Preventing Outbreaks through Interactive, Experiential Real-Life Simulations.

Operation Outbreak (OO) is a Bluetooth-based simulation platform that teaches students how pathogens spread and the impact of interventions, thereby facilitating the safe reopening of schools. OO also generates data to inform epidemiological models and prevent future outbreaks. Before SARS-CoV-2 was reported, we repeatedly simulated a virus with similar features, correctly predicting many human behaviors later observed during the pandemic.

Digital measurement of SARS-CoV-2 transmission risk from 7 million contacts.

How likely is it to become infected by SARS-CoV-2 after being exposed? Almost everyone wondered about this question during the COVID-19 pandemic. Contact-tracing apps1,2 recorded measurements of proximity3 and duration between nearby smartphones. Contacts-individuals exposed to confirmed cases-were notified according to public health policies such as the 2 m, 15 min guideline4,5, despite limited evidence supporting this threshold. Here we analysed 7 million contacts notified by the National Health Service COVID-19 app6,7 in England and Wales to infer how app measurements translated to actual transmissions. Empirical metrics and statistical modelling showed a strong relation between app-computed risk scores and actual transmission probability. Longer exposures at greater distances had risk similar to that of shorter exposures at closer distances. The probability of transmission confirmed by a reported positive test increased initially linearly with duration of exposure (1.1% per hour) and continued increasing over several days. Whereas most exposures were short (median 0.7 h, interquartile range 0.4-1.6), transmissions typically resulted from exposures lasting between 1 h and several days (median 6 h, interquartile range 1.4-28). Households accounted for about 6% of contacts but 40% of transmissions. With sufficient preparation, privacy-preserving yet precise analyses of risk that would inform public health measures, based on digital contact tracing, could be performed within weeks of the emergence of a new pathogen.

The epidemiological impact of the NHS COVID-19 app.

The COVID-19 pandemic has seen the emergence of digital contact tracing to help to prevent the spread of the disease. A mobile phone app records proximity events between app users, and when a user tests positive for COVID-19, their recent contacts can be notified instantly. Theoretical evidence has supported this new public health intervention1-6, but its epidemiological impact has remained uncertain7. Here we investigate the impact of the National Health Service (NHS) COVID-19 app for England and Wales, from its launch on 24 September 2020 to the end of December 2020. It was used regularly by approximately 16.5 million users (28% of the total population), and sent approximately 1.7 million exposure notifications: 4.2 per index case consenting to contact tracing. We estimated that the fraction of individuals notified by the app who subsequently showed symptoms and tested positive (the secondary attack rate (SAR)) was 6%, similar to the SAR for manually traced close contacts. We estimated the number of cases averted by the app using two complementary approaches: modelling based on the notifications and SAR gave an estimate of 284,000 (central 95% range of sensitivity analyses 108,000-450,000), and statistical comparison of matched neighbouring local authorities gave an estimate of 594,000 (95% confidence interval 317,000-914,000). Approximately one case was averted for each case consenting to notification of their contacts. We estimated that for every percentage point increase in app uptake, the number of cases could be reduced by 0.8% (using modelling) or 2.3% (using statistical analysis). These findings support the continued development and deployment of such apps in populations that are awaiting full protection from vaccines.

Mobility network models of COVID-19 explain inequities and inform reopening.

The coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)1. Here we introduce a metapopulation susceptible-exposed-infectious-removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of 'superspreader' points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2-8 solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19.

Acute Effects of Coffee Consumption on Health among Ambulatory Adults.

