviced demographic groups and communities.

Analysis of Bluetooth RSSI for Proximity Detection of Ship Passengers

Concern about the health of people who traveled onboard was raised during the COVID-19 outbreak on the Diamond Princess cruise ship. The ship’s narrow space offers an environment conducive to the virus’s spread. Close contact isolation remains one of the most critical current measures to stop the virus’s rapid spread. Contacts can be identified efficiently by detecting intelligent devices nearby. The smartphone’s Bluetooth RSSI signal is essential data for proximity detection. This paper analyzes Bluetooth RSSI signals available to the public and compares RSSI signals in two distinct poses: standing and sitting. These features can improve accuracy and provide an essential basis for creating algorithms for proximity detection. This allows for improved accuracy in identifying close contacts and can help ships sustainably manage persons onboard in the post-epidemic era.

Application of machine learning in the fight against the COVID-19 pandemic: A review

Introduction: Machine learning (ML) plays a significant role in the fight against the COVID-19 (officially known as SARS-CoV-2) pandemic. ML techniques enable the rapid detection of patterns and trends in large datasets. Therefore, ML provides efficient methods to generate knowledge from structured and unstructured data. This potential is particularly significant when the pandemic affects all aspects of human life. It is necessary to collect a large amount of data to identify methods to prevent the spread of infection, early detection, reduction of consequences, and finding appropriate medicine. Modern information and communication technologies (ICT) such as the Internet of Things (IoT) allow the collection of large amounts of data from various sources. Thus, we can create predictive ML-based models for assessments, predictions, and decisions. Methods: This is a review article based on previous studies and scientifically proven knowledge. In this paper, bibliometric data from authoritative databases of research publications (Web of Science, Scopus, PubMed) are combined for bibliometric analyses in the context of ML applications for COVID-19. Aim: This paper reviews some ML-based applications used for mitigating COVID-19. We aimed to identify and review ML potentials and solutions for mitigating the COVID-19 pandemic as well as to present some of the most commonly used ML techniques, algorithms, and datasets applied in the context of COVID-19. Also, we provided some insights into specific emerging ideas and open issues to facilitate future research. Conclusion: ML is an effective tool for diagnosing and early detection of symptoms, predicting the spread of a pandemic, developing medicines and vaccines, etc.

Surveillance of SARS-CoV-2 in Sewage Treatment Plants between January 2020 and July 2021 in Taiwan

An outbreak of a new type of coronavirus pneumonia (COVID-19) began in Wuhan, Hubei Province, China, at the end of 2019, and it later spread to other areas of China and around the world. Taiwan reported the first confirmed case from an individual who returned from Wuhan, China, in January 2020 for Chinese New Year. Monitoring microbes in environmental sewage is an important epidemiological indicator, especially for pathogens that can be shed in feces such as poliovirus. We have conducted additional SARS-CoV-2 sewage testing since January 2020 using a well-established poliovirus environmental sewage surveillance system in Taiwan. Wastewater samples were collected from 11 sewage treatment plants from different parts of Taiwan twice a month for laboratory testing. By the end of July 2021, 397 wastewater specimens had been tested, and two samples were positive for SARS-CoV-2. These two wastewater samples were collected in the northern region of Taiwan from Taipei (site A) and New Taipei City (site C) at the beginning of June 2021. This result is consistent with the significant increase in confirmed COVID-19 cases observed in the same period of time. As the pandemic ebbed after June, the wastewater samples in these areas also tested negative for SARS-CoV-2 in July 2021.

Managing passenger flows for seaborne transportation during COVID-19 pandemic

The ongoing coronavirus disease 2019 (COVID-19) pandemic has negatively affected the cruise and ferry industry as the passenger numbers and revenues have plummeted. Therefore, we developed a holistic approach for mitigating COVID-19 during seaborne transportation in a cost-efficient way by combining behavioural changes, procedural workflows and technical innovations to reset the industry.

