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Smartphone-based apps designed and deployed to mitigate the COVID-19 pandemic may become infrastructure for postpandemic public health surveillance in the United States. Through the lenses of privacy concerns and user expectations of digital pandemic mitigation techniques, we identified possible long-term sociotechnical implications of such an infrastructure. We explored how people in the United States perceive the possible routinization of pandemic tracking apps for public health surveillance in general. Our interdisciplinary analysis focused on the interplay between privacy concerns, data practices of surveillance capitalism, and trust in health care providers. We conducted this analysis to achieve a richer understanding of the sociotechnical issues raised by the deployment and use of technology for pandemic mitigation. We conducted scenario-based, semistructured interviews (n=19) with adults in the United States. The interviews focused on how people perceive the short- and long-term privacy concerns associated with a fictional smart thermometer app deployed to mitigate the \"outbreak of a contagious disease.\" In order to elicit future-oriented discussions, the scenario indicated that the app would continue functioning \"after the disease outbreak has dissipated.\" We analyzed interview transcripts using reflexive thematic analysis. In the context of pandemic mitigation technology, including app-based tracking, people perceive a core trade-off between public health and personal privacy. People tend to rationalize this trade-off by invoking the concept of \"the greater good.\" The interplay between the trade-off and rationalization forms the core of sociotechnical issues that pandemic mitigation technologies raise. Participants routinely expected that data collected through apps related to public health would be shared with unknown third parties for the financial gain of the app makers. This expectation suggests a perceived alignment between an app-based infrastructure for public health surveillance and the broader economics of surveillance capitalism. Our results highlight unintended and unexpected sociotechnical impacts of routinizing app-based tracking on postpandemic life, which are rationalized by invoking a nebulous concept of the greater good. While technologies such as app-based tracking could be useful for pandemic mitigation and preparedness, the routinization of such apps as a form of public health surveillance may have broader, unintentional sociotechnical implications for individuals and the societies in which they live. Although technology has the potential to increase the efficacy of pandemic mitigation, it exists within a broader network of sociotechnical concerns. Therefore, it is necessary to consider the long-term implications of pandemic mitigation technologies beyond the immediate needs of addressing the COVID-19 pandemic. Potential negative consequences include the erosion of patient trust in health care systems and providers, grounded in concerns about privacy violations and overly broad surveillance.

Background and Aim Understanding the prevalence and impact of SARS‐CoV‐2 variants has assumed paramount importance. This study statistically analyzed to effectively track the emergence and spread of the variants and highlights the importance of such investigations in developing potential next‐gen vaccine to combat the continuously emerging Omicron subvariants. Methods Transmission fitness advantage and effective reproductive number (Re) of epidemiologically relevant SARS‐CoV‐2 sublineages through time during the study period based on the GISAID data were estimated. Results The analyses covered the period from January to June 2023 around an array of sequenced samples. The dominance of the XBB variant strain, accounting for approximately 57.63% of the cases, was identified during the timeframe. XBB.1.5 exhibited 37.95% prevalence rate from March to June 2023. Multiple variants showed considerable global influence throughout the study, as sporadically documented. Notably, the XBB variant demonstrated an estimated relative 28% weekly growth advantage compared with others. Numerous variants were resistant to the over‐the‐counter vaccines and breakthrough infections were reported. Similarly, the efficacy of mAB‐based therapy appeared limited. However, it's important to underscore the perceived benefits of these preventive and therapeutic measures were restricted to specific variants. Conclusion Given the observed trends, a comprehensive next‐gen vaccine coupled with an advanced vaccination strategy could be a potential panacea in the fight against the pandemic. The findings suggest that targeted vaccine development could be an effective strategy to prevent infections. The study also highlights the need of global collaborations to rapidly develop and distribute the vaccines to ensure global human health.

