Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study

Characterizing superspreading potential of infectious disease: Decomposition of individual transmissibility

In the context of infectious disease transmission, high heterogeneity in individual infectiousness indicates that a few index cases can generate large numbers of secondary cases, a phenomenon commonly known as superspreading. The potential of disease superspreading can be characterized by describing the distribution of secondary cases (of each seed case) as a negative binomial (NB) distribution with the dispersion parameter, k. Based on the feature of NB distribution, there must be a proportion of individuals with individual reproduction number of almost 0, which appears restricted and unrealistic. To overcome this limitation, we generalized the compound structure of a Poisson rate and included an additional parameter, and divided the reproduction number into independent and additive fixed and variable components. Then, the secondary cases followed a Delaporte distribution. We demonstrated that the Delaporte distribution was important for understanding the characteristics of disease transmission, which generated new insights distinct from the NB model. By using real-world dataset, the Delaporte distribution provides improvements in describing the distributions of COVID-19 and SARS cases compared to the NB distribution. The model selection yielded increasing statistical power with larger sample sizes as well as conservative type I error in detecting the improvement in fitting with the likelihood ratio (LR) test. Numerical simulation revealed that the control strategy-making process may benefit from monitoring the transmission characteristics under the Delaporte framework. Our findings highlighted that for the COVID-19 pandemic, population-wide interventions may control disease transmission on a general scale before recommending the high-risk-specific control strategies.

Superspreading is one of the key transmission features of many infectious diseases and is considered a consequence of the heterogeneity in infectiousness of individual cases. To characterize the superspreading potential, we divided individual infectiousness into two independent and additive components, including a fixed baseline and a variable part. Such decomposition produced an improvement in the fit of the model explaining the distribution of real-world datasets of COVID-19 and SARS that can be captured by the classic statistical tests. Disease control strategies may be developed by monitoring the characteristics of superspreading. For the COVID-19 pandemic, population-wide interventions are suggested first to limit the transmission at a scale of general population, and then high-risk-specific control strategies are recommended subsequently to lower the risk of superspreading.

Delineating privacy aspects of COVID tracing applications embedded with proximity measurement technologies & digital technologies

As the COVID-19 pandemic expanded over the globe, governments implemented a series of technological measures to prevent the disease's spread. The development of the COVID Tracing Application (CTA) was one of these measures. In this study, we employed bibliometric and topic-based content analysis to determine the most significant entities and research topics. Additionally, we identified significant privacy concerns posed by CTAs, which gather, store, and analyze data in partnership with large technology corporations using proximity measurement technologies, artificial intelligence, and blockchain. We examined a series of key privacy threats identified in our study. These privacy risks include anti-democratic and discriminatory behaviors, politicization of care, derogation of human rights, techno governance, citizen distrust and refusal to adopt, citizen surveillance, and mandatory legislation of the apps' installation. Finally, sixteen research gaps were identified. Then, based on the identified theoretical gaps, we recommended fourteen prospective study strands. Theoretically, this study contributes to the growing body of knowledge about the privacy of mobile health applications that are embedded with cutting-edge technologies and are employed during global pandemics.

Can Digital Transformation Promote the Rapid Recovery of Cities from the COVID-19 Epidemic? An Empirical Analysis from Chinese Cities

Background: Digital transformation has become a key intervention strategy for the global response to the COVID-19 epidemic, and digital technology is helping cities recover from the COVID-19 epidemic. However, the effects of urban digital transformation on the recovery from the COVID-19 epidemic still lack mechanism analyses and empirical testing. This study aimed to explain the theoretical mechanism of urban digital transformation on the recovery from the COVID-19 epidemic and to test its effectiveness using an empirical analysis. Methods: This study, using a theoretical and literature-based analysis, summarizes the impact mechanisms of urban digital transformation on the recovery of cities from the COVID-19 epidemic. A total of 83 large- and medium-sized cities from China are included in the empirical research sample, covering most major cities in China. The ordinary least squares (OLS) method is adopted to estimate the effect of China’s urban digitalization level on population attraction in the second quarter of 2020. Results: The theoretical analysis found that urban digital transformation improves the ability of cities to recover from the COVID-19 epidemic by promoting social communication, collaborative governance, and resilience. The main findings of the empirical analysis show that the digital level of a city has a significant positive effect on urban population attraction (p < 0.001). Conclusions: A positive relationship was found between urban digital transformation and the rapid recovery of cities from the COVID-19 epidemic. Digital inventions for social communication, collaborative governance, and urban resilience are an effective way of fighting the COVID-19 emergency.