How to control cruise ship disease risk? Inspiration from the research literature

The COVID-19 pandemic once brought the global cruise industry to a standstill. This has led to the realization that the development of viable disease risk management policies and measures will guarantee the sustainability of cruise tourism. The purpose of this study is to identify and develop a framework for risk management of cruise ship disease based on the research literature of cruise diseases in the Web of Science from 1996 to 2019. The study analyzed the characteristics of the literature researchers, the relationships between their research institutions organizations, the main cruise ship disease cases and measures. Based on the discussion of COVID-19 on cruise ships，risk management factors of cruise ship diseases were proposed，which include the port country's epidemic prevention capacity, the mode of disease transmission, the relevant regulations on international public health disposal, the design and construction of cruise ships, the medical and health conditions on cruise ships, and the characteristics of cruise tourism activities. A timeline and system framework for cruise ship disease risk management is proposed. A special "maritime mobile community prevention and control system" should be established, and a cooperation mechanism consisting of the government, non-governmental organizations, trade groups and industry experts should be established. The port should be capable of border isolation, detection and establishment of temporary shelter hospitals. At the same time, big data technologies such as disease tracking, investigation and health data are also important components of the risk management system.

Six Cs of pandemic emergency management: A case study of Taiwan's initial response to the COVID-19 pandemic

A review of the disaster literature indicates that emergency responses to pandemics are often understudied; the current COVID-19 crisis provides an important opportunity to improve awareness and understanding about this and other contagious and disruptive diseases. With this in mind, this study examines Taiwan's response to COVID-19 because it was successful in spite of a high probability of contagion. The paper first explores the assertion that cognition, communication, collaboration, and control are vital for effective disaster response; it then indicates the need to consider two additional Cs: confidence (trust of government's competency) and coproduction (public participation in disaster transmission prevention). The paper also conducts a qualitative descriptive study of the Taiwan government's response timeline with examples of each of these concepts in action. To further illustrate the need for the two additional Cs, survey data illustrate how public confidence serves as a pivot between government's COVID-19 response and citizen coproduction in COVID-19 transmission prevention.

Comparing Fear of COVID-19 and Preventive COVID-19 Infection Behaviors Between Iranian and Taiwanese Older People: Early Reaction May Be a Key

This study assessed fear of the novel coronavirus-2019 (COVID-19), preventive COVID-19 infection behaviors, and the association between fear of COVID-19 and preventive COVID-19 infection behaviors among older people in Iran and Taiwan. Older people aged over 60 years (n = 144 for Iranians and 139 for Taiwanese) completed the Fear of COVID-19 Scale (FCV-19S) and two items on preventive COVID-19 infection behaviors (i.e., hand washing and mouth covering when sneezing). Iranian older people had a significantly higher level of fear of COVID-19 than did Taiwanese older people. Moreover, Iranian older people had significantly lower frequencies of preventive COVID-19 infection behaviors than did Taiwanese older people. Different timings in implementing COVID-19 infection control policies in Iran and Taiwan may explain why Iranian older people had greater fear of COVID-19 and lower preventive COVID-19 infection behaviors than did Taiwanese older people.

On limits of contact tracing in epidemic control

Contact tracing and quarantine are well established non-pharmaceutical epidemic control tools. The paper aims to clarify the impact of these measures in evolution of epidemic. The proposed deterministic model defines a simple rule on the reproduction number R in terms of ratio of diagnosed cases and, quarantine and transmission parameters. The model is applied to the early stage of Covid19 crisis in Poland. We investigate 3 scenarios corresponding to different ratios of diagnosed cases. Our results show that, depending on the scenario, contact tracing prevented from 50% to over 90% of cases. The effects of quarantine are limited by fraction of undiagnosed cases. The key conclusion is that under realistic assumptions the epidemic can not be controlled without any social distancing measures.

Eligibility for live, interactive otolaryngology telemedicine: 19-month experience before and during the COVID-19 pandemic in Taiwan

Background

Unequal access to healthcare is a global medical problem. Telemedicine, recently made possible by technological advances, may mitigate this inequity. However, the usefulness of telemedicine for procedure-driven disciplines, such as otolaryngology, under infectious conditions (e.g., the COVID-19 pandemic) is unknown.

