Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators-derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity-from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in "flat" or "down" directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during "up" trends.

An open repository of real-time COVID-19 indicators.

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

Effectiveness of Public Health Digital Surveillance Systems for Infectious Disease Prevention and Control at Mass Gatherings: Systematic Review.

BACKGROUND: Mass gatherings (MGs; eg, religious, sporting, musical, sociocultural, and other occasions that draw large crowds) pose public health challenges and concerns related to global health. A leading global concern regarding MGs is the possible importation and exportation of infectious diseases as they spread from the attendees to the general population, resulting in epidemic outbreaks. Governments and health authorities use technological interventions to support public health surveillance and prevent and control infectious diseases. OBJECTIVE: This study aims to review the evidence on the effectiveness of public health digital surveillance systems for infectious disease prevention and control at MG events. METHODS: A systematic literature search was conducted in January 2022 using the Ovid MEDLINE, Embase, CINAHL, and Scopus databases to examine relevant articles published in English up to January 2022. Interventional studies describing or evaluating the effectiveness of public health digital surveillance systems for infectious disease prevention and control at MGs were included in the analysis. Owing to the lack of appraisal tools for interventional studies describing and evaluating public health digital surveillance systems at MGs, a critical appraisal tool was developed and used to assess the quality of the included studies. RESULTS: In total, 8 articles were included in the review, and 3 types of MGs were identified: religious (the Hajj and Prayagraj Kumbh), sporting (the Olympic and Paralympic Games, the Federation International Football Association World Cup, and the Micronesian Games), and cultural (the Festival of Pacific Arts) events. In total, 88% (7/8) of the studies described surveillance systems implemented at MG events, and 12% (1/8) of the studies described and evaluated an enhanced surveillance system that was implemented for an event. In total, 4 studies reported the implementation of a surveillance system: 2 (50%) described the enhancement of the system that was implemented for an event, 1 (25%) reported a pilot implementation of a surveillance system, and 1 (25%) reported an evaluation of an enhanced system. The types of systems investigated were 2 syndromic, 1 participatory, 1 syndromic and event-based, 1 indicator- and event-based, and 1 event-based surveillance system. In total, 62% (5/8) of the studies reported timeliness as an outcome generated after implementing or enhancing the system without measuring its effectiveness. Only 12% (1/8) of the studies followed the Centers for Disease Control and Prevention guidelines for evaluating public health surveillance systems and the outcomes of enhanced systems based on the systems' attributes to measure their effectiveness. CONCLUSIONS: On the basis of the review of the literature and the analysis of the included studies, there is limited evidence of the effectiveness of public health digital surveillance systems for infectious disease prevention and control at MGs because of the absence of evaluation studies.

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.

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.

A Digital Mask-Voiceprint System for Postpandemic Surveillance and Tracing Based on the STRONG Strategy.

Lockdowns and border closures due to COVID-19 imposed mental, social, and financial hardships in many societies. Living with the virus and resuming normal life are increasingly being advocated due to decreasing virus severity and widespread vaccine coverage. However, current trends indicate a continued absence of effective contingency plans to stop the next more virulent variant of the pandemic. The COVID-19-related mask waste crisis has also caused serious environmental problems and virus spreads. It is timely and important to consider how to precisely implement surveillance for the dynamic clearance of COVID-19 and how to efficiently manage discarded masks to minimize disease transmission and environmental hazards. In this viewpoint, we sought to address this issue by proposing an appropriate strategy for intelligent surveillance of infected cases and centralized management of mask waste. Such an intelligent strategy against COVID-19, consisting of wearable mask sample collectors (masklect) and voiceprints and based on the STRONG (Spatiotemporal Reporting Over Network and GPS) strategy, could enable the resumption of social activities and economic recovery and ensure a safe public health environment sustainably.

Describing Memes Referencing Vaping: Thematic Analysis.