BACKGROUND: Coffee is one of the most commonly consumed beverages in the world, but the acute health effects of coffee consumption remain uncertain. METHODS: We conducted a prospective, randomized, case-crossover trial to examine the effects of caffeinated coffee on cardiac ectopy and arrhythmias, daily step counts, sleep minutes, and serum glucose levels. A total of 100 adults were fitted with a continuously recording electrocardiogram device, a wrist-worn accelerometer, and a continuous glucose monitor. Participants downloaded a smartphone application to collect geolocation data. We used daily text messages, sent over a period of 14 days, to randomly instruct participants to consume caffeinated coffee or avoid caffeine. The primary outcome was the mean number of daily premature atrial contractions. Adherence to the randomization assignment was assessed with the use of real-time indicators recorded by the participants, daily surveys, reimbursements for date-stamped receipts for coffee purchases, and virtual monitoring (geofencing) of coffee-shop visits. RESULTS: The mean (±SD) age of the participants was 39±13 years; 51% were women, and 51% were non-Hispanic White. Adherence to the random assignments was assessed to be high. The consumption of caffeinated coffee was associated with 58 daily premature atrial contractions as compared with 53 daily events on days when caffeine was avoided (rate ratio, 1.09; 95% confidence interval [CI], 0.98 to 1.20; P = 0.10). The consumption of caffeinated coffee as compared with no caffeine consumption was associated with 154 and 102 daily premature ventricular contractions, respectively (rate ratio, 1.51; 95% CI, 1.18 to 1.94); 10,646 and 9665 daily steps (mean difference, 1058; 95% CI, 441 to 1675); 397 and 432 minutes of nightly sleep (mean difference, 36; 95% CI, 25 to 47); and serum glucose levels of 95 mg per deciliter and 96 mg per deciliter (mean difference, -0.41; 95% CI, -5.42 to 4.60). CONCLUSIONS: In this randomized trial, the consumption of caffeinated coffee did not result in significantly more daily premature atrial contractions than the avoidance of caffeine. (Funded by the University of California, San Francisco, and the National Institutes of Health; CRAVE ClinicalTrials.gov number, NCT03671759.).

IMPRINTS.CETSA and IMPRINTS.CETSA.app: an R package and a Shiny application for the analysis and interpretation of IMPRINTS-CETSA data.

IMPRINTS-CETSA (Integrated Modulation of Protein Interaction States-Cellular Thermal Shift Assay) provides a highly resolved means to systematically study the interactions of proteins with other cellular components, including metabolites, nucleic acids and other proteins, at the proteome level, but no freely available and user-friendly data analysis software has been reported. Here, we report IMPRINTS.CETSA, an R package that provides the basic data processing framework for robust analysis of the IMPRINTS-CETSA data format, from preprocessing and normalization to visualization. We also report an accompanying R package, IMPRINTS.CETSA.app, which offers a user-friendly Shiny interface for analysis and interpretation of IMPRINTS-CETSA results, with seamless features such as functional enrichment and mapping to other databases at a single site. For the hit generation part, the diverse behaviors of protein modulations have been typically segregated with a two-measure scoring method, i.e. the abundance and thermal stability changes. We present a new algorithm to classify modulated proteins in IMPRINTS-CETSA experiments by a robust single-measure scoring. In this way, both the numerical changes and the statistical significances of the IMPRINTS information can be visualized on a single plot. The IMPRINTS.CETSA and IMPRINTS.CETSA.app R packages are freely available on GitHub at https://github.com/nkdailingyun/IMPRINTS.CETSA and https://github.com/mgerault/IMPRINTS.CETSA.app, respectively. IMPRINTS.CETSA.app is also available as an executable program at https://zenodo.org/records/10636134.

Population flow drives spatio-temporal distribution of COVID-19 in China.

Sudden, large-scale and diffuse human migration can amplify localized outbreaks of disease into widespread epidemics1-4. Rapid and accurate tracking of aggregate population flows may therefore be epidemiologically informative. Here we use 11,478,484 counts of mobile phone data from individuals leaving or transiting through the prefecture of Wuhan between 1 January and 24 January 2020 as they moved to 296 prefectures throughout mainland China. First, we document the efficacy of quarantine in ceasing movement. Second, we show that the distribution of population outflow from Wuhan accurately predicts the relative frequency and geographical distribution of infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) until 19 February 2020, across mainland China. Third, we develop a spatio-temporal 'risk source' model that leverages population flow data (which operationalize the risk that emanates from epidemic epicentres) not only to forecast the distribution of confirmed cases, but also to identify regions that have a high risk of transmission at an early stage. Fourth, we use this risk source model to statistically derive the geographical spread of COVID-19 and the growth pattern based on the population outflow from Wuhan; the model yields a benchmark trend and an index for assessing the risk of community transmission of COVID-19 over time for different locations. This approach can be used by policy-makers in any nation with available data to make rapid and accurate risk assessments and to plan the allocation of limited resources ahead of ongoing outbreaks.

Directing smartphone use through the self-nudge app one sec.

