Developing and Evaluating Digital Public Health Interventions Using the Digital Public Health Framework DigiPHrame: A Framework Development Study.

BACKGROUND: Digital public health (DiPH) interventions may help us tackle substantial public health challenges and reach historically underserved populations, in addition to presenting valuable opportunities to improve and complement existing services. However, DiPH interventions are often triggered through technological advancements and opportunities rather than public health needs. To develop and evaluate interventions designed to serve public health needs, a comprehensive framework is needed that systematically covers all aspects with relevance for public health. This includes considering the complexity of the technology, the context in which the technology is supposed to operate, its implementation, and its effects on public health, including ethical, legal, and social aspects. OBJECTIVE: We aimed to develop such a DiPH framework with a comprehensive list of core principles to be considered throughout the development and evaluation process of any DiPH intervention. METHODS: The resulting digital public health framework (DigiPHrame) was based on a scoping review of existing digital health and public health frameworks. After extracting all assessment criteria from these frameworks, we clustered the criteria. During a series of multidisciplinary meetings with experts from the Leibniz ScienceCampus Digital Public Health, we restructured each domain to represent the complexity of DiPH. In this paper, we used a COVID-19 contact-tracing app as a use case to illustrate how DigiPHrame may be applied to assess DiPH interventions. RESULTS: The current version of DigiPHrame consists of 182 questions nested under 12 domains. Domain 1 describes the current status of health needs and existing interventions; domains 2 and 3, the DiPH technology under assessment and aspects related to human-computer interaction, respectively; domains 4 and 5, structural and process aspects, respectively; and domains 6-12, contextual conditions and the outcomes of the DiPH intervention from broad perspectives. In the CWA use case, a number of questions relevant during its development but also important for assessors once the CWA was available were highlighted. CONCLUSIONS: DigiPHrame is a comprehensive framework for the development and assessment of digital technologies designed for public health purposes. It is a living framework and will, therefore, be updated regularly and as new public health needs and technological advancements emerge.

Meeting Social Needs in a Crisis Context: Lessons Learned from Integrating the 'Take Care Initiative' into New York City's Testing and Contact Tracing Program.

The COVID-19 pandemic highlighted the importance of addressing social needs in a crisis context. Some US jurisdictions integrated a social service component into case investigation and contact tracing (CI/CT) programs, including the New York City (NYC) Test & Trace (T2) Program; the Take Care initiative referred NYC residents who tested positive or were exposed to COVID-19 to services to support isolation and quarantine and meet basic needs. More research is needed to determine effective implementation strategies for integrating social needs provision into CI/CT programs. To identify barriers and facilitators to the implementation of the Take Care initiative, we conducted key informant interviews with program staff, community-based organization partners, and cases and contacts as part of a larger evaluation of the T2 program. Interviews were recorded, transcribed, and analyzed using rapid qualitative methods. Key facilitators to implementation included utilizing a case management software system, employing strategies to encourage service uptake, leveraging cross-agency collaborations, and partnering with community-based organizations for resource navigation. Barriers identified included external management of the software system, challenges reaching and engaging the public, administrative complications due to shifting collaborations, and management of CBO partners' structure and hiring. Based on our findings, we provide recommendations to support effective planning and implementation of social needs service provision in a crisis context. Future research should focus on testing promising implementation strategies highlighted in this study and applying them to varied contexts and crisis situations.

Use of a five-category partner-type classification within a chlamydia and gonorrhoea service evaluation highlights opportunities for targeted partner notification to improve STI control.