Methods

Telemedicine was made legal in Taiwan by an amendment to the Physician Act in 2018. Kaohsiung Chang Gung Memorial Hospital was the first hospital in Taiwan to provide the telemedicine service by connecting to the Chenggong Branch of Taitung Hospital (CGBTH) in November 2018. This retrospective cohort study included all new and established otolaryngology outpatient consultations between November 2018 and May 2020 at CGBTH. The Current Procedural Terminology and International Classification of Disease, 10th Revision codes, patient demographic data, and questionnaire data were obtained.

Results

The study included 123 patients with 218 encounters over 19 months. The majority of complaints were ear-related (52.6%). Overall, 49% of the encounters required a specialized procedure for diagnosis and treatment; of these, cerumen removal was the most common procedure. The patient subjective improvement rate increased over the study period (from 62.0% to 78.9%). The rates of return and case closure were both around 90% in 2018 and 2019. The number of otolaryngology consultations and rate of return declined after the start of the COVID-19 pandemic; however, the subjective improvement and case closure rates remained stable. The telemedicine service saved at least 2 h driving time per visit.

Conclusion

Telemedicine for otolaryngology is a promising approach for remote and underserved regions, as well as during an infectious disease pandemic.

The Clinical Information Systems Response to the COVID-19 Pandemic

OBJECTIVE

The year 2020 was predominated by the coronavirus disease 2019 (COVID-19) pandemic. The objective of this article is to review the areas in which clinical information systems (CIS) can be and have been utilized to support and enhance the response of healthcare systems to pandemics, focusing on COVID-19.

METHODS

PubMed/MEDLINE, Google Scholar, the tables of contents of major informatics journals, and the bibliographies of articles were searched for studies pertaining to CIS, pandemics, and COVID-19 through October 2020. The most informative and detailed studies were highlighted, while many others were referenced.

RESULTS

CIS were heavily relied upon by health systems and governmental agencies worldwide in response to COVID-19. Technology-based screening tools were developed to assist rapid case identification and appropriate triaging. Clinical care was supported by utilizing the electronic health record (EHR) to onboard frontline providers to new protocols, offer clinical decision support, and improve systems for diagnostic testing. Telehealth became the most rapidly adopted medical trend in recent history and an essential strategy for allowing safe and effective access to medical care. Artificial intelligence and machine learning algorithms were developed to enhance screening, diagnostic imaging, and predictive analytics - though evidence of improved outcomes remains limited. Geographic information systems and big data enabled real-time dashboards vital for epidemic monitoring, hospital preparedness strategies, and health policy decision making. Digital contact tracing systems were implemented to assist a labor-intensive task with the aim of curbing transmission. Large scale data sharing, effective health information exchange, and interoperability of EHRs remain challenges for the informatics community with immense clinical and academic potential. CIS must be used in combination with engaged stakeholders and operational change management in order to meaningfully improve patient outcomes.

CONCLUSION

Managing a pandemic requires widespread, timely, and effective distribution of reliable information. In the past year, CIS and informaticists made prominent and influential contributions in the global response to the COVID-19 pandemic.

Big Data Research in Fighting COVID-19: Contributions and Techniques

The COVID-19 pandemic has induced many problems in various sectors of human life. After more than one year of the pandemic, many studies have been conducted to discover various technological innovations and applications to combat the virus that has claimed many lives. The use of Big Data technology to mitigate the threats of the pandemic has been accelerated. Therefore, this survey aims to explore Big Data technology research in fighting the pandemic. Furthermore, the relevance of Big Data technology was analyzed while technological contributions to five main areas were highlighted. These include healthcare, social life, government policy, business and management, and the environment. The analytical techniques of machine learning, deep learning, statistics, and mathematics were discussed to solve issues regarding the pandemic. The data sources used in previous studies were also presented and they consist of government officials, institutional service, IoT generated, online media, and open data. Therefore, this study presents the role of Big Data technologies in enhancing the research relative to COVID-19 and provides insights into the current state of knowledge within the domain and references for further development or starting new studies are provided.

