The Development of a Chatbot Technology to Disseminate Post-COVID-19 Information: Descriptive Implementation Study.

BACKGROUND: Post-COVID-19, or long COVID, has now affected millions of individuals, resulting in fatigue, neurocognitive symptoms, and an impact on daily life. The uncertainty of knowledge around this condition, including its overall prevalence, pathophysiology, and management, along with the growing numbers of affected individuals, has created an essential need for information and disease management. This has become even more critical in a time of abundant online misinformation and potential misleading of patients and health care professionals. OBJECTIVE: The RAFAEL platform is an ecosystem created to address the information about and management of post-COVID-19, integrating online information, webinars, and chatbot technology to answer a large number of individuals in a time- and resource-limited setting. This paper describes the development and deployment of the RAFAEL platform and chatbot in addressing post-COVID-19 in children and adults. METHODS: The RAFAEL study took place in Geneva, Switzerland. The RAFAEL platform and chatbot were made available online, and all users were considered participants of this study. The development phase started in December 2020 and included developing the concept, the backend, and the frontend, as well as beta testing. The specific strategy behind the RAFAEL chatbot balanced an accessible interactive approach with medical safety, aiming to relay correct and verified information for the management of post-COVID-19. Development was followed by deployment with the establishment of partnerships and communication strategies in the French-speaking world. The use of the chatbot and the answers provided were continuously monitored by community moderators and health care professionals, creating a safe fallback for users. RESULTS: To date, the RAFAEL chatbot has had 30,488 interactions, with an 79.6% (6417/8061) matching rate and a 73.2% (n=1795) positive feedback rate out of the 2451 users who provided feedback. Overall, 5807 unique users interacted with the chatbot, with 5.1 interactions per user, on average, and 8061 stories triggered. The use of the RAFAEL chatbot and platform was additionally driven by the monthly thematic webinars as well as communication campaigns, with an average of 250 participants at each webinar. User queries included questions about post-COVID-19 symptoms (n=5612, 69.2%), of which fatigue was the most predominant query (n=1255, 22.4%) in symptoms-related stories. Additional queries included questions about consultations (n=598, 7.4%), treatment (n=527, 6.5%), and general information (n=510, 6.3%). CONCLUSIONS: The RAFAEL chatbot is, to the best of our knowledge, the first chatbot developed to address post-COVID-19 in children and adults. Its innovation lies in the use of a scalable tool to disseminate verified information in a time- and resource-limited environment. Additionally, the use of machine learning could help professionals gain knowledge about a new condition, while concomitantly addressing patients' concerns. Lessons learned from the RAFAEL chatbot will further encourage a participative approach to learning and could potentially be applied to other chronic conditions.

Digital Disease Surveillance for Emerging Infectious Diseases: An Early Warning System Using the Internet and Social Media Data for COVID-19 Forecasting in Canada.

BACKGROUND: Emerging Infectious Diseases (EID) are a significant threat to population health globally. We aimed to examine the relationship between internet search engine queries and social media data on COVID-19 and determine if they can predict COVID-19 cases in Canada. METHODS: We analyzed Google Trends (GT) and Twitter data from 1/1/2020 to 3/31/2020 in Canada and used various signal-processing techniques to remove noise from the data. Data on COVID-19 cases was obtained from the COVID-19 Canada Open Data Working Group. We conducted time-lagged cross-correlation analyses and developed the long short-term memory model for forecasting daily COVID-19 cases. RESULTS: Among symptom keywords, "cough," "runny nose," and "anosmia" were strong signals with high cross-correlation coefficients >0.8 ( rCough = 0.825, t - 9; rRunnyNose = 0.816, t - 11; rAnosmia = 0.812, t - 3 ), showing that searching for "cough," "runny nose," and "anosmia" on GT correlated with the incidence of COVID-19 and peaked 9, 11, and 3 days earlier than the incidence peak, respectively. For symptoms- and COVID-related Tweet counts, the cross-correlations of Tweet signals and daily cases were rTweetSymptoms = 0.868, t - 11 and tTweetCOVID = 0.840, t - 10, respectively. The LSTM forecasting model achieved the best performance (MSE = 124.78, R2 = 0.88, adjusted R2 = 0.87) using GT signals with cross-correlation coefficients >0.75. Combining GT and Tweet signals did not improve the model performance. CONCLUSION: Internet search engine queries and social media data can be used as early warning signals for creating a real-time surveillance system for COVID-19 forecasting, but challenges remain in modelling.

