Michael Parker

Background: Exposure-history data collected during epidemics or pandemics within digital contact-tracing applications hold immense potential for rapid, scalable epidemiological inference. The sensitivity of these data makes privacy-preserving designs essential to secure public trust and app uptake. However, during the COVID-19 pandemic, such approaches were not yet available at the scale or maturity required for deployment. We present a Bayesian decentralised framework to infer transmission dynamics from exposure-history data while protecting user privacy. Methods: Our approach is based on iterative communication between user devices and a central server, computing log-likelihood gradients on device from exposure-history data and centrally updating parameter values using only aggregated gradients. Applying this framework to synthetic exposure-history data from a hypothetical respiratory pathogen epidemic simulated on dynamic contact networks, we assessed its ability to estimate the underlying transmission parameters, perform infector attribution, and reconstruct serial-interval (SI) distributions. We systematically explored how inference was affected by incomplete exposure-history data arising from partial app adoption, case under-ascertainment, and incomplete exposure logging, and by transmission scenarios. Findings: Our decentralised framework accurately recovered transmission parameters, closely matching estimates derived from centralised data with no systematic bias in posterior means. Parameter recovery remained robust when hazards from unobserved exposures were known as a daily average across the population. Although incomplete exposure-history data and specific transmission scenarios introduced compensatory parameter biases, the inferred host infectiousness profile retained sufficient accuracy, and the weekly SI distributions reconstructed from infector attributions closely resembled the ground-truth distributions. Interpretation: Our findings highlight that, beyond its primary role as a control measure, digital contact tracing could help characterise transmission dynamics almost in real time and inform public health interventions while preserving user privacy. Funding: Oxford Martin School Programme on Digital Pandemic Preparedness; National Institute for Health and Care Research (NIHR) Health Protection Research Unit in Emerging and Zoonotic Infections. Evidence before this study: We searched PubMed for studies published from database inception to Sept 30, 2026, using the keywords (“digital contact tracing” OR “exposure notification” OR “contact tracing app”) AND (“federated learning” OR “decentralised” OR “privacy-preserving” OR “statistical inference” OR “Bayesian inference”) AND (“transmission” OR “serial interval” OR “infectiousness”). During the COVID-19 pandemic, digital contact-tracing (DCT) apps were deployed at an unprecedented scale: 224 COVID-19 apps were launched across 127 countries, 128 of which supported exposure notification. In England and Wales, the NHS COVID-19 app had 16.5 million regular users (28% of the total population) and averted an estimated 1 million cases and 9,600 deaths in its first year. To protect privacy, however, most apps used decentralised architectures that kept exposure-history data on users’ devices. This privacy-preserving principle has largely prevented these data from being used for epidemiological inference to understand transmission dynamics, such as estimating the serial-interval (SI) distribution and host infectiousness profile over time. While existing decentralised algorithms estimate each user’s infection risk, we found no study that developed and evaluated a framework for estimating population-level epidemiological parameters from exposure-history data that remain on users’ devices. Added value of this study: To our knowledge, this is the first such framework. It estimates key quantities required for understanding transmission dynamics, including how an infected individual’s infectiousness changes over time relative to their symptom onset date, how much close contacts increase the risk of transmission, and the SI distribution. The framework learns an exposure hazard function from data that never leave users’ devices, then attributes infections to likely infectors, and reconstructs the SI distribution. We applied the framework to synthetic exposure-history data from epidemics simulated on dynamic contact networks of 166,034 individuals, matching the 2024 population of Oxford, UK. It produced estimates comparable to those from centralised analysis, identified the timing of peak host infectiousness relative to symptom onset to within one day, and reconstructed SI distributions that closely tracked the ground truth. We also systematically assessed how participation levels (app adoption and case ascertainment), technical limitations (incomplete exposure logging), and surveillance (knowledge of the background hazard from unobserved exposures) affected inference, and showed that daily population-level knowledge of the background hazard was sufficient for reliable inference. Implications of all the available evidence: Beyond their role as a control measure, DCT apps could provide timely, population-representative estimates of key transmission parameters, such as the host infectiousness profile and SI distribution. Our findings show that such inference is feasible within the privacy-preserving architectures likely to underpin future DCT systems. Greater app adoption, case ascertainment, and exposure logging would improve inference accuracy. To realise this potential, the framework will need to be embedded in DCT systems, supported by concurrent epidemiological research, real-world validation, and early collaboration with public health authorities and platform providers.