OBJECTIVES: Partner notification (PN) is a key component of sexually transmitted infection control. British Association for Sexual Health and HIV guidelines now recommend partner-centred PN outcomes using a five-category partner classification (established, new, occasional, one-off, sex worker). We evaluated the reporting of partner-centred PN outcomes in two contrasting UK sexual health services. METHODS: Using the electronic patient records of 40 patients with a positive gonorrhoea test and 180 patients with a positive chlamydia test, we extracted PN outcomes for the five most recent sexual contacts within the appropriate lookback period. RESULTS: 180 patients with chlamydia reported 262 partners: 220 were contactable (103 established, 9 new, 43 occasional, 52 one-off, 13 unknown/unrecorded). 40 patients with gonorrhoea reported 88 partners: 53 were contactable (7 established, 1 new, 14 occasional, 10 one-off and 21 unknown/not recorded). No sex worker partners were reported. Most established partners of people with chlamydia (96/103) or gonorrhoea (7/7) were notified but fewer (60/103 and 6/7, respectively) attended for testing. Of those, 39 had a positive chlamydia test and two had a positive gonorrhoea test. For both chlamydia and gonorrhoea, most occasional and new partners were reported to be notified but there was a sharper decline in those tested. For both infections, one-off partners had the lowest rates of accessing services and testing. For chlamydia, 81% were notified (42/52), 23% accessed services (12/52) and 21% tested (11/52). However, 91% of those tested were positive (10/11). The number of contactable one-off gonorrhoea contacts was small and few attended. CONCLUSIONS: Measuring partner-centred PN outcomes was feasible. There were differences in partner engagement with PN between the different infections and partner types. If these findings are replicated in larger samples, it suggests that interventions to target one-off partners who have low rates of PN engagement yet high levels of positivity could play a key role in reducing infection at population level.

Using mHealth Technologies for Case Finding in Tuberculosis and Other Infectious Diseases in Africa: Systematic Review.

BACKGROUND: Mobile health (mHealth) technologies are increasingly used in contact tracing and case finding, enhancing and replacing traditional methods for managing infectious diseases such as Ebola, tuberculosis, COVID-19, and HIV. However, the variations in their development approaches, implementation scopes, and effectiveness introduce uncertainty regarding their potential to improve public health outcomes. OBJECTIVE: We conducted this systematic review to explore how mHealth technologies are developed, implemented, and evaluated. We aimed to deepen our understanding of mHealth's role in contact tracing, enhancing both the implementation and overall health outcomes. METHODS: We searched and reviewed studies conducted in Africa focusing on tuberculosis, Ebola, HIV, and COVID-19 and published between 1990 and 2023 using the PubMed, Scopus, Web of Science, and Google Scholar databases. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to review, synthesize, and report the findings from articles that met our criteria. RESULTS: We identified 11,943 articles, but only 19 (0.16%) met our criteria, revealing a large gap in technologies specifically aimed at case finding and contact tracing of infectious diseases. These technologies addressed a broad spectrum of diseases, with a predominant focus on Ebola and tuberculosis. The type of technologies used ranged from mobile data collection platforms and smartphone apps to advanced geographic information systems (GISs) and bidirectional communication systems. Technologies deployed in programmatic settings, often developed using design thinking frameworks, were backed by significant funding and often deployed at a large scale but frequently lacked rigorous evaluations. In contrast, technologies used in research settings, although providing more detailed evaluation of both technical performance and health outcomes, were constrained by scale and insufficient funding. These challenges not only prevented these technologies from being tested on a wider scale but also hindered their ability to provide actionable and generalizable insights that could inform public health policies effectively. CONCLUSIONS: Overall, this review underscored a need for organized development approaches and comprehensive evaluations. A significant gap exists between the expansive deployment of mHealth technologies in programmatic settings, which are typically well funded and rigorously developed, and the more robust evaluations necessary to ascertain their effectiveness. Future research should consider integrating the robust evaluations often found in research settings with the scale and developmental rigor of programmatic implementations. By embedding advanced research methodologies within programmatic frameworks at the design thinking stage, mHealth technologies can potentially become technically viable and effectively meet specific contact tracing health outcomes to inform policy effectively.

The role of Contact-Tracing Mobile Apps in pandemic prevention: A multidisciplinary perspective on health beliefs, social, and technological factors.

During the recent COVID-19 pandemic, governments implemented mobile applications for contact tracing as a rapid and effective solution to mitigate the spread of the virus. However, these seemingly straightforward solutions did not achieve their intended objectives. In line with previous research, this paper aims to investigate the factors that influence the acceptance and usage of contact-tracing mobile apps (CTMAs) in the context of disease control. The research model in this paper integrates the Unified Theory of Acceptance and Use of Technology and the Health Belief Model (HBM). The present study involved a diverse sample of 770 French participants of all genders, ages, occupations, and regions. Critical elements from the Health Belief Model, technological factors related to the app, and social factors, including the centrality of religiosity, were assessed using well-established measurement scales. The research's findings demonstrate that several factors, such as perceived benefits and perceived severity, social influence, health motivation, and centrality of religiosity, significantly impact the intention to use a CTMA. These findings suggest that CTMAs hold promise as valuable tools for managing future epidemics. However, addressing challenges, revising implementation strategies, and potentially collaborating with specialized industry partners under regulatory frameworks are crucial. This practical insight can guide policymakers and public health officials in their decision-making.