Research suggests various associations of smartphone use with a range of physical, psychological, and performance dimensions. Here, we test one sec, a self-nudging app that is installed by the user in order to reduce the mindless use of selected target apps on the smartphone. When users attempt to open a target app of their choice, one sec interferes with a pop-up, which combines a deliberation message, friction by a short waiting time, and the option to dismiss opening the target app. In a field-experiment, we collected behavioral user data from 280 participants over 6 wk, and conducted two surveys before and after the intervention span. one sec reduced the usage of target apps in two ways. First, on average 36% of the times participants attempted opening a target app, they closed that app again after one sec interfered. Second, over the course of 6 wk, users attempted to open target apps 37% less than in the first week. In sum, one sec decreased users' actual opening of target apps by 57% after six consecutive weeks. Afterward, participants also reported spending less time with their apps and indicated increased satisfaction with their consumption. To disentangle one sec's effects, we tested its three psychological features in a preregistered online experiment (N = 500) that measured the consumption of real and viral social media video clips. We found that providing the additional option to dismiss the consumption attempt had the strongest effect. While the friction by time delay also reduced consumption instances, the deliberation message was not effective.

The machine learning-powered BirdNET App reduces barriers to global bird research by enabling citizen science participation.

The BirdNET App, a free bird sound identification app for Android and iOS that includes over 3,000 bird species, reduces barriers to citizen science while generating tens of millions of bird observations globally that can be used to replicate known patterns in avian ecology.

Meet the Metaorganism: A web-based learning app for undergraduate and graduate biology students.

Meet the Metaorganism is a web-based learning app that combines three fundamental biological concepts (coevolution, community dynamics, and immune system) with latest scientific findings using the metaorganism as a central case study. In a transdisciplinary team of scientists, information designers, programmers, science communicators, and educators, we conceptualized and developed the app according to the latest didactic and scientific findings and aimed at setting new standards in visual design, digital knowledge transfer, and online education. A content management system allows continuous integration of new findings, which enables us to expand the app with the dynamics of the research field. Students can thus gain a close insight and connection to current research, and at the same time learn that knowledge is not static but grows dynamically. Especially in the realm of the easily accessible metaorganism research, visualization plays an essential role to keep complex processes understandable and memorable. Meet the Metaorganism is freely available online and can be accessed here: www.metaorganism.app.

Pest Alert Tool-a web-based application for flagging species of concern in metabarcoding datasets.

Advances in high-throughput sequencing (HTS) technologies and their increasing affordability have fueled environmental DNA (eDNA) metabarcoding data generation from freshwater, marine and terrestrial ecosystems. Research institutions worldwide progressively employ HTS for biodiversity assessments, new species discovery and ecological trend monitoring. Moreover, even non-scientists can now collect an eDNA sample, send it to a specialized laboratory for analysis and receive in-depth biodiversity record from a sampling site. This offers unprecedented opportunities for biodiversity assessments across wide temporal and spatial scales. The large volume of data produced by metabarcoding also enables incidental detection of species of concern, including non-indigenous and pathogenic organisms. We introduce an online app-Pest Alert Tool-for screening nuclear small subunit 18S ribosomal RNA and mitochondrial cytochrome oxidase subunit I datasets for marine non-indigenous species as well as unwanted and notifiable marine organisms in New Zealand. The output can be filtered by minimum length of the query sequence and identity match. For putative matches, a phylogenetic tree can be generated through the National Center for Biotechnology Information's BLAST Tree View tool, allowing for additional verification of the species of concern detection. The Pest Alert Tool is publicly available at https://pest-alert-tool-prod.azurewebsites.net/.

In T2DM requiring insulin initiation, icodec titrated with an app safely reduced HbA1c vs. daily basal insulin analogues at 52 wk.

SOURCE CITATION: Bajaj HS, Aberle J, Davies M, et al. Once-weekly insulin icodec with dosing guide app versus once-daily basal insulin analogues in insulin-naive type 2 diabetes (ONWARDS 5): a randomized trial. Ann Intern Med. 2023;176:1476-1485. 37748181.

tRigon: an R package and Shiny App for integrative (path-)omics data analysis.