The Relationship Between Face Mask Use and Face-Touching Frequency in Public Areas: Naturalistic Study.

BACKGROUND: Throughout the COVID-19 pandemic in the United States, a major public health goal has been reducing the spread of the virus, with particular emphasis on reducing transmission from person to person. Frequent face touching can transmit viral particles from one infected person and subsequently infect others in a public area. This raises an important concern about the use of face masks and their relationship with face-touching behaviors. One concern discussed during the pandemic is that wearing a mask, and different types of masks, could increase face touching because there is a need to remove the mask to smoke, drink, eat, etc. To date, there have been few studies that have assessed this relationship between mask wearing and the frequency of face touching relative to face-touching behaviors. OBJECTIVE: This study aimed to compare the frequency of face touching in people wearing a mask versus not wearing a mask in high-foot traffic urban outdoor areas. The purpose of this study was to assess if mask wearing was associated with increased face touching. METHODS: Public webcam videos from 4 different cities in New York, New Jersey, Louisiana, and Florida were used to collect data. Face touches were recorded as pedestrians passed under the webcam. Adult pedestrians wearing masks were compared to those not wearing masks. Quantitative measures of frequency, duration, site of touch, and oral activities were recorded. Linear regression analysis was used to assess the association between mask use and face touching. RESULTS: Of the 490 observed subjects, 241 (49.2%) were wearing a mask properly and 249 (50.8%) were not. In the unmasked group, 33.7% (84/249) were wearing it improperly, covering the mouth only. Face touching occurred in 11.4% (56/490) of the masked group and 17.6% (88/490) in the unmasked group. Of those who touched their face, 61.1% (88/144) of people were not wearing a mask. The most common site of face touching was the perioral region in both groups. Both the masked and unmasked group had a frequency of face touching for 0.03 touches/s. Oral activities such as eating or smoking increased face touching in the unmasked group. CONCLUSIONS: Contrary to expectations, non-mask-wearing subjects touched their face more frequently than those who were wearing a mask. This finding is substantial because wearing a face mask had a negative association with face touching. When wearing a mask, individuals are less likely to be spreading and ingesting viral particles. Therefore, wearing a mask is more effective in preventing the spread of viral particles.

Artificial intelligence in public health: the potential of epidemic early warning systems.

The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to-not a replacement of-traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.

Emerging data inputs for infectious diseases surveillance and decision making.

Infectious diseases create a significant health and social burden globally and can lead to outbreaks and epidemics. Timely surveillance for infectious diseases is required to inform both short and long term public responses and health policies. Novel data inputs for infectious disease surveillance and public health decision making are emerging, accelerated by the COVID-19 pandemic. These include the use of technology-enabled physiological measurements, crowd sourcing, field experiments, and artificial intelligence (AI). These technologies may provide benefits in relation to improved timeliness and reduced resource requirements in comparison to traditional methods. In this review paper, we describe current and emerging data inputs being used for infectious disease surveillance and summarize key benefits and limitations.

COVID-19, Personal Data Protection and Privacy in India.

The corona pandemic altered many traditional and historical norms of society and law. COVID-19 created a humanitarian crisis in some parts of globe, while pandemic privacy and civil liberties were under threat all over world. To combat the deadly virus, individual liberty and equality were compromised. This paper focuses on how India's health problem has compromised people's right to privacy. It will highlight how strict executive policies led to the creation of a massive surveillance system in the name of combating the COVID-19 pandemic, as well as how the absence of any policy or legal framework led to the exclusion of individuals and their families who were suspected of having the virus or caring for those who were infected with the deadly virus. The paper uses case studies and data collected from primary as well as secondary sources. The authors will also point out how the absence of privacy regulation puts millions of citizens' private information at risk of being compromised or exploited against their will.

Time-Space Companions: Digital Surveillance, Social Management, and Abuse of Power During the Covid-19 Pandemic in China

Based on ethnographic research carried out during the 2022 Covid-19 surge in southern China, this paper examines the roll-out of a contact-tracing tool called the Time-Space Companion project. The project exemplifies a state effort to incorporate data-driven surveillance technology into the public health apparatus during the coronavirus outbreak. By exploring the definition, identification, and management of Time-Space Companions, the paper shows that the project was used to discipline Chinese citizens and shift public health responsibilities onto them by transforming daily life into sites of public health regulation, discipline, and criminalization. The project also exemplified an on-going state effort to leverage surveillance technologies for the purposes of social management. The paper draws attention to the social repercussions that resulted when technology offered a tempting tool to enhance the infrastructural and despotic powers of mundane state actors. © 2023 BCAS, Inc.