Improving COVID-19 contact tracing and testing of exposed individuals in Cameroon using digital health technology: a cluster randomised trial.

Background: Contact tracing was described as a key strategy to contribute to controlling the spread of severe acute respiratory syndrome of Coronavirus 2 (SARS-CoV-2) but implementing it can be a challenge. Digitalisation of contact tracing is among the proposed solutions being explored in sub-Saharan African settings. We assessed the effectiveness of a digital tool to expand SARS-CoV-2 testing in exposed individuals in Cameroon. Methods: We conducted a cluster-randomised (1:1) trial in eight health districts, including 22 facilities and SARS-CoV-2 testing units, randomly assigned to a digital (intervention) or standard (control) contact tracing approach. The intervention consisted of a contact tracing module added to the digital platform "Mamal PRO" used for monitoring and coordination of Coronavirus Disease 2019 pandemic response in Cameroon. The primary outcome was the proportion of contacts declared by SAR-CoV-2 index patients who were successfully traced and tested for SARS-CoV-2 evaluated with a Poisson regression model with cluster adjustment. This study is registered with ClinicalTrials.gov (NCT05684887). Findings: Between October 18, 2022, and March 31, 2023, we enrolled 164 index patients in the intervention arm and 149 in the control arm, who identified 854 and 849 contacts, respectively. In the intervention arm, 93.8% (801/854) of identified contacts were successfully reached by the tracing unit versus 54.5% (463/849) in the control arm. The intervention significantly increased the likelihood of successfully tracing contacts (adjusted relative risks (RR) 1.72 [95% CI: 1.00-2.95], p = 0.049). The median (interquartile range, IQR) time to successfully tracing contacts was 0 days [IQR: 0, 1] in the intervention and 1 day [IQR: 0, 2] in the control arm. In the intervention arm, 21.3% (182/854) of identified contacts received SARS-CoV-2 testing compared to 14.5% (123/849) in the control arm (adjusted RR 1.47 [95% CI: 0.44-4.90], p = 0.530). Interpretation: Digitalising the contact tracing process improved exposure notification and facilitated the tracing of a greater number of contacts of individuals infected with SARS-CoV-2 in resource-limited settings. Funding: The study was funded by FIND, United Kingdom (FCDO 40105983), Switzerland (81066910), Netherlands (SDD 4000004160), Canada (DFATD 7429348), The Kingdom of Saudi Arabia (FIND-ACT-A DX PARTNERSHIP 20.08.2020), The Rockefeller Foundation (2020 HTH 059), Germany (BMZ Covid-19 Diagnostic and Surveillance Response 27.07.2021), Australia (DFAT 76442), Kuwait (M239/2020), The Government of Portugal and Partners (ANF, BCP, CGF, APIFARMA) and The BlackRock Foundation (Grant Agreement as of April 20, 2022).

Stochastic EM algorithm for partially observed stochastic epidemics with individual heterogeneity.

We develop a stochastic epidemic model progressing over dynamic networks, where infection rates are heterogeneous and may vary with individual-level covariates. The joint dynamics are modeled as a continuous-time Markov chain such that disease transmission is constrained by the contact network structure, and network evolution is in turn influenced by individual disease statuses. To accommodate partial epidemic observations commonly seen in real-world data, we propose a stochastic EM algorithm for inference, introducing key innovations that include efficient conditional samplers for imputing missing infection and recovery times which respect the dynamic contact network. Experiments on both synthetic and real datasets demonstrate that our inference method can accurately and efficiently recover model parameters and provide valuable insight at the presence of unobserved disease episodes in epidemic data.

Construction of effective reproduction number of infectious disease individuals based on spatiotemporal discriminant search model: take hand-foot-mouth disease as an example.