Background: Memes, images or videos with text overlay that embody a concept or belief about the contemporary society, are endemic to Internet culture, are popular among youth and diffuse rapidly across social media platforms. E-cigarettes and vaping have grown in popularity in the era of Internet culture however there is little research describing the intersection of memes and vaping. This is an important gap in the literature as memes may be part of the broader online e-cigarette information landscape that can normalize vaping among young people. Memes could also point to emerging trends in product preferences. This study content analyzed memes to identify key themes, characters and vape products depicted therein. Methods: Data were drawn from a sub-reddit devoted to vaping-related memes. Memes were electronically copied from the forum to analyze (n = 527). Using an inductive approach, the research team identified 14 themes. Results: In-group communication (n = 202, 38.33%) was the most predominant theme followed by Critique of vaping regulations and public perceptions (n = 76, 14.42%), and Vape device modifications and hacks (n = 62, 11.76%). Memes included Cartoons (n = 124, 23.53%), Celebrities (n = 75, 14.23%), and Fictional characters (n = 53, 10.06%). Memes referenced Tanks or mods (n = 120, 22.77%), Component parts (n = 96, 18.22%) and E-liquids/Nicotine salts (n = 81, 15.37%). Conclusion: Memes referenced in-group communication and cartoons among other youth friendly images, raising concern about the potential to normalize vaping-related behaviors. Future research should monitor emerging vape devices and determine the impact of memes on attitudes and behaviors among adolescents and young adults. Implications: Given the popularity and reach of memes among youth, continuous monitoring of vaping-related memes may reveal aspects that may be addressed in vaping prevention campaigns.

Digital surveillance in Latin American diseases outbreaks: information extraction from a novel Spanish corpus.

BACKGROUND: In order to detect threats to public health and to be well-prepared for endemic and pandemic illness outbreaks, countries usually rely on event-based surveillance (EBS) and indicator-based surveillance systems. Event-based surveillance systems are key components of early warning systems and focus on fast capturing of data to detect threat signals through channels other than traditional surveillance. In this study, we develop Natural Language Processing tools that can be used within EBS systems. In particular, we focus on information extraction techniques that enable digital surveillance to monitor Internet data and social media. RESULTS: We created an annotated Spanish corpus from ProMED-mail health reports regarding disease outbreaks in Latin America. The corpus has been used to train algorithms for two information extraction tasks: named entity recognition and relation extraction. The algorithms, based on deep learning and rules, have been applied to recognize diseases, hosts, and geographical locations where a disease is occurring, among other entities and relations. In addition, an in-depth analysis of micro-average F1 metrics shows the suitability of our approaches for both tasks. CONCLUSIONS: The annotated corpus and algorithms presented could leverage the development of automated tools for extracting information from news and health reports written in Spanish. Moreover, this framework could be useful within EBS systems to support the early detection of Latin American disease outbreaks.

Engagement With HIV and COVID-19 Prevention: Nationwide Cross-sectional Analysis of Users on a Geosocial Networking App.