The Coalition for Epidemic Preparedness Innovations’ (CEPI) 100 Days Mission, and broader global pandemic preparedness efforts, require overcoming challenges in evaluating vaccine efficacy during emerging epidemics and outbreaks. Key challenges, addressed in a workshop jointly hosted by CEPI and the University of Oxford’s Pandemic Sciences Institute, arise from the sporadic and heterogeneous nature of outbreaks, high fatality rates that may preclude traditional placebo-controlled trials and biases from small or real-world studies. Here, we review recent developments, potential solutions and areas for innovation emphasized in the workshop to address these challenges. These opportunities for research and methodological development encompass four main areas: serology, exposure measurements, statistical modelling and trial design.

Recent decades have seen the rising establishment of public engagement professionals supporting communication and engagement in the contexts of science and health research. This article explores how public engagement professionals consider the ethical dimensions of their work. Based on analysis of 17 interviews with science communication and public engagement specialists at UK academic institutions and in practice settings, we examine the ways in which they frame ethical issues associated with engagement, as well as how they frame both publics and researchers. In doing so, we explore two tensions that together highlight how the rising popularity of engagement methods, including engaged research, can increase awareness of the need to ethically scrutinise engagement and associated practices. We also argue that public engagement professionals can further contribute to and advance academic discussion around engagement, arguing that ongoing research with practitioners is beneficial for academic understanding of communication, engagement and engaged research.

Background Older women’s contributions to their families, societies, and economies—including as caregivers for grandchildren—have remained largely invisible within a dominant public discourse that tends to portray older people as societal burdens. Methods This article explores the phenomenon of skip-generation caregiving in rural Cambodia, where grandparents become caregivers to their grandchildren as a consequence of long-term labour migration of the parental generation. We use an intersectional lens to situate the health concerns of older populations within broader interlocking systems of power and wider, converging, and dynamic social structures. Results Through an in-depth intersectional analysis of a case study of a Cambodian grandmother caregiver we illustrate how interlocking systems of power (ageism, ableism, sexism, and classism), converge with health policy to structure the life chances of a multiply marginalised older woman. The analysis exposes the limits of current global health policy and infrastructure to adequately address the needs of older women who are multiply disadvantaged by the intersection of their unique social locations. Conclusion Considering the cumulation of structural disadvantages across the life course, policies and interventions are needed that support older women to fulfil their pivotal caregiving roles without self-neglect. An intersectional understanding of older women’s lived realities can help to advance policies and interventions aimed at alleviating these existing intergenerational and gender-based inequalities.

The increasing integration of genomic medicine into routine medical care brings to light issues of complexity and uncertainty in the interpretation of genetic variation. We describe a fictional case study based on our experiences in the haematological malignancy setting to illustrate the complexities in using genomic testing to evaluate the suitability of relatives to act as stem cell donors. In particular, we demonstrate the ethical issues arising following the identification of a germline variant of uncertain significance (VUS) in a gene associated with inherited cancer susceptibility in a potential recipient. We discuss the factors that need to be considered, such as how this might influence donor selection, the potential for donor-derived leukaemia and the issues of offering testing to other family members. Popular discourse suggests genomics can offer clarity and transformation in healthcare. This case highlights the ethically complex nature of decision-making in genetic medicine due to the familial aspects of genomic information, the uncertain nature of many genomic results and how changes in the use of genomic information in clinical practice can call for urgent decision-making. This highlights the need for increased interdisciplinary working which takes into account the factors driving decisions for each specialty and recommends access to independent ethics support for members of the multidisciplinary team.