OBJECTIVE: In order to facilitate the tracing of infectious diseases in a small area and to effectively carry out disease control and epidemiological investigations, this research proposes a novel spatiotemporal model to estimate effective reproduction number(Re)for infectious diseases, based on the fundamental concept of contact tracing. METHODS: This study utilizes the incidence of hand, foot, and mouth disease (HFMD) among children in Bishan District, Chongqing, China from 2015 to 2019. The study incorporates the epidemiological characteristics of HFMD and aims to construct a Spatiotemporal Correlation Discrimination of HFMD. Utilizing ARC ENGINE and C# programming for the creation of a spatio-temporal database dedicated to HFMD to facilitate data collection and analysis. The scientific validity of the proposed method was verified by comparing the effective reproduction number obtained by the traditional SEIR model. RESULTS: We have ascertained the optimal search radius for the spatiotemporal search model to be 1.5 km. Upon analyzing the resulting Re values, which range from 1.14 to 4.75, we observe a skewed distribution pattern from 2015 to 2019. The median and quartile Re value recorded is 2.42 (1.98, 2.72). Except for 2018, the similarity coefficient r of the years 2015, 2016, 2017, and 2019 were all close to 1, and p <0.05 in the comparison of the two models, indicating that the Re values obtained by using the search model and the traditional SEIR model are correlated and closely related. The results exhibited similarity between the Re curves of both models and the epidemiological characteristics of HFMD. Finally, we illustrated the regional distribution of Re values obtained by the search model at various time intervals on Geographic Information System (GIS) maps which highlighted variations in the incidence of diseases across different communities, neighborhoods, and even smaller areas. CONCLUSION: The model comprehensively considers both temporal variation and spatial heterogeneity in disease transmission and accounts for each individual's distinct time of onset and spatial location. This proposed method differs significantly from existing mathematical models used for estimating Re in that it is founded on reasonable scientific assumptions and computer algorithms programming that take into account real-world spatiotemporal factors. It is particularly well-suited for estimating the Re of infectious diseases in relatively stable mobile populations within small geographical areas.

Evaluation of the New York City COVID-19 case investigation and contact tracing program: a cascade of care analysis.

BACKGROUND: New York City (NYC) was the first COVID-19 epicenter in the United States and home to one of the country's largest contact tracing programs, NYC Test & Trace (T2). Understanding points of attrition along the stages of program implementation and follow-up can inform contact tracing efforts for future epidemics or pandemics. The objective of this study was to evaluate the completeness and timeliness of T2 case and contact notification and monitoring using a "cascade of care" approach. METHODS: This cross-sectional study included all SARS-CoV-2 cases and contacts reported to T2 from May 31, 2020 to January 1, 2022. Attrition along the "cascade of care" was defined as: (1) attempted, (2) reached, (3) completed intake (main outcome), (4) eligible for monitoring, and (5) successfully monitored. Timeliness was assessed: (1) by median days from a case's date of testing until their positive result was reported to T2, (2) from result until the case was notified by T2, and (3) from a case report of a contact until notification of the contact. RESULTS: A total of 1.45 million cases and 1.38 million contacts were reported to T2 during this period. For cases, attrition occurred evenly across the first three cascade steps (~-12%) and did not change substantially until the Omicron wave in December 2021. During the Omicron wave, the proportion of cases attempted dropped precipitously. For contacts, the largest attrition occurred between attempting and reaching (-27%), and attrition rose with each COVID-19 wave as contact volumes increased. Attempts to reach contacts discontinued entirely during the Omicron wave. Overall, 67% of cases and 49% of contacts completed intake interviews (79% and 57% prior to Omicron). T2 was timely, with a median of 1 day to receive lab results, 2 days to notify cases, and < 1 day to notify contacts. CONCLUSIONS: T2 provided a large volume of NYC residents with timely notification and monitoring. Engagement in the program was lower for contacts than cases, with the largest gap coming from inability to reach individuals during call attempts. To strengthen future test-and-trace efforts, strategies are needed to encourage acceptance of local contact tracer outreach attempts.

Rate of nosocomial MRSA transmission evaluated via contact screening.

