Decoding China’s COVID‐19 ‘virus exceptionalism’: Community‐based digital contact tracing in Wuhan

Smartphone Apps for Containing the COVID-19 Pandemic in Germany: Qualitative Interview Study With Experts Based on Grounded Theory

BACKGROUND

Smartphone apps, including those for digital contact tracing (DCT), played a crucial role in containing infections during the COVID-19 pandemic. Their primary function is to generate and disseminate information to disrupt transmissions based on various events, such as encounters, vaccinations, locations, or infections. Although the functionality of these apps has been extensively studied, there is still a lack of qualitative research addressing critical issues.

OBJECTIVE

We will demonstrate that the use of DCT presents a challenge due to the tension between continuous health monitoring and uncertainties related to transparency and user sovereignty. On one hand, DCT enables the monitoring of various risk factors, including data-based calculations of infection probabilities. On the other hand, continuous risk management is intertwined with several uncertainties, including the unclear storage of personal data, who has access to it, and how it will be used in the future.

METHODS

We focus on the German "Corona-Warn-App" and support our argument with empirical data from 19 expert interviews conducted between 2020 and 2021. The interviews were conducted using a semistructured questionnaire and analyzed according to the principles of grounded theory.

RESULTS

Our data underscores 3 dimensions: transparency, data sovereignty, and the east-west divide. While transparency is considered an essential foundation for establishing trust in the use of DCT by providing a sense of security, data sovereignty is seen as a high value during the pandemic, protecting users from an undesired loss of control. The aspect of the east-west divide highlights the idea of incorporating sociocultural values and standards into technology, emphasizing that algorithms and data-driven elements, such as distance indicators, encounters, and isolations, are also influenced by sociocultural factors.

CONCLUSIONS

The effective use of DCT for pandemic containment relies on achieving a balance between individual control and technological prevention. Maximizing the technological benefits of these tools is crucial. However, users must also be mindful of the information they share and maintain control over their shared data.

Space and social distancing in managing and preventing COVID-19 community spread: An overview

The spread of COVID-19 at a large scale and at a rapid pace indicates the lack of social distancing measures at multiple levels. The individuals are not to be blamed, nor should we assume the early measures were ineffective or not implemented. It is all down to the multiplicity of transmission factors that made the situation more complicated than initially anticipated. Therefore, in facing the COVID-19 pandemic, this overview paper discusses the importance of space in social distancing measures. The methods used to investigate this study are literature review and case study. Many scholarly works have already provided us with evidence-based models that suggest the influential role of social distancing measures in preventing COVID-19 community spread. To further elaborate on this important topic, the aim here is to look at the role of space not only at the individual level but at larger scales of communities, cities, regions, etc. The analysis helps better management of cities during the pandemics such as COVID-19. By reflecting on some of the ongoing research on social distancing, the study concludes with the role of space at multiple scales and how it is central to the practice of social distancing. We need to be more reflective and responsive to achieve earlier control and containment of the disease and the outbreak at the macro level.

Digitalization impacts the COVID-19 pandemic and the stringency of government measures

COVID-19 poses a significant burden to populations worldwide. Although the pandemic has accelerated digital transformation, little is known about the influence of digitalization on pandemic developments. Therefore, this country-level study aims to explore the impact of pre-pandemic digital adoption on COVID-19 outcomes and government measures. Using the Digital Adoption Index (DAI), we examined the association between countries' digital preparedness levels and COVID-19 cases, deaths, and stringency indices (SI) of government measures until March 2021. Gradient Tree Boosting based algorithm pinpointed essential features related to COVID-19 trends, such as digital adoption, populations' smoker fraction, age, and poverty. Subsequently, regression analyses indicated that higher DAI was associated with significant declines in new cases (β = - 362.25/pm; p < 0.001) and attributed deaths (β = - 5.53/pm; p < 0.001) months after the peak. When plotting DAI against the SI normalized for the starting day, countries with higher DAI adopted slightly more stringent government measures (β = 4.86; p < 0.01). Finally, a scoping review identified 70 publications providing valuable arguments for our findings. Countries with higher DAI before the pandemic show a positive trend in handling the pandemic and facilitate the implementation of more decisive governmental measures. Further distribution of digital adoption may have the potential to attenuate the impact of COVID-19 cases and deaths.

Different Impacts of COVID-19 on Quality of Therapy, Psychological Condition, and Work Life Among Occupational Therapists in Physical and Mental Health Fields

Background

The negative impacts of the coronavirus disease 2019 (COVID-19) pandemic have worsened the quality of therapy, psychological condition, and work life of second-line healthcare workers and occupational therapists (OTs). However, no study has investigated whether the impact of COVID-19 varies among OTs working in different fields. This study aimed to investigate the differences on the impact of COVID-19 between OTs in the physical and mental health fields.

Methods

A cross-sectional online survey was conducted in Japan between January 20 and January 25, 2021. A total of 4,418 registered OTs who were members of the Japanese Association of Occupational Therapists volunteered for this study. After screening using the exclusion criteria, 1,383 participants were classified into two groups based on their field (mental health and physical health), and their quality of therapy, psychological condition, and work life were analyzed.