Healthcare professionals routinely navigate complex value conflicts that span personal, interpersonal, and organisational domains. This paper examines the concept of moral craftsmanship—the skilled practice of understanding, analysing, and working through value conflicts in healthcare settings—and argues that value pluralism provides a more realistic framework for healthcare ethics than approaches seeking overarching moral consensus. Through analysis of cases spanning clinical genetics, paediatric end-of-life care, and institutional resource allocation, the paper explores how value conflicts manifest across interconnected domains and explores the practical reasoning processes through which healthcare professionals successfully navigate seemingly intractable moral disagreements. Drawing on examples from clinical genetics counselling and recent analyses of dissensus in paediatric care, the paper argues that deep value pluralism is compatible with reasoned decision-making and that moral craftsmanship represents an essential skill for effective healthcare practice. Oversimplified ethical frameworks risk creating dangerous gaps between institutional processes and lived moral experience, potentially undermining public trust in healthcare systems. Healthcare institutions must develop approaches that acknowledge genuine value plurality while supporting practical decision-making, maintaining mechanisms for incorporating diverse public values, and addressing the moral residue that persists beyond immediate decisions.

Background Contemporary societies increasingly face deep, persistent moral conflicts that resist resolution through conventional dialogue and democratic processes. While the conceptual features of moral disagreement have been extensively analysed in philosophy, there has been inadequate attention to the full complexity of moral disagreements as social and cultural phenomena. This paper introduces and analyses the concept of "radical moral disagreement" (RMD) as a phenomenon requiring urgent scholarly attention. Methods We conducted a conceptual analysis drawing on examples from contemporary moral conflicts including abortion debates, climate action, and pandemic responses. We identified and analysed seven defining characteristics of RMDs through theoretical examination and comparison with moral disagreements of other kinds. We developed a preliminary conceptual framework and outlined research questions spanning philosophy, history, sociology, psychology, and media studies. Results We identified seven key characteristics that distinguish RMDs from ordinary moral disagreements: collision of incompatible values requiring resolution e.g. in policy decisions, intransigence even under ideal conditions for resolution, emotional intensity that precludes compromise, progressive polarisation of positions, censure that limits public discourse, widespread social disruption, and contested epistemologies regarding evidence and expertise. These characteristics are primarily scalar in nature, suggesting RMDs exist along a continuum rather than as categorical states. We demonstrated that RMDs can arise around a wide range of issues when they become entangled with identity, power, and risk within specific social contexts. Conclusions RMDs represent complex moral and social phenomena that, while potentially destabilising social cohesion and democratic governance, may also catalyse moral progress when properly understood and navigated. The paper describes a theoretical foundation for studying RMDs and outlines relevant research questions requiring attention, spanning philosophy, history, sociology, psychology, and media studies. We propose that understanding RMDs is essential for developing effective approaches to coexistence in pluralistic yet polarised societies.

Comprehensive collections of cancer data, including genomic data, are needed to improve cancer risk prediction and treatments. A recent government review, Better, Broader, Safer: Using health data for research and analysis, has argued for high-quality Patient and Public Involvement and Engagement (PPIE) for ethical data use. In this paper we determine a role and justification for PPIE to govern uses of genomic data in fields like cancer. First, we analyse two public attitudes studies about the role of PPIE in genomics governance. Second, we characterise two ethically-significant features of the context of governing genomic data: 1) data aggregation leading to novel group formation, and 2) the hybrid territory of genomic cancer data uses. Thirdly, we bring together these aspects to describe a fully determined role for PPIE within an approach to governing cancer genomic data, which is tailored to major areas of ethical consideration. Our account is a novel interpretation of what PPIE is for in governance, how it may foster public support and how its success in so doing depends on it being tailored to context.