BACKGROUND: The prevention of methicillin-resistant S. aureus (MRSA) transmission in the healthcare setting is a priority in Infection Control practices. A cornerstone of this policy is contact tracing of nosocomial contacts after an unexpected MRSA finding. The objective of this retrospective study was to quantify the rates of MRSA transmission in different clinical settings. METHODS: This multi-centre study included MRSA contact screening results from two regional hospitals and one academic hospital. MRSA contact tracing investigations from 2000 until 2019 were reviewed and post-contact screening results were included of index patients with an MRSA-positive culture and their unprotected contacts. Available typing results were used to rule out incidental findings. RESULTS: Of 27,377 contacts screened after MRSA exposure, 21,488 were Health Care Workers (HCW) and 4816 patients. Post-contact screening was initiated for a total of 774 index cases, the average number of screened contacts per index case was 35.7 (range 1 to 640). MRSA transmission was observed in 0.15% (41) of the contacts, 19 (0.09%) HCW and 22 (0.46%) patients. The number needed to screen to detect one MRSA transmission was 667. The highest risk of MRSA transmission occurred during patient-to-patient contacts, with transmission rates varying from 0.32 to 1.32% among the participating hospitals. No transmissions were detected in HCW (n=2834) in the outpatient setting, and the rate of transmissions among HCW contacts on the wards was 0.13% (19 of 15,874). Among 344 contacts of patients with contact precautions, no transmissions were detected. CONCLUSIONS: Reconsidering current MRSA contact tracing practices may lead to a more targeted approach with a lower number needed to screen.

The public health impact of COVID-19 variants of concern on the effectiveness of contact tracing in Vermont, United States.

Case investigation and contact tracing (CICT) are public health measures that aim to break the chain of pathogen transmission. Changes in viral characteristics of COVID-19 variants have likely affected the effectiveness of CICT programs. We estimated and compared the cases averted in Vermont when the original COVID-19 strain circulated (Nov. 25, 2020-Jan. 19, 2021) with two periods when the Delta strain dominated (Aug. 1-Sept. 25, 2021, and Sept. 26-Nov. 20, 2021). When the original strain circulated, we estimated that CICT prevented 7180 cases (55% reduction in disease burden), compared to 1437 (15% reduction) and 9970 cases (40% reduction) when the Delta strain circulated. Despite the Delta variant being more infectious and having a shorter latency period, CICT remained an effective tool to slow spread of COVID-19; while these viral characteristics did diminish CICT effectiveness, non-viral characteristics had a much greater impact on CICT effectiveness.

Lessons Learned from the Launch and Implementation of the COVID-19 Contact Tracing Program in New York City: a Qualitative Study.

On June 1, 2020, NYC Health + Hospitals, in partnership with the NYC Department of Health and Mental Hygiene, other city agencies, and a large network of community partners, launched the New York City Test & Trace (T2) COVID-19 response program to identify and isolate cases, reduce transmission through contact tracing, and provide support to residents during isolation or quarantine periods. In this paper, we describe lessons learned with respect to planning and implementation of case notification and contact tracing. Our findings are based on extensive document review and analysis of 74 key informant interviews with T2 leadership and frontline staff, cases, and contacts conducted between January and September 2022. Interviews elicited respondent background, history of program development, program leadership and structure, goals of the program, program evolution, staffing, data systems, elements of community engagement, trust with community, program reach, timeliness, equity, general barriers and challenges, general facilitators and best practices, and recommendations/improvement for the program. Facilitators and barriers revealed in the interviews primarily revolved around hiring and managing staff, data and technology, and quality of interactions with the public. Based on these facilitators and barriers, we identify suggestions to support effective planning and response for future case notification and contact tracing programs, including recommendations for planning during latent periods, case management and data systems, and processes for outreach to cases and contacts.

Comparison of the efficacy of COVID-19 responses in South Korea and the United States.

BACKGROUND: The COVID-19 pandemic devastated many countries worldwide by causing large numbers of fatalities. In our research, we wanted to answer the question: Why was there such a large difference in the mortality rate between South Korea and the United States? This is because many East Asian countries, such as Korea, had a lower mortality rate than many countries, including developed ones, across the world - the mortality rate of South Korea was about five times lower than the United States. METHODS: This study comprehensively compares strategies used to address the COVID-19 pandemic in two different countries: South Korea and the United States. The various aspects of these two countries' responses are examined, including initial response, information dissemination and public compliance, mitigation strategies, and vaccine rollout and their impacts. RESULTS: Early and widespread testing, rigorous contact tracing, the clear release of government information, and an organized vaccine rollout powered a proactive approach in South Korea. The United States had a contrasting response consisting of delayed and more decentralized measures, where testing lagged due to varying policies and the political controversies facing vaccine distribution. CONCLUSIONS: We signify the gravity of rapid response and testing, clear communication, and efficient vaccine distribution, as we believe this could correlate with a lower mortality rate. In addition, we discuss future directions, including the need for a specific health infrastructure and protocol against highly infectious outbreaks.