BACKGROUND: Pathomics facilitates automated, reproducible and precise histopathology analysis and morphological phenotyping. Similar to molecular omics, pathomics datasets are high-dimensional, but also face large outlier variability and inherent data missingness, making quick and comprehensible data analysis challenging. To facilitate pathomics data analysis and interpretation as well as support a broad implementation we developed tRigon (Toolbox foR InteGrative (path-)Omics data aNalysis), a Shiny application for fast, comprehensive and reproducible pathomics analysis. RESULTS: tRigon is available via the CRAN repository ( https://cran.r-project.org/web/packages/tRigon ) with its source code available on GitLab ( https://git-ce.rwth-aachen.de/labooratory-ai/trigon ). The tRigon package can be installed locally and its application can be executed from the R console via the command 'tRigon::run_tRigon()'. Alternatively, the application is hosted online and can be accessed at https://labooratory.shinyapps.io/tRigon . We show fast computation of small, medium and large datasets in a low- and high-performance hardware setting, indicating broad applicability of tRigon. CONCLUSIONS: tRigon allows researchers without coding abilities to perform exploratory feature analyses of pathomics and non-pathomics datasets on their own using a variety of hardware.

Computational application of internationally harmonized defined approaches to skin sensitization: DASS App.

BACKGROUND: Chemically induced skin sensitization, or allergic contact dermatitis, is a common occupational and public health issue. Regulatory authorities require an assessment of potential to cause skin sensitization for many chemical products. Defined approaches for skin sensitization (DASS) identify potential chemical skin sensitizers by integrating data from multiple non-animal tests based on human cells, molecular targets, and computational model predictions using standardized data interpretation procedures. While several DASS are internationally accepted by regulatory agencies, the data interpretation procedures vary in logical complexity, and manual application can be time-consuming or prone to error. RESULTS: We developed the DASS App, an open-source web application, to facilitate user application of three regulatory testing strategies for skin sensitization assessment: the Two-out-of-Three (2o3), the Integrated Testing Strategy (ITS), and the Key Event 3/1 Sequential Testing Strategy (KE 3/1 STS) without the need for software downloads or computational expertise. The application supports upload and analysis of user-provided data, includes steps to identify inconsistencies and formatting issues, and provides predictions in a downloadable format. CONCLUSION: This open-access web-based implementation of internationally harmonized regulatory guidelines for an important public health endpoint is designed to support broad user uptake and consistent, reproducible application. The DASS App is freely accessible via https://ntp.niehs.nih.gov/go/952311 and all scripts are available on GitHub ( https://github.com/NIEHS/DASS ).

Comparing Location Data From Smartphone and Dedicated Global Positioning System Devices: Implications for Epidemiologic Research.

In this study, we compared location data from a dedicated Global Positioning System (GPS) device with location data from smartphones. Data from the Interventions, Equity, and Action in Cities Team (INTERACT) Study, a study examining the impact of urban-form changes on health in 4 Canadian cities (Victoria, Vancouver, Saskatoon, and Montreal), were used. A total of 337 participants contributed data collected for about 6 months from the Ethica Data smartphone application (Ethica Data Inc., Toronto, Ontario, Canada) and the SenseDoc dedicated GPS (MobySens Technologies Inc., Montreal, Quebec, Canada) during the period 2017-2019. Participants recorded an average total of 14,781 Ethica locations (standard deviation, 19,353) and 197,167 SenseDoc locations (standard deviation, 111,868). Dynamic time warping and cross-correlation were used to examine the spatial and temporal similarity of GPS points. Four activity-space measures derived from the smartphone app and the dedicated GPS device were compared. Analysis showed that cross-correlations were above 0.8 at the 125-m resolution for the survey and day levels and increased as cell size increased. At the day or survey level, there were only small differences between the activity-space measures. Based on our findings, we recommend dedicated GPS devices for studies where the exposure and the outcome are both measured at high frequency and when the analysis will not be aggregate. When the exposure and outcome are measured or will be aggregated to the day level, the dedicated GPS device and the smartphone app provide similar results.

Electronic health intervention to manage symptoms of immunotherapy in patients with cancer (SOFIA): Results from a randomized controlled pilot trial.

