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From early in the coronavirus disease 2019 (COVID‐19) pandemic, there was interest in using machine learning methods to predict COVID‐19 infection status based on vocal audio signals, for example, cough recordings. However, early studies had limitations in terms of data collection and of how the performances of the proposed predictive models were assessed. This article describes how these limitations have been overcome in a study carried out by the Turing‐RSS Health Data Laboratory and the UK Health Security Agency. As part of the study, the UK Health Security Agency collected a dataset of acoustic recordings, SARS‐CoV‐2 infection status and extensive study participant meta‐data. This allowed us to rigorously assess state‐of‐the‐art machine learning techniques to predict SARS‐CoV‐2 infection status based on vocal audio signals. The lessons learned from this project should inform future studies on statistical evaluation methods to assess the performance of machine learning techniques for public health tasks. [ABSTRACT FROM AUTHOR]

Although deep learning algorithms show increasing promise for disease diagnosis, their use with rapid diagnostic tests performed in the field has not been extensively tested. Here we use deep learning to classify images of rapid human immunodeficiency virus (HIV) tests acquired in rural South Africa. Using newly developed image capture protocols with the Samsung SM-P585 tablet, 60 fieldworkers routinely collected images of HIV lateral flow tests. From a library of 11,374 images, deep learning algorithms were trained to classify tests as positive or negative. A pilot field study of the algorithms deployed as a mobile application demonstrated high levels of sensitivity (97.8%) and specificity (100%) compared with traditional visual interpretation by humans--experienced nurses and newly trained community health worker staff--and reduced the number of false positives and false negatives. Our findings lay the foundations for a new paradigm of deep learning-enabled diagnostics in low- and middle-income countries, termed REASSURED diagnostics.sup.1, an acronym for real-time connectivity, ease of specimen collection, affordable, sensitive, specific, user-friendly, rapid, equipment-free and deliverable. Such diagnostics have the potential to provide a platform for workforce training, quality assurance, decision support and mobile connectivity to inform disease control strategies, strengthen healthcare system efficiency and improve patient outcomes and outbreak management in emerging infections. In a pilot field study conducted in rural South Africa, deep learning algorithms can accurately classify rapid HIV tests as positive or negative, highlighting the potential of deep learning-enabled diagnostics for use in low- and middle-income countries., Author(s): Valérian Turbé [sup.1] , Carina Herbst [sup.2] , Thobeka Mngomezulu [sup.2] , Sepehr Meshkinfamfard [sup.1] , Nondumiso Dlamini [sup.2] , Thembani Mhlongo [sup.2] , Theresa Smit [sup.2] , Valeriia [...]

Digital technologies are being harnessed to support the public-health response to COVID-19 worldwide, including population surveillance, case identification, contact tracing and evaluation of interventions on the basis of mobility data and communication with the public. These rapid responses leverage billions of mobile phones, large online datasets, connected devices, relatively low-cost computing resources and advances in machine learning and natural language processing. This Review aims to capture the breadth of digital innovations for the public-health response to COVID-19 worldwide and their limitations, and barriers to their implementation, including legal, ethical and privacy barriers, as well as organizational and workforce barriers. The future of public health is likely to become increasingly digital, and we review the need for the alignment of international strategies for the regulation, evaluation and use of digital technologies to strengthen pandemic management, and future preparedness for COVID-19 and other infectious diseases. The COVID-19 pandemic has resulted in an accelerated development of applications for digital health, including symptom monitoring and contact tracing. Their potential is wide ranging and must be integrated into conventional approaches to public health for best effect., Author(s): Jobie Budd [sup.1] [sup.2] , Benjamin S. Miller [sup.1] , Erin M. Manning [sup.1] , Vasileios Lampos [sup.3] , Mengdie Zhuang [sup.4] , Michael Edelstein [sup.5] , Geraint Rees [...]

Mobile health, or 'mHealth', is the application of mobile devices, their components and related technologies to healthcare. It is already improving patients' access to treatment and advice. Now, in combination with internet-connected diagnostic devices, it offers novel ways to diagnose, track and control infectious diseases and to improve the efficiency of the health system. Here we examine the promise of these technologies and discuss the challenges in realizing their potential to increase patients' access to testing, aid in their treatment and improve the capability of public health authorities to monitor outbreaks, implement response strategies and assess the impact of interventions across the world. Combining mobile phone technologies with infectious disease diagnostics can increase patients' access to testing and treatment and provide public health authorities with new ways to monitor and control outbreaks of infectious diseases., Author(s): Christopher S. Wood [sup.1] [sup.2] [sup.3] [sup.4] , Michael R. Thomas [sup.1] [sup.2] [sup.3] , Jobie Budd [sup.5] , Tivani P. Mashamba-Thompson [sup.6] , Kobus Herbst [sup.7] , Deenan [...]

Background: Understanding inequalities in outcomes between demographic groups is a necessary step in addressing them in clinical care. Inequalities in treatment uptake between demographic groups may explain disparities in outcomes in people with first-episode psychosis (FEP)., Aims: To investigate disparities between broad demographic groups in symptomatic improvement in patients with FEP and their relationship to treatment uptake., Method: We used data from 6813 patients from the 2021-2022 National Clinical Audit of Psychosis data-set. Data were grouped by category type to obtain mean outcomes before adjustment to see whether disparities in outcomes remained after differences in treatment uptake had been accounted for. After matching, the average effect of each demographic variable in terms of outcome change was calculated. Moderator effects on specific treatments were investigated using interaction terms in a regression model., Results: Observational results showed that patients aged 18-24 years were less likely to improve in outcome, unless adjusted for intervention uptake. Patients classified as Black and Black British were less likely to improve in outcome (moderation effect 0.04, 95% CI 0-0.07) after adjusting for treatment take-up and demographic factors. Regression analysis showed the general positive effect of supported employment interventions in improving outcomes (coefficient -0.13, 95% CI -0.07 to -0.18, P < 0.001), and moderator analysis suggested targeting particular groups for interventions., Conclusions: Inequalities in treatment uptake and psychotic symptom outcome of FEP by social and demographic factors require monitoring over time. Our analysis provides a framework for monitoring health inequalities across national clinical audits in the UK.