Dining-Out Behavior as a Proxy for the Superspreading Potential of SARS-CoV-2 Infections: Modeling Analysis.

BACKGROUND: While many studies evaluated the reliability of digital mobility metrics as a proxy of SARS-CoV-2 transmission potential, none examined the relationship between dining-out behavior and the superspreading potential of COVID-19. OBJECTIVE: We employed the mobility proxy of dining out in eateries to examine this association in Hong Kong with COVID-19 outbreaks highly characterized by superspreading events. METHODS: We retrieved the illness onset date and contact-tracing history of all laboratory-confirmed cases of COVID-19 from February 16, 2020, to April 30, 2021. We estimated the time-varying reproduction number (Rt) and dispersion parameter (k), a measure of superspreading potential, and related them to the mobility proxy of dining out in eateries. We compared the relative contribution to the superspreading potential with other common proxies derived by Google LLC and Apple Inc. RESULTS: A total of 6391 clusters involving 8375 cases were used in the estimation. A high correlation between dining-out mobility and superspreading potential was observed. Compared to other mobility proxies derived by Google and Apple, the mobility of dining-out behavior explained the highest variability of k (ΔR-sq=9.7%, 95% credible interval: 5.7% to 13.2%) and Rt (ΔR-sq=15.7%, 95% credible interval: 13.6% to 17.7%). CONCLUSIONS: We demonstrated that there was a strong link between dining-out behaviors and the superspreading potential of COVID-19. The methodological innovation suggests a further development using digital mobility proxies of dining-out patterns to generate early warnings of superspreading events.

Emotional Distress During COVID-19 by Mental Health Conditions and Economic Vulnerability: Retrospective Analysis of Survey-Linked Twitter Data With a Semisupervised Machine Learning Algorithm.

BACKGROUND: Monitoring the psychological conditions of social media users during rapidly developing public health crises, such as the COVID-19 pandemic, using their posts on social media has rapidly gained popularity as a relatively easy and cost-effective method. However, the characteristics of individuals who created these posts are largely unknown, making it difficult to identify groups of individuals most affected by such crises. In addition, large annotated data sets for mental health conditions are not easily available, and thus, supervised machine learning algorithms can be infeasible or too costly. OBJECTIVE: This study proposes a machine learning framework for the real-time surveillance of mental health conditions that does not require extensive training data. Using survey-linked tweets, we tracked the level of emotional distress during the COVID-19 pandemic by the attributes and psychological conditions of social media users in Japan. METHODS: We conducted online surveys of adults residing in Japan in May 2022 and collected their basic demographic information, socioeconomic status, and mental health conditions, along with their Twitter handles (N=2432). We computed emotional distress scores for all the tweets posted by the study participants between January 1, 2019, and May 30, 2022 (N=2,493,682) using a semisupervised algorithm called latent semantic scaling (LSS), with higher values indicating higher levels of emotional distress. After excluding users by age and other criteria, we examined 495,021 (19.85%) tweets generated by 560 (23.03%) individuals (age 18-49 years) in 2019 and 2020. We estimated fixed-effect regression models to examine their emotional distress levels in 2020 relative to the corresponding weeks in 2019 by the mental health conditions and characteristics of social media users. RESULTS: The estimated level of emotional distress of our study participants increased in the week when school closure started (March 2020), and it peaked at the beginning of the state of emergency (estimated coefficient=0.219, 95% CI 0.162-0.276) in early April 2020. Their level of emotional distress was unrelated to the number of COVID-19 cases. We found that the government-induced restrictions disproportionately affected the psychological conditions of vulnerable individuals, including those with low income, precarious employment, depressive symptoms, and suicidal ideation. CONCLUSIONS: This study establishes a framework to implement near-real-time monitoring of the emotional distress level of social media users, highlighting a great potential to continuously monitor their well-being using survey-linked social media posts as a complement to administrative and large-scale survey data. Given its flexibility and adaptability, the proposed framework is easily extendable for other purposes, such as detecting suicidality among social media users, and can be used on streaming data for continuous measurement of the conditions and sentiment of any group of interest.

Monitoring Symptoms of COVID-19: Review of Mobile Apps.