BACKGROUND: For patients receiving immune checkpoint inhibitors, early detection of immune-related adverse events (irAEs) is critical for one's safety. To this end, a smartphone app (SOFIA) was developed that featured the assessment of electronic patient-reported outcomes (ePROs) focusing on irAEs as well as a set of comprehensive supportive information. Its feasibility and preliminary efficacy were evaluated in a randomized controlled trial (RCT). METHODS: Patients who received immune checkpoint inhibition therapy were randomly assigned to an intervention group (IG) or a control group (CG; care as usual). During the 12-week intervention period, IG patients used SOFIA to report twice weekly ePROs and receive cancer- and immunotherapy-relevant contents. Before a patient's next clinical visit, the physician in charge was given the ePRO reports. The primary objective was to test the feasibility of SOFIA. Furthermore, the preliminary efficacy of SOFIA for health-related quality of life (HRQOL), psychosocial outcomes, and medical data was examined. Clinical outcomes were assessed at baseline (T0), post-intervention (T1), and a 3-month follow-up (T2). RESULTS: Seventy-one patients were randomized to the IG (n = 34) or the CG (n = 37). SOFIA showed high feasibility and acceptance. At T1, patients in the IG reported significantly better HRQOL and role functioning and less depression, distress, and appetite loss. No significant differences were revealed regarding medical data, the utilization of supportive care services, or survival. CONCLUSIONS: SOFIA showed high feasibility and acceptance and improved HRQOL and psychosocial outcomes. These results suggest further evaluation of efficacy in a large-scale confirmatory multicenter RCT.

Scaling national and international improvement in virtual gene panel curation via a collaborative approach to discordance resolution.

Clinical validity assessments of gene-disease associations underpin analysis and reporting in diagnostic genomics, and yet wide variability exists in practice, particularly in use of these assessments for virtual gene panel design and maintenance. Harmonization efforts are hampered by the lack of agreed terminology, agreed gene curation standards, and platforms that can be used to identify and resolve discrepancies at scale. We undertook a systematic comparison of the content of 80 virtual gene panels used in two healthcare systems by multiple diagnostic providers in the United Kingdom and Australia. The process was enabled by a shared curation platform, PanelApp, and resulted in the identification and review of 2,144 discordant gene ratings, demonstrating the utility of sharing structured gene-disease validity assessments and collaborative discordance resolution in establishing national and international consensus.

Gait retraining to reduce falls: an experimental study toward scalable and personalised use in the home.

BACKGROUND: Age-related neurological conditions can result in poor mobility typified by gait abnormalities and falls, increasing risk of frailty and lowering quality of life. In the UK, the expense and inaccessibility of services to improve mobility through gait training (eg, auditory cueing) is a public health issue. Contemporary and scalable pervasive technologies for widespread public use could provide an affordable and accessible solution. We aimed to show the preliminary efficacy of a novel smartphone app that provides a personalised approach to mobility and gait assessment while facilitating gait training. METHODS: In this experimental study, we recruited participants aged 22-46 years with no physical functional impairments (ie, no age-related neurological condition and who could walk unaided) from Northumbria University staff (Newcastle upon Tyne, UK) between April 19, and May 26. Participants wore a smartphone on their lower back. Inertial data from the smartphone were recorded during two walks, one at a self-selected pace and the other with a personalised auditory cue via headphones (+10% pace on walk 1). Smartphone app functionality enabled the measurement of clinically relevant gait characteristics via a Python-based Cloud server. We compared smartphone-based mobility or gait characteristics with a gold-standard reference (Opal Mobility Lab, APDM). We used Pearson and intraclass correlation coefficients (ICC2,1) to examine agreement between the novel app and reference. The study ran from April 4 to July 21, 2023. This study received ethics approval from the Northumbria University Ethics committee, and all participants provided written informed consent. FINDINGS: Ten adults were recruited (six women and four men; mean age 27·4 years [SD 6·2], mean weight 79·6 kg [SD 12·7], mean height 174·7 cm [SD 7·9]). High levels of agreement were found between the smartphone app and reference, quantified by Pearson (≥0·858) and ICC values (≥0·911). The personalised cueing intervention increased the mean cadence by an average of 11%, which shows good participant adherence to cueing via an app. INTERPRETATION: Here, we propose a contemporary approach to increase the accessibility to a health-based intervention. Preliminary findings suggest the smartphone app is a suitable tool for personalised mobility or gait assessment while facilitating gait training. Use of a scalable app could be an accessible and affordable method for improving mobility to reduce falls in the home. Here, current limitations are the lack of investigation with the smartphone app for neurological gait assessment on older adults and the lack of information on participants app experience, but this will be included in future work. The pervasive use of smartphones enables a decentralised approach to overcoming issues such as frailty and logistical challenges of travelling to bespoke clinics. FUNDING: National Institute of Health and Care Research (NIHR) Applied Research Collaboration (ARC) North-East and North Cumbria (NENC); Faculty of Engineering and Environment at Northumbria University.