Effectiveness of contact tracing apps for SARS-CoV-2: a rapid systematic review

OBJECTIVE

To systematically review evidence on effectiveness of contact tracing apps (CTAs) for SARS-CoV-2 on epidemiological and clinical outcomes.

DESIGN

Rapid systematic review.

DATA SOURCES

EMBASE (OVID), MEDLINE (PubMed), BioRxiv and MedRxiv were searched up to 28 October 2020.

STUDY SELECTION

Studies, both empirical and model-based, assessing effect of CTAs for SARS-CoV-2 on reproduction number (R), total number of infections, hospitalisation rate, mortality rate, and other epidemiologically and clinically relevant outcomes, were eligible for inclusion.

DATA EXTRACTION

Empirical and model-based studies were critically appraised using separate checklists. Data on type of study (ie, empirical or model-based), sample size, (simulated) time horizon, study population, CTA type (and associated interventions), comparator and outcomes assessed, were extracted. The most important findings were extracted and narratively summarised. Specifically for model-based studies, characteristics and values of important model parameters were collected.

RESULTS

2140 studies were identified, of which 17 studies (2 empirical, 15 model-based studies) were eligible and included in this review. Both empirical studies were observational (non-randomised) studies and at high risk of bias, most importantly due to risk of confounding. Risk of bias of model-based studies was considered low for 12 out of 15 studies. Most studies demonstrated beneficial effects of CTAs on R, total number of infections and mortality rate. No studies assessed effect on hospitalisation. Effect size was dependent on model parameters values used, but in general, a beneficial effect was observed at CTA adoption rates of 20% or higher.

CONCLUSIONS

CTAs have the potential to be effective in reducing SARS-CoV-2 related epidemiological and clinical outcomes, though effect size depends on other model parameters (eg, proportion of asymptomatic individuals, or testing delays), and interventions after CTA notification. Methodologically sound comparative empirical studies on effectiveness of CTAs are required to confirm findings from model-based studies.

Dental education changed by COVID-19: Student's perceptions and attitudes

BACKGROUND

Dental students have encountered changes in the teaching format amid the SARS CoV-2 pandemic. This study aims to evaluate the attitudes of dental students of one medical university toward online courses and compare them with those of non-dental students amid the SARS CoV-2 pandemic.

METHODS

A cross-sectional survey with a self-report online questionnaire was conducted at the medical university in May 2020 in Taipei. Students from the School of Dentistry, School of Dental Technology, and School of Oral Hygiene Study were enrolled in our survey.

RESULTS

In total, 473 students responded to the survey, 318 (67.2%) of whom were dental students. Overall, 366 (77%) students agreed with the change to online learning. Only 10.4% of students thought that dental professional courses with a laboratory format could be changed to online courses. Dental students were significantly more worried than non-dental students about being infected with COVID-19 and about the COVID-19 pandemic continuing.

CONCLUSIONS

In conclusion, changing to online learning seems to be perceived as feasible by students. However, more discussion about changing dental professional courses with a laboratory format to online courses considering the attitudes from students is needed.

Lack of cross-transmission of SARS-CoV-2 between passenger's cabins on the Diamond Princess cruise ship

An outbreak of COVID-19 occurred on the Diamond Princess cruise ship in January and February 2020 in Japan. We analysed information on the cases of infection to infer whether airborne transmission of SARS-CoV-2, the causative agent of COVID-19, had occurred between cabins. We infer from our analysis that most infections in passengers started on 28 January and were completed by 6 February, except in those who shared a cabin with another infected passenger. The distribution of the infected cabins was random, and no spatial cluster of the infected can be identified. We infer that the ship's central air-conditioning system for passenger's cabins did not play a role in SARS-CoV-2 transmission, i.e. airborne transmission did not occur between cabins during the outbreak, suggesting that the sufficient ventilation was provided. We also infer that the ship's cabin drainage system did not play a role. Most transmission appears to have occurred in the public areas of the cruise ship, likely due to crowding and insufficient ventilation in some of these areas.