BACKGROUND: Geosocial networking (GSN) apps play a pivotal role in catalyzing sexual partnering, especially among men who have sex with men. OBJECTIVE: To quantify the prevalence and disparities in disclosure of pre-exposure prophylaxis (PrEP) use and COVID-19 vaccination among GSN app users, mostly men who have sex with men, in the United States. METHODS: Web-based Grindr profiles from the top 50 metropolitan areas as well as the 50 most rural counties in the United States by population were randomly sampled. Grindr provides an option to disclose current PrEP use (HIV positive, HIV negative, or HIV negative with PrEP use). The free text in all profiles was analyzed, and any mention of COVID-19 vaccination was recorded. Multivariable logistic regression to assess independent associations with PrEP disclosure and COVID-19 vaccination was performed. Imputation analyses were used to test the robustness of the results. RESULTS: We evaluated 1889 urban and 384 rural profiles. Mean age among urban profiles was 32.9 (SD 9.6) years; mean age among rural profiles was 33.5 (SD 12.1) years (P=.41). Among the urban profiles, 16% reported being vaccinated against COVID-19 and 23% reported PrEP use compared to 10% and 8% in rural profiles, respectively (P=.002 and P<.001, respectively). Reporting COVID-19 vaccination (adjusted odds ratio [aOR] 1.7, 95% CI 1.2-2.4), living in an urban center (aOR 3.2, 95% CI 1.8-5.7), and showing a face picture as part of the Grindr profile (aOR 4.0, 95% CI 2.3-7.0) were positively associated with PrEP disclosure. Self-identified Black and Latino users were less likely to report PrEP use (aOR 0.6, 95% CI 0.4-0.9 and aOR 0.5, 95% CI 0.4-0.9, respectively). Reporting PrEP use (aOR 1.7, 95% CI 1.2-2.4), living in an urban center (aOR 2.5, 95% CI 1.4-4.5), having a "discreet" status (aOR 1.6, 95% CI 1.0-2.5), and showing a face picture (aOR 2.7, 95% CI 1.5-4.8) were positively associated with reporting COVID-19 vaccination on their profile. Users in the southern United States were less likely to report COVID-19 vaccination status than those in the northeast United States (aOR 0.6, 95% CI 0.3-0.9). CONCLUSIONS: Variations in PrEP disclosure are associated with race, whereas COVID-19 vaccination disclosure is associated with geographic area. However, rural GSN users were less likely to report both PrEP use and COVID-19 vaccination. The data demonstrate a need to expand health preventative services in the rural United States for sexual minorities. GSN platforms may be ideal for deployment of preventative interventions to improve access for this difficult-to-reach population.

Digital Surveillance Through an Online Decision Support Tool for COVID-19 Over One Year of the Pandemic in Italy: Observational Study.

BACKGROUND: Italy has experienced severe consequences (ie, hospitalizations and deaths) during the COVID-19 pandemic. Online decision support systems (DSS) and self-triage applications have been used in several settings to supplement health authority recommendations to prevent and manage COVID-19. A digital Italian health tech startup, Paginemediche, developed a noncommercial, online DSS with a chat user interface to assist individuals in Italy manage their potential exposure to COVID-19 and interpret their symptoms since early in the pandemic. OBJECTIVE: This study aimed to compare the trend in online DSS sessions with that of COVID-19 cases reported by the national health surveillance system in Italy, from February 2020 to March 2021. METHODS: We compared the number of sessions by users with a COVID-19-positive contact and users with COVID-19-compatible symptoms with the number of cases reported by the national surveillance system. To calculate the distance between the time series, we used the dynamic time warping algorithm. We applied Symbolic Aggregate approXimation (SAX) encoding to the time series in 1-week periods. We calculated the Hamming distance between the SAX strings. We shifted time series of online DSS sessions 1 week ahead. We measured the improvement in Hamming distance to verify the hypothesis that online DSS sessions anticipate the trends in cases reported to the official surveillance system. RESULTS: We analyzed 75,557 sessions in the online DSS; 65,207 were sessions by symptomatic users, while 19,062 were by contacts of individuals with COVID-19. The highest number of online DSS sessions was recorded early in the pandemic. Second and third peaks were observed in October 2020 and March 2021, respectively, preceding the surge in notified COVID-19 cases by approximately 1 week. The distance between sessions by users with COVID-19 contacts and reported cases calculated by dynamic time warping was 61.23; the distance between sessions by symptomatic users was 93.72. The time series of users with a COVID-19 contact was more consistent with the trend in confirmed cases. With the 1-week shift, the Hamming distance between the time series of sessions by users with a COVID-19 contact and reported cases improved from 0.49 to 0.46. We repeated the analysis, restricting the time window to between July 2020 and December 2020. The corresponding Hamming distance was 0.16 before and improved to 0.08 after the time shift. CONCLUSIONS: Temporal trends in the number of online COVID-19 DSS sessions may precede the trend in reported COVID-19 cases through traditional surveillance. The trends in sessions by users with a contact with COVID-19 may better predict reported cases of COVID-19 than sessions by symptomatic users. Data from online DSS may represent a useful supplement to traditional surveillance and support the identification of early warning signals in the COVID-19 pandemic.