Results

OTs in the mental health field showed a greater decrease in therapy quality and increase in workload and a lower rate of decrease in working hours than those in the physical health field. In the multinomial logistic regression analysis, decreased and increased therapy quality and decreased therapy quality were significantly associated with depression in the physical health field, and decreased therapy quality was associated with insomnia in the mental health field. Furthermore, insomnia and anxiety were commonly associated with increased workload and working hours, respectively, in both fields, whereas anxiety and depression were associated with increased workload only in the physical health field.

Conclusions

These results demonstrate that COVID-19 differently impacted quality of treatment, workload, work time, and psychological condition in the physical and mental health fields; moreover, the relationships among these are different in these two fields. These results highlight the importance of investigating the field-specific negative impacts of COVID-19 on OTs and may provide helpful information for devising tailored and effective prevention and intervention strategies to address these challenges.

Machine learning approach for study on subway passenger flow

We investigate regional features nearby the subway station using the clustering method called the funFEM and propose a two-step procedure to predict a subway passenger transport flow by incorporating the geographical information from the cluster analysis to functional time series prediction. A massive smart card transaction dataset is used to analyze the daily number of passengers for each station in Seoul Metro. First, we cluster the stations into six categories with respect to their patterns of passenger transport. Then, we forecast the daily number of passengers with respect to each cluster. By comparing our predicted results with the actual number of passengers, we demonstrate the predicted number of passengers based on the clustering results is more accurate in contrast to the result without considering the regional properties. The result from our data-driven approach can be applied to improve the subway service plan and relieve infectious diseases as we can reduce the congestion by controlling train intervals based on the passenger flow. Furthermore, the prediction result can be utilized to plan a ‘smart city’ which seeks shorter commuting time, comfortable ridership, and environmental sustainability.

Management of and Revitalization Strategy for Megacities Under Major Public Health Emergencies: A Case Study of Wuhan

The outbreak of the COVID-19 pandemic in late 2019 has meant an uphill battle for city management. However, due to deficiencies in facilities and management experience, many megacities are less resilient when faced with such major public health events. Therefore, we chose Wuhan for a case study to examine five essential modules of urban management relevant to addressing the pandemic: (1) the medical and health system, (2) lifeline engineering and infrastructure, (3) community and urban management, (4) urban ecology and (5) economic development. The experience and deficiencies of each module in fighting the pandemic are analyzed, and strategies for revitalization and sustainable development in the future are proposed. The results show that in response to large-scale public health events, a comprehensive and coordinated medical system and good urban ecology can prevent the rapid spread of the epidemic. Additionally, good infrastructure and community management can maintain the operation of the city under the pandemic, and appropriate support policies are conducive to the recovery and development of the urban economy. These precedents provide insights and can serve as a reference for how to change the course of the pandemic in megacities that are still at risk, and they provide experience for responding to other pandemics.

The changing role of innovation for crisis management in times of COVID-19: An integrative literature review

Since the outbreak of the COVID-19 pandemic, countries have concentrated on developing policies that encourage the creation of more innovative products and services in response to the global health emergency. Effective collaboration, communication, and Open Innovation (OI) among government entities, education and research institutions, and the private sector have been critical to each country's overall effectiveness during the economic crisis. The objective of this paper is to examine the relationship between innovation and COVID-19 critically to have a better understanding of future research and practice developments. A systematic evaluation was conducted, analyzing papers on innovation and the COVID-19 pandemic. A total of 218 studies were analyzed to determine the essential research directions in this domain. Our suggested framework is made of aggregate components, which include technology adaption, sustainable development, healthcare, and sustainable economic performance. These components form the basis for the identification of emerging research hotspots in the field of COVID and innovation, as well as frame the COVID-19 issue as an opportunity to raise awareness about the crucial role of innovation in business and society at large.

The application framework of big data technology in the COVID-19 epidemic emergency management in local government-a case study of Hainan Province, China

Background

As COVID-19 continues to spread globally, traditional emergency management measures are facing many practical limitations. The application of big data analysis technology provides an opportunity for local governments to conduct the COVID-19 epidemic emergency management more scientifically. The present study, based on emergency management lifecycle theory, includes a comprehensive analysis of the application framework of China’s SARS epidemic emergency management lacked the support of big data technology in 2003. In contrast, this study first proposes a more agile and efficient application framework, supported by big data technology, for the COVID-19 epidemic emergency management and then analyses the differences between the two frameworks.

Methods

This study takes Hainan Province, China as its case study by using a file content analysis and semistructured interviews to systematically comprehend the strategy and mechanism of Hainan’s application of big data technology in its COVID-19 epidemic emergency management.

Results

Hainan Province adopted big data technology during the four stages, i.e., migration, preparedness, response, and recovery, of its COVID-19 epidemic emergency management. Hainan Province developed advanced big data management mechanisms and technologies for practical epidemic emergency management, thereby verifying the feasibility and value of the big data technology application framework we propose.

Conclusions

This study provides empirical evidence for certain aspects of the theory, mechanism, and technology for local governments in different countries and regions to apply, in a precise, agile, and evidence-based manner, big data technology in their formulations of comprehensive COVID-19 epidemic emergency management strategies.