Fear of COVID-19 Scale (FCV-19S) across countries: Measurement invariance issues

AIM

The threats of novel coronavirus disease 2019 (COVID-19) have caused fears worldwide. The Fear of COVID-19 Scale (FCV-19S) was recently developed to assess the fear of COVID-19. Although many studies found that the FCV-19S is psychometrically sound, it is unclear whether the FCV-19S is invariant across countries. The present study aimed to examine the measurement invariance of the FCV-19S across eleven countries.

DESIGN

Cross-sectional study.

METHODS

Using data collected from prior research on Bangladesh (N = 8,550), United Kingdom (N = 344), Brazil (N = 1,843), Taiwan (N = 539), Italy (N = 249), New Zealand (N = 317), Iran (N = 717), Cuba (N = 772), Pakistan (N = 937), Japan (N = 1,079) and France (N = 316), comprising a total 15,663 participants, the present study used the multigroup confirmatory factor analysis (CFA) and Rasch differential item functioning (DIF) to examine the measurement invariance of the FCV-19S across country, gender and age (children aged below 18 years, young to middle-aged adults aged between 18 and 60 years, and older people aged above 60 years).

RESULTS

The unidimensional structure of the FCV-19S was confirmed. Multigroup CFA showed that FCV-19S was partially invariant across country and fully invariant across gender and age. DIF findings were consistent with the findings from multigroup CFA. Many DIF items were displayed for country, few DIF items were displayed for age, and no DIF items were displayed for gender.

CONCLUSION

Based on the results of the present study, the FCV-19S is a good psychometric instrument to assess fear of COVID-19 during the pandemic period. Moreover, the use of FCV-19S is supported in at least ten countries with satisfactory psychometric properties.

Lessons from countries implementing find, test, trace, isolation and support policies in the rapid response of the COVID-19 pandemic: a systematic review

OBJECTIVE

To systematically learn lessons from the experiences of countries implementing find, test, trace, isolate, support (FTTIS) in the first wave of the COVID-19 pandemic.

DESIGN, DATA SOURCES AND ELIGIBILITY CRITERIA

We searched MEDLINE (PubMed), Cochrane Library, SCOPUS and JSTOR, initially between 31 May 2019 and 21 January 2021. Research articles and reviews on the use of contact tracing, testing, self-isolation and quarantine for COVID-19 management were included in the review.

DATA EXTRACTION AND SYNTHESIS

We extracted information including study objective, design, methods, main findings and implications. These were tabulated and a narrative synthesis was undertaken given the diverse research designs, methods and implications.

RESULTS

We identified and included 118 eligible studies. We identified the core elements of an effective find, test, trace, isolate, support (FTTIS) system needed to interrupt the spread of a novel infectious disease, where treatment or vaccination was not yet available, as pertained in the initial stages of the COVID-19 pandemic. We report methods used to shorten case finding time, improve accuracy and efficiency of tests, coordinate stakeholders and actors involved in an FTTIS system, support individuals isolating and make appropriate use of digital tools.

CONCLUSIONS

We identified in our systematic review the key components of an FTTIS system. These include border controls, restricted entry, inbound traveller quarantine and comprehensive case finding; repeated testing to minimise false diagnoses and pooled testing in resource-limited circumstances; extended quarantine period and the use of digital tools for contact tracing and self-isolation. Support for mental or physical health and livelihoods is needed for individuals undergoing self-isolation/quarantine. An integrated system with rolling-wave planning can best use effective FTTIS tools to respond to the fast-changing COVID-19 pandemic. Results of the review may inform countries considering implementing these measures.

Impact of COVID-19 on IoT Adoption in Healthcare, Smart Homes, Smart Buildings, Smart Cities, Transportation and Industrial IoT

COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic's potential impact on the adoption of the Internet of Things (IoT) in various broad sectors, namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our perspective and forecast of this impact on IoT adoption is based on a thorough research literature review, a careful examination of reports from leading consulting firms and interactions with several industry experts. For each of these sectors, we also provide the details of notable IoT initiatives taken in the wake of COVID-19. We also highlight the challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption.