BACKGROUND: Mobile health (mHealth) apps have facilitated symptom monitoring of COVID-19 symptoms globally and have been used to share data with health care professionals and support disease prediction, prevention, management, diagnostics, and improvements in treatments and patient education. OBJECTIVE: The aim of this review is to evaluate the quality and functionality of COVID-19 mHealth apps that support tracking acute and long-term symptoms of COVID-19. METHODS: We systematically reviewed commercially available mHealth apps for COVID-19 symptom monitoring by searching Google Play and Apple iTunes using search terms such as "COVID-19," "Coronavirus," and "COVID-19 and symptoms." All apps underwent three rounds of screening. The final apps were independently assessed using the Mobile Application Rating Scale (MARS), an informatics functionality scoring system, and the Center for Disease Control and World Health Organization symptom guidelines. The MARS is a 19-item standardized tool to evaluate the quality of mHealth apps on engagement, functionality, aesthetics, and information quality. Functionality was quantified across the following criteria: inform, instruct, record (collect, share, evaluate, and intervene), display, guide, remind or alert, and communicate. Interrater reliability between the reviewers was calculated. RESULTS: A total of 1017 mobile apps were reviewed, and 20 (2%) met the inclusion criteria. The majority of the 20 included apps (n=18, 90%) were designed to track acute COVID-19 symptoms, and only 2 (10%) addressed long-term symptoms. Overall, the apps scored high on quality, with an overall MARS rating of 3.89 out of 5, and the highest domain score for functionality (4.2). The most common functionality among all apps was the instruct function (n=19, 95%). The most common symptoms included in the apps for tracking were fever and dry cough (n=18, 90%), aches and pains (n=17, 85%), difficulty breathing (n=17, 85%), tiredness, sore throat, headache, loss of taste or smell (n=16, 80%), and diarrhea (n=15, 75%). Only 2 (10%) apps specifically tracked long-term symptoms of COVID-19. The top 4 rated apps overall were state-specific apps developed and deployed for public use. CONCLUSIONS: Overall, mHealth apps designed to monitor symptoms of COVID-19 were of high quality, but the majority of apps focused almost exclusively on acute symptoms. Future apps should also incorporate monitoring long-term symptoms of COVID-19 and evidence-based educational materials; they should also include a feature that would allow patients to communicate their symptoms to specific caregivers or their own health care team. App developers should also follow updated technical and clinical guidelines from the Center for Disease Control and the World Health Organization.

Representativeness, Vaccination Uptake, and COVID-19 Clinical Outcomes 2020-2021 in the UK Oxford-Royal College of General Practitioners Research and Surveillance Network: Cohort Profile Summary.

BACKGROUND: The Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) is one of Europe's oldest sentinel systems, working with the UK Health Security Agency (UKHSA) and its predecessor bodies for 55 years. Its surveillance report now runs twice weekly, supplemented by online observatories. In addition to conducting sentinel surveillance from a nationally representative group of practices, the RSC is now also providing data for syndromic surveillance. OBJECTIVE: The aim of this study was to describe the cohort profile at the start of the 2021-2022 surveillance season and recent changes to our surveillance practice. METHODS: The RSC's pseudonymized primary care data, linked to hospital and other data, are held in the Oxford-RCGP Clinical Informatics Digital Hub, a Trusted Research Environment. We describe the RSC's cohort profile as of September 2021, divided into a Primary Care Sentinel Cohort (PCSC)-collecting virological and serological specimens-and a larger group of syndromic surveillance general practices (SSGPs). We report changes to our sampling strategy that brings the RSC into alignment with European Centre for Disease Control guidance and then compare our cohort's sociodemographic characteristics with Office for National Statistics data. We further describe influenza and COVID-19 vaccine coverage for the 2020-2021 season (week 40 of 2020 to week 39 of 2021), with the latter differentiated by vaccine brand. Finally, we report COVID-19-related outcomes in terms of hospitalization, intensive care unit (ICU) admission, and death. RESULTS: As a response to COVID-19, the RSC grew from just over 500 PCSC practices in 2019 to 1879 practices in 2021 (PCSC, n=938; SSGP, n=1203). This represents 28.6% of English general practices and 30.59% (17,299,780/56,550,136) of the population. In the reporting period, the PCSC collected >8000 virology and >23,000 serology samples. The RSC population was broadly representative of the national population in terms of age, gender, ethnicity, National Health Service Region, socioeconomic status, obesity, and smoking habit. The RSC captured vaccine coverage data for influenza (n=5.4 million) and COVID-19, reporting dose one (n=11.9 million), two (n=11 million), and three (n=0.4 million) for the latter as well as brand-specific uptake data (AstraZeneca vaccine, n=11.6 million; Pfizer, n=10.8 million; and Moderna, n=0.7 million). The median (IQR) number of COVID-19 hospitalizations and ICU admissions was 1181 (559-1559) and 115 (50-174) per week, respectively. CONCLUSIONS: The RSC is broadly representative of the national population; its PCSC is geographically representative and its SSGPs are newly supporting UKHSA syndromic surveillance efforts. The network captures vaccine coverage and has expanded from reporting primary care attendances to providing data on onward hospital outcomes and deaths. The challenge remains to increase virological and serological sampling to monitor the effectiveness and waning of all vaccines available in a timely manner.