Main findings: The study suggests strategies that may be effective ways to reduce fatalities during a pandemic through a comparative analysis of COVID-19 responses in South Korea and the United States.Added knowledge: This review further consolidates the importance of an effective defense strategy involving testing, contact tracing, information dissemination, and vaccine rollouts.Global health impact for policy and action: There is a need for the development of a specific pandemic response infrastructure against fast-spreading and fatal viruses involving effective policies that take into account both the freedom and health of citizens.

Identifying Barriers to the Adoption of Digital Contact Tracing Apps in England: Semistructured Interview Study With Professionals Involved in the Pandemic Response.

BACKGROUND: The NHS (National Health Service) COVID-19 app was a digital contact tracing app (DCTA) used in England in response to the COVID-19 pandemic. The aim of which was to limit the spread of COVID-19 by providing exposure alerts. At the time of the pandemic, questions were raised regarding the effectiveness and cost of the NHS COVID-19 app and whether DCTAs have a role in future pandemics. OBJECTIVE: This study aims to explore key barriers to DCTAs in England during the COVID-19 pandemic. METHODS: This is a qualitative study using semistructured video interviews conducted with professionals in public health, digital health, clinicians, health care law, and health executives who had an active role in the COVID-19 pandemic. These interviews aimed to explore the perspective of different experts involved in the pandemic response and gauge their opinions on the key barriers to DCTAs in England during the COVID-19 pandemic. The initial use of maximum variation sampling combined with a snowball sampling approach ensured diversity within the cohort of interviewees. Interview transcripts were then analyzed using Braun and Clarke's 6 steps for thematic analysis. RESULTS: Key themes that acted as barriers to DCTAs were revealed by interviewees such as privacy concerns, poor communication, technological accessibility, digital literacy, and incorrect use of the NHS COVID-19 app. Interviewees believed that some of these issues stemmed from poor governmental communication and a lack of transparency regarding how the NHS COVID-19 app worked, resulting in decreased public trust. Moreover, interviewees highlighted that a lack of social support integration within the NHS COVID-19 app and delayed app notification period also contributed to the poor adoption rates. CONCLUSIONS: Qualitative findings from interviews highlighted barriers to the NHS COVID-19 app, which can be applied to DCTAs more widely and highlight some important implications for the future use of DCTAS. There was no consensus among interviewees as to whether the NHS COVID-19 app was a success; however, all interviewees provided recommendations for improvements in creating and implementing DCTAs in the future.

Open Data-Driven Decision Making: Observations from COVID-19 Minimal Essential Data, Canton Zürich, Switzerland.

This research delves into the relationship between open data and decision making, drawing insights from the utilization of Minimal Essential Data (MED) during the COVID-19 pandemic in Canton Zürich, Switzerland. It examines observations and lessons learned, emphasizing the pivotal role of open data in response to an emergency. Through a case study approach, this article sheds light on the practical implications and observed strategies in publishing open data from digital public health surveillance software. While it is arguable that open data has the potential to enhance data quality in the context of a public health emergency, our findings indicate that it is firstly essential that a Minimal Essential Dataset (MED) is defined and publicly published, an open data publication platform and procedure are secondly in place (ideally following FAIR data principles), and thirdly, that regular data quality reviews are conducted.

Estimating the human bottleneck for contact tracing.

The SARS-CoV-2 pandemic has highlighted the importance of contact tracing for epidemiological mitigation. Contact tracing interviews (CTIs) typically rely on episodic memory, which is prone to decline over time. Here, we provide a quantitative estimate of reporting decline for age- and gender-representative samples from the United Kingdom and Germany, emulating >15,000 CTIs. We find that the number of reported contacts declines as a power function of recall delay and is significantly higher for younger subjects and for those who used memory aids, such as a scheduler. We further find that these factors interact with delay: Older subjects and those who made no use of memory aids have steeper decline functions. These findings can inform epidemiological modeling and policies in the context of infectious diseases.