Estimating global burden of COVID-19 with disability-adjusted life years and value of statistical life metrics

BACKGROUND

Global burden of COVID-19 has not been well studied, disability-adjusted life years (DALYs) and value of statistical life (VSL) metrics were therefore proposed to quantify its impacts on health and economic loss globally.

METHODS

The life expectancy, cases, and death numbers of COVID-19 until 30th April 2021 were retrieved from open data to derive the epidemiological profiles and DALYs (including years of life lost (YLL) and years loss due to disability (YLD)) by four periods. The VSL estimates were estimated by using hedonic wage method (HWM) and contingent valuation method (CVM). The estimate of willingness to pay using CVM was based on the meta-regression mixed model. Machine learning method was used for classification.

RESULTS

Globally, DALYs (in thousands) due to COVID-19 was tallied as 31,930 from Period I to IV. YLL dominated over YLD. The estimates of VSL were US$591 billion and US$5135 billion based on HWM and CVM, respectively. The estimate of VSL increased from US$579 billion in Period I to US$2160 billion in Period IV using CVM. The higher the human development index (HDI), the higher the value of DALYs and VSL. However, there exits the disparity even at the same level of HDI. Machine learning analysis categorized eight patterns of global burden of COVID-19 with a large variation from US$0.001 billion to US$691.4 billion.

CONCLUSION

Global burden of COVID-19 pandemic resulted in substantial health and value of life loss particularly in developed economies. Classifications of such health and economic loss is informative to early preparation of adequate resource to reduce impacts.

Effectiveness of non-pharmaceutical interventions and vaccine for containing the spread of COVID-19: Three illustrations before and after vaccination periods

Background

There are few studies demonstrating how the effectiveness of various extents of non-pharmaceutical interventions (NPIs) before and after vaccination periods. The study aimed to demonstrate such an effectiveness in the alteration of the epidemic curves from theory to practice.

Methods

The empirical data on the daily reported COVID-19 cases were extracted from open source. A computer simulation design in conjunction with the susceptible-exposed-infected-recovered (SEIR) type model was applied to evaluating confinement measures in Italy with adjustment for underreported cases; isolation and quarantine in Taiwan; and NPIs and vaccination in Israel.

Results

In Italy scenario, the extents of confinement measures were 34% before the end of March and then scaled up to 70% after then. Both figures were reduced to 22–69% after adjusting for underreported cases. Approximately 44% of confinement measures were implemented in the second surge of pandemic in Italy.

Fitting the observational data on Taiwan assuming the initial outbreak similar to Wuhan, China, 44% of isolation and quarantine were estimated before March 23rd, 2020. Isolation and quarantine were scaled up to 90% and at least 60% to contain community-acquired outbreaks from March 24th, 2020 onwards. Given 15% monthly vaccination rate from December 2020 in Israel, the effectiveness estimates of reducing the infected toll were 36%, 56%, and 85% for NPIs alone, vaccination alone, and both combined, respectively.

Conclusion

We demonstrated how various NPIs stamp out and delay the epidemic curve of COVID-19. The optimal implementation of these NPIs has to be planned before wide vaccine uptake worldwide.

A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study

Background

More than 79.2 million confirmed COVID-19 cases and 1.7 million deaths were caused by SARS-CoV-2; the disease was named COVID-19 by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the globe, but there are limited studies that investigate the global trend of the COVID-19 pandemic together with each country’s policy measures.

Objective

We aimed to develop an online artificial intelligence (AI) system to analyze the dynamic trend of the COVID-19 pandemic, facilitate forecasting and predictive modeling, and produce a heat map visualization of policy measures in 171 countries.