Exploring the Utility of Google Mobility Data During the COVID-19 Pandemic in India: Digital Epidemiological Analysis.

BACKGROUND: Association between human mobility and disease transmission has been established for COVID-19, but quantifying the levels of mobility over large geographical areas is difficult. Google has released Community Mobility Reports (CMRs) containing data about the movement of people, collated from mobile devices. OBJECTIVE: The aim of this study is to explore the use of CMRs to assess the role of mobility in spreading COVID-19 infection in India. METHODS: In this ecological study, we analyzed CMRs to determine human mobility between March and October 2020. The data were compared for the phases before the lockdown (between March 14 and 25, 2020), during lockdown (March 25-June 7, 2020), and after the lockdown (June 8-October 15, 2020) with the reference periods (ie, January 3-February 6, 2020). Another data set depicting the burden of COVID-19 as per various disease severity indicators was derived from a crowdsourced API. The relationship between the two data sets was investigated using the Kendall tau correlation to depict the correlation between mobility and disease severity. RESULTS: At the national level, mobility decreased from -38% to -77% for all areas but residential (which showed an increase of 24.6%) during the lockdown compared to the reference period. At the beginning of the unlock phase, the state of Sikkim (minimum cases: 7) with a -60% reduction in mobility depicted more mobility compared to -82% in Maharashtra (maximum cases: 1.59 million). Residential mobility was negatively correlated (-0.05 to -0.91) with all other measures of mobility. The magnitude of the correlations for intramobility indicators was comparatively low for the lockdown phase (correlation ≥0.5 for 12 indicators) compared to the other phases (correlation ≥0.5 for 45 and 18 indicators in the prelockdown and unlock phases, respectively). A high correlation coefficient between epidemiological and mobility indicators was observed for the lockdown and unlock phases compared to the prelockdown phase. CONCLUSIONS: Mobile-based open-source mobility data can be used to assess the effectiveness of social distancing in mitigating disease spread. CMR data depicted an association between mobility and disease severity, and we suggest using this technique to supplement future COVID-19 surveillance.

Privacy at risk? Understanding the perceived privacy protection of health code apps in China

As a key constituent of China's approach to fighting COVID-19, Health Code apps (HCAs) not only serve the pandemic control imperatives but also exercise the agency of digital surveillance. As such, HCAs pave a new avenue for ongoing discussions on contact tracing solutions and privacy amid the global pandemic. This article attends to the perceived privacy protection among HCA users via the lens of the contextual integrity theory. Drawing on an online survey of adult HCA users in Wuhan and Hangzhou (N = 1551), we find users’ perceived convenience, attention towards privacy policy, trust in government, and acceptance of government purposes regarding HCA data management are significant contributors to users’ perceived privacy protection in using the apps. By contrast, users’ frequency of mobile privacy protection behaviors has limited influence, and their degrees of perceived protection do not vary by sociodemographic status. These findings shed new light on China's distinctive approach to pandemic control with respect to the state's expansion of big data-driven surveillance capacity. Also, the findings foreground the heuristic value of contextual integrity theory to examine controversial digital surveillance in non-Western contexts. Put tougher, our findings contribute to the thriving scholarly conversations around digital privacy and surveillance in China, as well as contact tracing solutions and privacy amid the global pandemic. © The Author(s) 2022.

Moderate support for the use of digital tracking to support climate-mitigation strategies.