Application of artificial intelligence in COVID-19 medical area: a systematic review

BACKGROUND

Coronavirus disease 2019 (COVID-19) has caused a large-scale global epidemic, impacting international politics and the economy. At present, there is no particularly effective medicine and treatment plan. Therefore, it is urgent and significant to find new technologies to diagnose early, isolate early, and treat early. Multimodal data drove artificial intelligence (AI) can potentially be the option. During the COVID-19 Pandemic, AI provided cutting-edge applications in disease, medicine, treatment, and target recognition. This paper reviewed the literature on the intersection of AI and medicine to analyze and compare different AI model applications in the COVID-19 Pandemic, evaluate their effectiveness, show their advantages and differences, and introduce the main models and their characteristics.

METHODS

We searched PubMed, arXiv, medRxiv, and Google Scholar through February 2020 to identify studies on AI applications in the medical areas for the COVID-19 Pandemic.

RESULTS

We summarize the main AI applications in six areas: (I) epidemiology, (II) diagnosis, (III) progression, (IV) treatment, (V) psychological health impact, and (VI) data security. The ongoing development in AI has significantly improved prediction, contact tracing, screening, diagnosis, treatment, medication, and vaccine development for the COVID-19 Pandemic and reducing human intervention in medical practice.

DISCUSSION

This paper provides strong advice for using AI-based auxiliary tools for related applications of human diseases. We also discuss the clinicians' role in the further development of AI. They and AI researchers can integrate AI technology with current clinical processes and information systems into applications. In the future, AI personnel and medical workers will further cooperate closely.

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.

A Web-Based Digital Contact Tracing Strategy Addresses Stigma Concerns Among Individuals Evaluated for COVID-19

Background: Conventional contact tracing approaches have not kept pace with the scale of the coronavirus disease 2019 (COVID-19) pandemic and the highly anticipated smartphone applications for digital contact tracing efforts are plagued by low adoption rates attributed to privacy concerns; therefore, innovation is needed in this public health capability. Methods: This study involved a cross-sectional, nonrepresentative, online survey in the United States of individuals tested for COVID-19. Testing survey items measured the performance of conventional contact tracing programs, quantified the stigma related to the notification of COVID-19 close contacts, and assessed the acceptability of a website service for digital contact tracing. Results: A sample of 668 (19.9%) individuals met the inclusion criteria and consented to participation. Among the 95 participants with COVID-19, results were received after a median of 2 days, 63.2% interacted with a contact tracing program a median of 2 days after receiving test results, 62.1% had close contacts, and 37.1% of participants with COVID-19 and close contacts did not disclose their results to all close contacts. Among all participants, 17% had downloaded a mobile application and 40.3% reported interest in a website service. One hundred and nine participants perceived stigma with the disclosure of COVID-19 test results; of these, 58.7% reported that a website service for close contact notification would decrease this stigma. Discussion: Conventional contact tracing programs did not comprehensively contact individuals who tested positive for COVID-19 nor did so within a meaningful time frame. Digital contact tracing innovations may address these shortcomings; however, the low penetration of mobile application services in the United States indicates that a suite of digital contact tracing tools, including website services, are warranted for a more exhaustive coverage of the population. Conclusions: Public health officials should develop a complementary toolkit of digital contact tracing strategies to enable effective pandemic containment strategies.