Methods

The COVID-19 Pandemic AI System (CPAIS) integrated two data sets: the data set from the Oxford COVID-19 Government Response Tracker from the Blavatnik School of Government, which is maintained by the University of Oxford, and the data set from the COVID-19 Data Repository, which was established by the Johns Hopkins University Center for Systems Science and Engineering. This study utilized four statistical and deep learning techniques for forecasting: autoregressive integrated moving average (ARIMA), feedforward neural network (FNN), multilayer perceptron (MLP) neural network, and long short-term memory (LSTM). With regard to 1-year records (ie, whole time series data), records from the last 14 days served as the validation set to evaluate the performance of the forecast, whereas earlier records served as the training set.

Results

A total of 171 countries that featured in both databases were included in the online system. The CPAIS was developed to explore variations, trends, and forecasts related to the COVID-19 pandemic across several counties. For instance, the number of confirmed monthly cases in the United States reached a local peak in July 2020 and another peak of 6,368,591 in December 2020. A dynamic heat map with policy measures depicts changes in COVID-19 measures for each country. A total of 19 measures were embedded within the three sections presented on the website, and only 4 of the 19 measures were continuous measures related to financial support or investment. Deep learning models were used to enable COVID-19 forecasting; the performances of ARIMA, FNN, and the MLP neural network were not stable because their forecast accuracy was only better than LSTM for a few countries. LSTM demonstrated the best forecast accuracy for Canada, as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 2272.551, 1501.248, and 0.2723075, respectively. ARIMA (RMSE=317.53169; MAPE=0.4641688) and FNN (RMSE=181.29894; MAPE=0.2708482) demonstrated better performance for South Korea.

Conclusions

The CPAIS collects and summarizes information about the COVID-19 pandemic and offers data visualization and deep learning–based prediction. It might be a useful reference for predicting a serious outbreak or epidemic. Moreover, the system undergoes daily updates and includes the latest information on vaccination, which may change the dynamics of the pandemic.

A Review of the Global Impact of the COVID-19 Pandemic on Public Mental Health, with a Comparison Between the USA, Australia, and Poland with Taiwan and Thailand

Coronavirus may have a negative impact not only on physical, but also on mental wellbeing. Despite the different approaches of countries to stop the spread of the virus and different infection rates, the dynamically developing pandemic has already affected the entire world. The consequences of the coronavirus for our mental health can be divided into those related to strategies for the prevention of infection, like isolation, quarantine, limitation of social contacts, and remote work, and those related to the direct impact of infection on our nervous system. This review aims to highlight the global effects of the Coronavirus Disease 2019 (COVID-19) pandemic on public mental health following social restrictions, to identify how infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may have direct neurophysiological effects and to compare the impact on public mental health between the USA, Australia, and Poland with Taiwan and Thailand.

Leveraging Polio GIS platforms in the African Region for mitigating Covid-19 contact tracing and Surveillance challenges

Background

The ongoing COVID-19 pandemic in Africa is an urgent public health crisis. Estimated models projected over 150,000 deaths and 4,600,000 hospitalizations in the first year of the disease in the absence of adequate interventions. Therefore, electronic contact tracing and surveillance have critical roles in decreasing COVID-19 transmission; yet, if not conducted properly, these methods can rapidly become a bottleneck for synchronized data collection, case detection, and case management. While the continent is currently reporting relatively low COVID-19 cases, digitized contact tracing mechanisms and surveillance reporting are necessary for standardizing real-time reporting of new chains of infection in order to quickly reverse growing trends and halt the pandemic.

Objective

This paper aims to describe a COVID-19 contact tracing smartphone app that includes health facility surveillance with a real-time visualization platform. The app was developed by the AFRO (African Regional Office) GIS (geographic information system) Center, in collaboration with the World Health Organization (WHO) emergency preparedness and response team. The app was developed through the expertise and experience gained from numerous digital apps that had been developed for polio surveillance and immunization via the WHO’s polio program in the African region.