The use of digital tracking of individuals throughout the coronavirus 2019 (COVID-19) pandemic renewed societal debates on the efficacy and ethics of digital surveillance to mitigate collective crises. While digital emissions tracking is being used to support climate-mitigation strategies, to date there has been limited exploration of the opportunities and challenges of deploying it at the individual level. Here, we assess temporal and regional differences in levels of support for the use of digital surveillance in times of crisis, such as climate change. Results from a global survey indicate moderate support for the use of digital tracking, including for personal carbon footprints. Response varied regionally, with the lowest support in North America and Europe. This study raises key questions-if digital surveillance tools could be part of a socially acceptable response to the climate crisis, is it worth exploring? Or is this an unacceptable risk for society?

New digital public health tools: privacy by design in contact tracing mobile apps for COVID-19

The technology adoption cycle in the public sector is usually much longer than in the private sector. The COVID-19 pandemic caused an acceleration in the adoption of various digital tools which serve as a bridge between the public and private sector. These digital tools include instantaneous contact tracing mobile applications (apps) used to alert individuals who have recently been in contact with an infected person and used by governments to manage public health policies. From the perspective of individuals' data storage there are two general possible approaches, namely centralised and decentralised. Each approach has some legal and ethical considerations, mostly related to finding the right balance between the individual's privacy and public health. In this paper we will outline how privacy according to the design principle should be applied as a minimum standard when developing government approved contact tracking apps. Copyright © 2022 Inderscience Enterprises Ltd.

Design and Preliminary Findings of Adherence to the Self-Testing for Our Protection From COVID-19 (STOP COVID-19) Risk-Based Testing Protocol: Prospective Digital Study.

BACKGROUND: Serial testing for SARS-CoV-2 is recommended to reduce spread of the virus; however, little is known about adherence to recommended testing schedules and reporting practices to health departments. OBJECTIVE: The Self-Testing for Our Protection from COVID-19 (STOP COVID-19) study aims to examine adherence to a risk-based COVID-19 testing strategy using rapid antigen tests and reporting of test results to health departments. METHODS: STOP COVID-19 is a 12-week digital study, facilitated using a smartphone app for testing assistance and reporting. We are recruiting 20,000 participants throughout the United States. Participants are stratified into high- and low-risk groups based on history of COVID-19 infection and vaccination status. High-risk participants are instructed to perform twice-weekly testing for COVID-19 using rapid antigen tests, while low-risk participants test only in the case of symptoms or exposure to COVID-19. All participants complete COVID-19 surveillance surveys, and rapid antigen results are recorded within the smartphone app. Primary outcomes include participant adherence to a risk-based serial testing protocol and percentage of rapid tests reported to health departments. RESULTS: As of February 2022, 3496 participants have enrolled, including 1083 high-risk participants. Out of 13,730 tests completed, participants have reported 13,480 (98.18%, 95% CI 97.9%-98.4%) results to state public health departments with full personal identifying information or anonymously. Among 622 high-risk participants who finished the study period, 35.9% showed high adherence to the study testing protocol. Participants with high adherence reported a higher percentage of test results to the state health department with full identifying information than those in the moderate- or low-adherence groups (high: 71.7%, 95% CI 70.3%-73.1%; moderate: 68.3%, 95% CI 66.0%-70.5%; low: 63.1%, 59.5%-66.6%). CONCLUSIONS: Preliminary results from the STOP COVID-19 study provide important insights into rapid antigen test reporting and usage, and can thus inform the use of rapid testing interventions for COVID-19 surveillance.

Sensing pedestrian flows for real-time assessment of non-pharmaceutical policy interventions during the COVID-19 pandemic.