'Barely keeping the wheels on the trolley': A qualitative study of the New Zealand COVID Tracer App.

Digital contact tracing apps were developed to help control the spread of COVID-19 but research exploring these apps has underrepresented both 'at-risk' communities and contact tracers. Our study examines perspectives of the New Zealand COVID Tracer app among 53 participants, comprising policy advisors, contact tracers, and Maori, Pacific, and disability stakeholders, underpinned by the theory of social construction of which positions technology within the social context in which it evolves, operates, and is negotiated. Although community stakeholders believed the app helped safeguard communities from COVID-19, the health officials' views on the app's usefulness in contact tracing varied. Participants who oversaw the app's technical development generally perceived it as being more useful, particularly regarding Bluetooth proximity detection, in contrast with contact tracers' perceptions. The findings highlight a disconnection between public sentiment and operational reality in the use of the app and the need for improved collaboration and consultation in future contact tracing responses.

Utilizing rapid qualitative assessment and thematic analysis methods to identify and share promising case investigation and contact tracing practices with people in refugee, immigrant, and migrant communities during COVID-19.

In the early months of the COVID-19 pandemic The National Resource Center for Refugees, Immigrants and Migrants (NRC-RIM) was established. NRC-RIM initially sought to rapidly identify promising case investigation and contact tracing (CICT) practices within refugee, immigrant, and migrant communities. Between September 2020 and April 2021, the team conducted 60 interviews with individuals from cross-sector organizations (i.e., public health, health systems, community experts/organizations) working with refugee, immigrant and migrant communities in health and public health capacities related to COVID-19. The overarching aim was to identify and amplify innovative promising and best practices for CICT with refugee, immigrant, and migrant communities, including an exploration of barriers and facilitators. We utilized layered methods to rapidly assess, summarize and disseminate promising practices while simultaneously completing four thematic analyses including: (1) public health organizations; (2) health system organizations; (3) community leaders and organizations; and (4) vaccine planning and access across the three sectors. The primary objective of this article is to describe the project design, applied methods, and team science approach we utilized. We found that rapid identification and dissemination of promising practices, and barriers and facilitators for CICT with refugee, immigrant and migrant communities was feasible during a public health emergency. This approach was essential for identifying and widely sharing culturally and linguistically concordant public health practices.

A Wireless Wearable Ecosystem for Social Network Analysis in Free-living Animals.

Studying animal social systems requires understanding variations in contact and interaction, influenced by factors like environmental conditions, resource availability, and predation risk. Traditional observational methods have limitations, but advancements in sensor technologies and data analytics provide new opportunities. We developed a wireless wearable sensor system, "Juxta," with features such as modular battery packs and a smartphone app for data collection. A pilot study on free-living prairie voles (Microtus ochrogaster), a species with complex social behavior, demonstrated Juxta's potential for studying social networks and behavior. We propose a framework for merging temporal, spatial, and event-driven data, which can help explore complex social dynamics across species and environments.

Epidemic exposure risk assessment in digital contact tracing: A fuzzy logic approach.

Background: Bluetooth low energy (BLE)-based contact-tracing applications were widely used during the COVID-19 pandemic. However, the use of only the received signal strength feature for proximity calculations may not be adaptable to different virus variants or scalable for other potential epidemic diseases. Objective: This study presents a novel framework in regard to evaluating and classifying personal exposure risk that considers both contact features, which include distance and length of contact, and environment features, which include crowd size and the number of recently infected cases in the environment. The framework utilizes a fuzzy expert system that is adaptable to different virus variants. Methods: The proposed method was tested on two viruses with different close contact features, which used four membership functions and 256 fuzzy rule sets. Results: The proposed framework classified personal exposure risks into four classes, which include low, medium, high, and too high risk. The empirical results showed that the fuzzy logic-based approach reduced the number of false positive cases and demonstrated better accuracy and precision than the current BLE-only approaches. Conclusions: The proposed framework provides a more practical and adaptable method in regard to assessing exposure risks in real-world scenarios. It has the potential to be scalable and adaptable to different virus variants and other potential epidemic diseases by considering both contact and environment features. These findings may be useful in order to develop more effective digital contact-tracing applications and policies.