Methods

We repurposed the GIS infrastructures of the polio program and the database structure that relies on mobile data collection that is built on the Open Data Kit. We harnessed the technology for visualization of real-time COVID-19 data using dynamic dashboards built on Power BI, ArcGIS Online, and Tableau. The contact tracing app was developed with the pragmatic considerations of COVID-19 peculiarities. The app underwent testing by field surveillance colleagues to meet the requirements of linking contacts to cases and monitoring chains of transmission. The health facility surveillance app was developed from the knowledge and assessment of models of surveillance at the health facility level for other diseases of public health importance. The Integrated Supportive Supervision app was added as an appendage to the pre-existing paper-based surveillance form. These two mobile apps collected information on cases and contact tracing, alongside alert information on COVID-19 reports at the health facility level; the information was linked to visualization platforms in order to enable actionable insights.

Results

The contact tracing app and platform were piloted between April and June 2020; they were then put to use in Zimbabwe, Benin, Cameroon, Uganda, Nigeria, and South Sudan, and their use has generated some palpable successes with respect to COVID-19 surveillance. However, the COVID-19 health facility–based surveillance app has been used more extensively, as it has been used in 27 countries in the region.

Conclusions

In light of the above information, this paper was written to give an overview of the app and visualization platform development, app and platform deployment, ease of replicability, and preliminary outcome evaluation of their use in the field. From a regional perspective, integration of contact tracing and surveillance data into one platform provides the AFRO with a more accurate method of monitoring countries’ efforts in their response to COVID-19, while guiding public health decisions and the assessment of risk of COVID-19.

Enhancing Data Linkage to Break the Chain of COVID-19 Spread: The Taiwan Experience

Digital technology has been widely used in health care systems and disease management, as well as in controlling the spread of COVID-19. As one of the most successful countries in combating the COVID-19 pandemic, Taiwan has successfully used digital technology to strengthen its efforts in controlling the COVID-19 pandemic. Taiwan has a well-established National Health Insurance System (NHIS), which provides a great opportunity to develop a nationwide data linkage model in an agile manner. Here we provide an overview of the application of data linkage models for strategies in combating COVID-19 in Taiwan, including NHIS centralized data linkage systems and “from border to community” information-driven data linkage systems during the COVID-19 pandemic. Furthermore, we discuss the dual role of digital technologies in being an “enabler” and a “driver” in early disease prevention. Lastly, Taiwan’s experience in applying digital technology to enhance the control of COVID-19 potentially highlights lessons learned and opportunities for other countries to handle the COVID-19 situation better.

Pandemic analysis of infection and death correlated with genomic open reading frame 10 mutation in severe acute respiratory syndrome coronavirus 2 victims

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues the pandemic spread of the coronavirus disease 2019 (COVID-19), over 60 million people confirmed infected and at least 1.8 million dead. One of the most known features of this RNA virus is its easiness to be mutated. In late 2020, almost no region of this SARS-CoV-2 genome can be found completely conserved within the original Wuhan coronavirus. Any information of the SARS-CoV-2 variants emerged through as time being will be evaluated for diagnosis, treatment, and prevention of COVID-19.

METHODS

We extracted more than two million data of SARS-CoV-2 infected patients from the open COVID-19 dashboard. The sequences of the 38-amino acid putative open reading frame 10 (Orf10) protein within infected patients were gathered output through from National Center for Biotechnology Information and the mutation rates in each position were analyzed and presented in each month of 2020. The mutation rates of A8 and V30 within Orf10 are displayed in selected counties: United States, India, German, and Japan.

RESULTS

The numbers of COVID-19 patients are correlated to the death numbers, but not with the death rates (stable and <3%). The amino acid positions locating at A8(F/G/L), I13, and V30(L) within the Orf10 sequence stay the highest mutation rate; N5, N25, and N36 rank at the lowest one. A8F expressed highly dominant in Japan (over 80%) and German (around 40%) coming to the end of 2020, but no significant finding in other countries.

CONCLUSION

The results demonstrate via mutation analysis of Orf10 can be further combined with advanced tools such as molecular simulation, artificial intelligence, and biosensors that can practically revealed for protein interactions and thus to imply the authentic Orf10 function of SARS-CoV-2 in the future.