Big data analytics (BDA) in healthcare has made a positive difference in the integration of Artificial Intelligence (AI) in advancements of analytical capabilities, while lowering the costs of medical care. The aim of this study is to improve the existing healthcare eSystem by implementing a Big Data Analytics (BDA) platform and to meet the requirements of the Czech Republic National Health Service (Tender-Id. VZ0036628, No. Z2017-035520). In addition to providing analytical capabilities on Linux platforms supporting current and near-future AI with machine-learning and data-mining algorithms, there is the need for ethical considerations mandating new ways to preserve privacy, all of which are preconditioned by the growing body of regulations and expectations. The presented BDA platform, has met all requirements (N > 100), including the healthcare industry-standard Transaction Processing Performance Council (TPC-H) decision support benchmark in compliance with the European Union (EU) and the Czech Republic legislations. Currently, the presented Proof of Concept (PoC) that has been upgraded to a production environment has unified isolated parts of Czech Republic healthcare over the past seven months. The reported PoC BDA platform, artefacts, and concepts are transferrable to healthcare systems in other countries interested in developing or upgrading their own national healthcare infrastructure in a cost-effective, secure, scalable and high-performance manner.

The coronavirus disease 2019 (COVID-19) pandemic has led to a large increase in mortality in the United States and around the world, leaving many grieving the sudden loss of family members. We created an indicator—the COVID-19 bereavement multiplier—that estimates the average number of individuals who will experience the death of a close relative (defined as a grandparent, parent, sibling, spouse, or child) for each COVID-19 death. Using demographic microsimulation-based estimates of kinship networks in the United States, the clear age gradient in COVID-19 mortality seen across contexts, and several hypothetical infection prevalence scenarios, we estimate COVID-19 bereavement multipliers for White and Black individuals in the United States. Our analysis shows that for every COVID-19 death, approximately nine surviving Americans will lose a grandparent, parent, sibling, spouse, or child. These estimates imply, for example, that if 190,000 Americans die from COVID-19, as some models project, then ~1.7 million will experience the death of a close relative. We demonstrate that our estimates of the bereavement multiplier are stable across epidemiological realities, including infection scenarios, total number of deaths, and the distribution of deaths, which means researchers can estimate the bereavement burden over the course of the epidemic in lockstep with rising death tolls. In addition, we provide estimates of bereavement multipliers by age group, types of kin loss, and race to illuminate prospective disparities. The bereavementmultiplier is a useful indicator for tracking COVID-19’s multiplicative impact as it reverberates across American families and can be tailored to other causes of death.

In the context of home confinement during the coronavirus disease (COVID-19) pandemic, objective, real-time data are needed to assess populations' adherence to home confinement to adapt policies and control measures accordingly. The aim of this study was to determine whether wearable activity trackers could provide information regarding users' adherence to home confinement policies because of their capacity for seamless and continuous monitoring of individuals' natural activity patterns regardless of their location. We analyzed big data from individuals using activity trackers (Withings) that count the wearer's average daily number of steps in a number of representative nations that adopted different modalities of restriction of citizens' activities. Data on the number of steps per day from over 740,000 individuals around the world were analyzed. We demonstrate the physical activity patterns in several representative countries with total, partial, or no home confinement. The decrease in steps per day in regions with strict total home confinement ranged from 25% to 54%. Partial lockdown (characterized by social distancing measures such as school closures, bar and restaurant closures, and cancellation of public meetings but without strict home confinement) does not appear to have a significant impact on people's activity compared to the pre-pandemic period. The absolute level of physical activity under total home confinement in European countries is around twofold that in China. In some countries, such as France and Spain, physical activity started to gradually decrease even before official commitment to lockdown as a result of initial less stringent restriction orders or self-quarantine. However, physical activity began to increase again in the last 2 weeks, suggesting a decrease in compliance with confinement orders. Aggregate analysis of activity tracker data with the potential for daily updates can provide information regarding adherence to home confinement policies.