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.

Automated digital reporting of clinical laboratory information to national public health surveillance systems, results of a EU/EEA survey, 2018.

BackgroundTimely reporting of microbiology test results is essential for infection management. Automated, machine-to-machine (M2M) reporting of diagnostic and antimicrobial resistance (AMR) data from laboratory information management systems (LIMS) to public health agencies improves timeliness and completeness of communicable disease surveillance.AimWe surveyed microbiology data reporting practices for national surveillance of EU-notifiable diseases in European Union/European Economic Area (EU/EEA) countries in 2018.MethodsEuropean Centre for Disease Prevention and Control (ECDC) National Microbiology and Surveillance Focal Points completed a questionnaire on the modalities and scope of clinical microbiology laboratory data reporting.ResultsComplete data were provided for all 30 EU/EEA countries. Clinical laboratories used a LIMS in 28 countries. LIMS data on EU-notifiable diseases and AMR were M2M-reported to the national level in 14 and nine countries, respectively. In the 14 countries, associated demographic data reported allowed the de-duplication of patient reports. In 13 countries, M2M-reported data were used for cluster detection at the national level. M2M laboratory data reporting had been validated against conventional surveillance methods in six countries, and replaced those in five. Barriers to M2M reporting included lack of information technology support and financial incentives.ConclusionM2M-reported laboratory data were used for national public health surveillance and alert purposes in nearly half of the EU/EEA countries in 2018. Reported data on infectious diseases and AMR varied in extent and disease coverage across countries and laboratories. Improving automated laboratory-based surveillance will depend on financial and regulatory incentives, and harmonisation of health information and communication systems.

Epihack Sri Lanka: development of a mobile surveillance tool for dengue fever.

BACKGROUND: Dengue is a serious problem around the globe, with 3.9 billion people at risk of the disease. Sri Lanka has recently seen unprecedented rates of dengue with 4.3 times more cases than during the same period over the previous six years. The paper discusses the development of an integrated health systems framework, aided by mobile technology, to combat and contain dengue via a health hackathon in Sri Lanka. RESULTS: The framework addresses the key functions of surveillance, health communication and civic engagement through innovations including digitisation of hospital forms; digital aid to Public Health Inspectors (PHIs); data consolidation and analytics; education for construction workers, GPs, and schools; and educating the general public. CONCLUSIONS: We present the impact of the disease burden in tropical countries, such as Sri Lanka, current technological solutions, and the process of developing the mobile application modules developed via the health hackathon.

A Platform for Monitoring Regional Antimicrobial Resistance, Using Online Data Sources: ResistanceOpen.

Background: Our understanding of the global burden of antimicrobial resistance is limited. Complementary approaches to antimicrobial resistance surveillance are needed. Methods: We developed a Web-based/mobile platform for aggregating, analyzing, and disseminating regional antimicrobial resistance information. Antimicrobial resistance indices from existing but disparate online sources were identified and abstracted. To validate antimicrobial resistance data, in the absence of regional comparators, US and Canadian indices were aggregated and compared to existing national and state estimates. Measures of variability of antimicrobial susceptibility were determined for the United States and Canada to evaluate magnitudes of differences within countries. Results: Over 850 resistance indices globally were identified and abstracted, totaling >5 million isolates, from 340 unique locations. Resistance index coverage spanned 41 countries, 6 continents, 43 of 50 US states, and 8 of 10 Canadian provinces. When compared to reported values, aggregated resistance values for the United States and Canada during 2013 and 2014 demonstrated agreements ranging from 94% to 97%. For the United States, state-specific resistance estimates demonstrated an agreement of 92%. Large differences in antimicrobial susceptibility were seen within countries. Conclusions: Using existing nontraditional data sources, we have developed a Web-based platform for aggregating antimicrobial resistance indices to support monitoring of regional antimicrobial resistance patterns.

Representation of marginalised populations in digital surveillance for notifiable conditions in Australia: a systematic review.