Introduction: To combat and mitigate the transmission of the SARS-CoV-2 virus, reducing the number of social contacts within a population is highly effective. Non-pharmaceutical policy interventions, e.g. stay-at-home orders, closing schools, universities, and (non-essential) businesses, are expected to decrease pedestrian flows in public areas, leading to reduced social contacts. The extent to which such interventions show the targeted effect is often measured retrospectively by surveying behavioural changes. Approaches that use data generated through mobile phones are hindered by data confidentiality and privacy regulations and complicated by selection effects. Furthermore, access to such sensitive data is limited. However, a complex pandemic situation requires a fast evaluation of the effectiveness of the introduced interventions aiming to reduce social contacts. Location-based sensor systems installed in cities, providing objective measurements of spatial mobility in the form of pedestrian flows, are suited for such a purpose. These devices record changes in a population's behaviour in real-time, do not have privacy problems as they do not identify persons, and have no selection problems due to ownership of a device. Objective: This work aimed to analyse location-based sensor measurements of pedestrian flows in 49 metropolitan areas at 100 locations in Germany to study whether such technology is suitable for the real-time assessment of behavioural changes during a phase of several different pandemic-related policy interventions. Methods: Spatial mobility data of pedestrian flows was linked with policy interventions using the date as a unique linkage key. Data was visualised to observe potential changes in pedestrian flows before or after interventions. Furthermore, differences in time series of pedestrian counts between the pandemic and the pre-pandemic year were analysed. Results: The sensors detected changes in mobility patterns even before policy interventions were enacted. Compared to the pre-pandemic year, pedestrian counts were 85% lower. Conclusions: The study illustrated the practical value of sensor-based real-time measurements when linked with non-pharmaceutical policy intervention data. This study's core contribution is that the sensors detected behavioural changes before enacting or loosening non-pharmaceutical policy interventions. Therefore, such technologies should be considered in the future by policymakers for crisis management and policy evaluation.

Identifying COVID-19 Outbreaks From Contact-Tracing Interview Forms for Public Health Departments: Development of a Natural Language Processing Pipeline.

BACKGROUND: In Wisconsin, COVID-19 case interview forms contain free-text fields that need to be mined to identify potential outbreaks for targeted policy making. We developed an automated pipeline to ingest the free text into a pretrained neural language model to identify businesses and facilities as outbreaks. OBJECTIVE: We aimed to examine the precision and recall of our natural language processing pipeline against existing outbreaks and potentially new clusters. METHODS: Data on cases of COVID-19 were extracted from the Wisconsin Electronic Disease Surveillance System (WEDSS) for Dane County between July 1, 2020, and June 30, 2021. Features from the case interview forms were fed into a Bidirectional Encoder Representations from Transformers (BERT) model that was fine-tuned for named entity recognition (NER). We also developed a novel location-mapping tool to provide addresses for relevant NER. Precision and recall were measured against manually verified outbreaks and valid addresses in WEDSS. RESULTS: There were 46,798 cases of COVID-19, with 4,183,273 total BERT tokens and 15,051 unique tokens. The recall and precision of the NER tool were 0.67 (95% CI 0.66-0.68) and 0.55 (95% CI 0.54-0.57), respectively. For the location-mapping tool, the recall and precision were 0.93 (95% CI 0.92-0.95) and 0.93 (95% CI 0.92-0.95), respectively. Across monthly intervals, the NER tool identified more potential clusters than were verified in WEDSS. CONCLUSIONS: We developed a novel pipeline of tools that identified existing outbreaks and novel clusters with associated addresses. Our pipeline ingests data from a statewide database and may be deployed to assist local health departments for targeted interventions.

Early Perceptions of COVID-19 Contact Tracing Apps in German-Speaking Countries: Comparative Mixed Methods Study.

BACKGROUND: The main German-speaking countries (Germany, Austria, and Switzerland) have implemented digital contact tracing apps to assist the authorities with COVID-19 containment strategies. Low user rates for these apps can affect contact tracing and, thus, its usefulness in controlling the spread of the novel coronavirus. OBJECTIVE: This study aimed to assess the early perceptions of people living in the German-speaking countries and compare them with the frames portrayed in the newspapers during the first wave of the COVID-19 pandemic. METHODS: We conducted qualitative interviews with 159 participants of the SolPan project. Of those, 110 participants discussed contact tracing apps and were included in this study. We analyzed articles regarding contact tracing apps from 12 newspapers in the German-speaking countries. RESULTS: Study participants perceived and newspaper coverage in all German-speaking countries framed contact tracing apps as governmental surveillance tools and embedded them in a broader context of technological surveillance. Participants identified trust in authorities, respect of individual privacy, voluntariness, and temporary use of contact tracing apps as prerequisites for democratic compatibility. Newspapers commonly referenced the use of such apps in Asian countries, emphasizing the differences in privacy regulation among these countries. CONCLUSIONS: The uptake of digital contact tracing apps in German-speaking countries may be undermined due to privacy risks that are not compensated by potential benefits and are rooted in a deeper skepticism towards digital tools. When authorities plan to implement new digital tools and practices in the future, they should be very transparent and proactive in communicating their objectives and the role of the technology-and how it differs from other, possibly similar, tools. It is also important to publicly address ethical, legal, and social issues related to such technologies prior to their launch.