In response to the COVID-19 pandemic, numerous countries, including the likes of Japan and Germany, initiated, developed, and deployed digital contact tracing solutions in an effort to detect and interrupt COVID-19 transmission chains. These initiatives indicated the willingness of both the Japanese and German governments to support eHealth solution development for public health; however, end user acceptance, trust, and willingness to make use of the solutions delivered through these initiatives are critical to their success. Through a case-based analysis of contact tracing solutions deployed in Japan and Germany during the COVID-19 pandemic we may gain valuable perspectives on the transnational role of digital technologies in crises, while also projecting possible directions for future pandemic technologies. In this study, we investigate (1) which types of digital contact tracing solutions were developed and deployed by the Japanese and German governments in response to the COVID-19 pandemic and (2) how many of these solutions are open-source software (OSS) solutions. Our objective is to establish not only the type of applications that may be needed in response to a pandemic from the perspective of 2 geographically diverse, world-leading economies but also how prevalent OSS pandemic technology development has been in this context. We analyze the official government websites of Japan and Germany to identify digital solutions that are developed and deployed for contact tracing purposes (for any length of time) during the timeframe January-December 2021, specifically in response to the COVID-19 pandemic. We subsequently perform a case-oriented comparative analysis, also identifying which solutions are published as open-source. In Japan, a proximity tracing tool (COVID-19 Contact-Confirming Application [COCOA]) and an outbreak management tool (Health Center Real-time Information-sharing System on COVID-19 [HER-SYS]) with an integrated symptom tracking tool (My HER-SYS) were developed. In Germany, a proximity tracing tool (Corona-Warn-App) and an outbreak management tool (Surveillance Outbreak Response Management and Analysis System [SORMAS]) were developed. From these identified solutions, COCOA, Corona-Warn-App, and SORMAS were published as open-source, indicating support by both the Japanese and German governments for OSS pandemic technology development in the context of public health. Japan and Germany showed support for developing and deploying not only digital contact tracing solutions but also OSS digital contact tracing solutions in response to the COVID-19 pandemic. Despite the open nature of such OSS solutions' source code, software solutions (both OSS and non-OSS) are only as transparent as the live or production environment where their processed data is hosted or stored. Software development and live software hosting are thus 2 sides of the same coin. It is nonetheless arguable that OSS pandemic technology solutions for public health are a step in the right direction for enhanced transparency in the interest of the greater public good.

In the prevention and control of infectious diseases, previous research on the application of big data technology has mainly focused on the early warning and early monitoring of infectious diseases. Although the application of big data technology for COVID-19 warning and monitoring remain important tasks, prevention of the disease's rapid spread and reduction of its impact on society are currently the most pressing challenges for the application of big data technology during the COVID-19 pandemic. After the outbreak of COVID-19 in Wuhan, the Chinese government and nongovernmental organizations actively used big data technology to prevent, contain, and control the spread of COVID-19. The aim of this study is to discuss the application of big data technology to prevent, contain, and control COVID-19 in China; draw lessons; and make recommendations. We discuss the data collection methods and key data information that existed in China before the outbreak of COVID-19 and how these data contributed to the prevention and control of COVID-19. Next, we discuss China's new data collection methods and new information assembled after the outbreak of COVID-19. Based on the data and information collected in China, we analyzed the application of big data technology from the perspectives of data sources, data application logic, data application level, and application results. In addition, we analyzed the issues, challenges, and responses encountered by China in the application of big data technology from four perspectives: data access, data use, data sharing, and data protection. Suggestions for improvements are made for data collection, data circulation, data innovation, and data security to help understand China's response to the epidemic and to provide lessons for other countries' prevention and control of COVID-19. In the process of the prevention and control of COVID-19 in China, big data technology has played an important role in personal tracking, surveillance and early warning, tracking of the virus's sources, drug screening, medical treatment, resource allocation, and production recovery. The data used included location and travel data, medical and health data, news media data, government data, online consumption data, data collected by intelligent equipment, and epidemic prevention data. We identified a number of big data problems including low efficiency of data collection, difficulty in guaranteeing data quality, low efficiency of data use, lack of timely data sharing, and data privacy protection issues. To address these problems, we suggest unified data collection standards, innovative use of data, accelerated exchange and circulation of data, and a detailed and rigorous data protection system. China has used big data technology to prevent and control COVID-19 in a timely manner. To prevent and control infectious diseases, countries must collect, clean, and integrate data from a wide range of sources; use big data technology to analyze a wide range of big data; create platforms for data analyses and sharing; and address privacy issues in the collection and use of big data.