AIM: This study aims to establish whether digital surveillance methods for notifiable diseases in Australia collect and report data in relation to marginalised populations. METHODS: The literature was systematically reviewed to identify primary research studies published between January 2005 and July 2023. Studies were included if they described an Australian digital surveillance system for notifiable conditions. The results were synthesised with a focus on evaluating the collection and reporting of data in relation to marginalised populations. RESULTS: A total of 13 articles reporting on seven surveillance systems were identified. Influenza and adverse events following immunisation were the two most common notifiable conditions monitored. A total of six surveillance systems encompassing 16 articles reported information on sub-populations. Of these, three surveillance systems (nine articles) included data on marginalised populations. CONCLUSION: The data collected or reported in relation to sub-groups that characterise diversity in terms of health care needs, access, and marginalised populations are minimal. It is recommended that a set of equity and reporting principles is established for the future creation and use of any digital surveillance system.

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.

The HOTspots Digital Surveillance: Conceptualisation to Clinical Deployment.

The HOTspots digital surveillance platform (HOTspots) is a critical technology of the HOTspots Surveillance and Response Program. It provides timely point-of-care access to pathology and demographic data from previously underserved regions. Co-designed with clinicians, epidemiologists, and health policy makers, the platform provides the evidence-base to empower efficient clinical management of patients with antimicrobial resistant (AMR) infections and supports national disease surveillance efforts in Australia. The pathway from conceptualisation to deployment for the HOTspots digital surveillance platform is described.

Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review.

BACKGROUND: Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts. OBJECTIVE: This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods. METHODS: A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers. RESULTS: Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses. CONCLUSIONS: The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.

Participatory Disease Surveillance for the Early Detection of Cholera-Like Diarrheal Disease Outbreaks in Rural Villages in Malawi: Prospective Cohort Study.

BACKGROUND: Cholera-like diarrheal disease (CLDD) outbreaks are complex and influenced by environmental factors, socioeconomic conditions, and population dynamics, leading to limitations in traditional surveillance methods. In Malawi, cholera is considered an endemic disease. Its epidemiological profile is characterized by seasonal patterns, often coinciding with the rainy season when contamination of water sources is more likely. However, the outbreak that began in March 2022 has extended to the dry season, with deaths reported in all 29 districts. It is considered the worst outbreak in the past 10 years. OBJECTIVE: This study aims to evaluate the feasibility and outcomes of participatory surveillance (PS) using interactive voice response (IVR) technology for the early detection of CLDD outbreaks in Malawi. METHODS: This longitudinal cohort study followed 740 households in rural settings in Malawi for 24 weeks. The survey tool was designed to have 10 symptom questions collected every week. The proxies' rationale was related to exanthematic, ictero-hemorragica for endemic diseases or events, diarrhea and respiratory/targeting acute diseases or events, and diarrhea and respiratory/targeting seasonal diseases or events. This work will focus only on the CLDD as a proxy for gastroenteritis and cholera. In this study, CLDD was defined as cases where reports indicated diarrhea combined with either fever or vomiting/nausea. RESULTS: During the study period, our data comprised 16,280 observations, with an average weekly participation rate of 35%. Maganga TA had the highest average of completed calls, at 144.83 (SD 10.587), while Ndindi TA had an average of 123.66 (SD 13.176) completed calls. Our findings demonstrate that this method might be effective in identifying CLDD with a notable and consistent signal captured over time (R2=0.681404). Participation rates were slightly higher at the beginning of the study and decreased over time, thanks to the sensitization activities rolled out at the CBCCs level. In terms of the attack rates for CLDD, we observed similar rates between Maganga TA and Ndindi TA, at 16% and 15%, respectively. CONCLUSIONS: PS has proven to be valuable for the early detection of epidemics. IVR technology is a promising approach for disease surveillance in rural villages in Africa, where access to health care and traditional disease surveillance methods may be limited. This study highlights the feasibility and potential of IVR technology for the timely and comprehensive reporting of disease incidence, symptoms, and behaviors in resource-limited settings.