Physical distancing, as a measure to contain the spreading of Covid-19, is defining a \"new normal\". Unless belonging to a family, pedestrians in shared spaces are asked to observe a minimal (country-dependent) pairwise distance. Coherently, managers of public spaces may be tasked with the enforcement or monitoring of this constraint. As privacy-respectful real-time tracking of pedestrian dynamics in public spaces is a growing reality, it is natural to leverage on these tools to analyze the adherence to physical distancing and compare the effectiveness of crowd management measurements. Typical questions are: \"in which conditions non-family members infringed social distancing?\", \"Are there repeated offenders?\", and \"How are new crowd management measures performing?\". Notably, dealing with large crowds, e.g. in train stations, gets rapidly computationally challenging. In this work we have a two-fold aim: first, we propose an efficient and scalable analysis framework to process, offline or in real-time, pedestrian tracking data via a sparse graph. The framework tackles efficiently all the questions mentioned above, representing pedestrian-pedestrian interactions via vector-weighted graph connections. On this basis, we can disentangle distance offenders and family members in a privacy-compliant way. Second, we present a thorough analysis of mutual distances and exposure-times in a Dutch train platform, comparing pre-Covid and current data via physics observables as Radial Distribution Functions. The versatility and simplicity of this approach, developed to analyze crowd management measures in public transport facilities, enable to tackle issues beyond physical distancing, for instance the privacy-respectful detection of groups and the analysis of their motion patterns.

COVID-19 CG ( covidcg.org ) is an open resource for tracking SARS-CoV-2 single-nucleotide variations (SNVs), lineages, and clades using the virus genomes on the GISAID database while filtering by location, date, gene, and mutation of interest. COVID-19 CG provides significant time, labor, and cost-saving utility to projects on SARS-CoV-2 transmission, evolution, diagnostics, therapeutics, vaccines, and intervention tracking. Here, we describe case studies in which users can interrogate (1) SNVs in the SARS-CoV-2 spike receptor binding domain (RBD) across different geographical regions to inform the design and testing of therapeutics, (2) SNVs that may impact the sensitivity of commonly used diagnostic primers, and (3) the emergence of a dominant lineage harboring an S477N RBD mutation in Australia in 2020. To accelerate COVID-19 efforts, COVID-19 CG will be upgraded with new features for users to rapidly pinpoint mutations as the virus evolves throughout the pandemic and in response to therapeutic and public health interventions. The discovery of faster spreading variants of the virus that causes coronavirus disease 2019 (COVID-19) has raised alarm. These new variants are the result of changes (called mutations) in the virus’ genetic code. Random mutations can occur each time a virus multiplies. Although most mutations do not introduce any meaningful changes, some can alter the characteristics of the virus, for instance, helping the virus to spread more easily, reinfecting people who have had COVID-19 before, or reducing the sensitivity to treatments or vaccines. Scientists need to know about mutations in the virus that make treatments or vaccines less effective as soon as possible, so they can adjust their pandemic response. As a result, tracking these genetic changes is essential. But individual scientists or public health agencies may not have the staff, time or computer resources to extract usable information from the growing amount of genetic data available. A free online tool created by Chen et al. may help scientists and public health officials to track changes to the virus more easily. The COVID-19 CoV Genetics tool (COVID-19 CG) can quickly provide information on which virus mutations are present in an area during a specific period. It does this by processing data on mutations found in viral genetic material collected worldwide from hundreds of thousands of people with COVID-19, which are hosted in an existing online database. The COVID-19 CG tool presents customizable, interactive visualizations of the data. Thousands of scientists, public health agencies, and COVID-19 vaccine and treatment developers in over 100 countries are already using the COVID-19 CG tool to find the most common mutations in their area and use it for research. They can use this information to develop more effective vaccines or treatments. Chen et al. plan to update and improve the tool as more information becomes available to help advance global efforts to end the COVID-19 pandemic.

New technological solutions play an important role in preventing the spread of Covid-19. Many countries have implemented tracking applications or other surveillance systems, which may raise concerns about privacy and civil rights violations but may be also perceived by citizens as a way to reduce threat and uncertainty. Our research examined whether feelings evoked by the pandemic (perceived threat and lack of control) as well as more stable ideological views predict the acceptance of such technologies. In two studies conducted in Poland, we found that perceived personal threat and lack of personal control were significantly positively related to the acceptance of surveillance technologies, but their predictive value was smaller than that of individual differences in authoritarianism and endorsement of liberty. Moreover, we found that the relationship between the acceptance of surveillance technologies and both perceived threat and lack of control was particularly strong among people high in authoritarianism. Our research shows that the negative feelings evoked by the unprecedented global crisis may inspire positive attitudes towards helpful but controversial surveillance technologies but that they do so to a lesser extent than ideological beliefs.