Paper by Barbara Tornimbene et al: “The COVID-19 pandemic highlighted substantial obstacles in real-time data generation and management needed for clinical research and epidemiological analysis. Three years after the pandemic, reflection on the difficulties of data integration offers potential to improve emergency preparedness. The fourth session of the WHO Pandemic and Epidemic Intelligence Forum sought to report the experiences of key global institutions in data integration and synthesis, with the aim of identifying solutions for effective integration. Data integration, defined as the combination of heterogeneous sources into a cohesive system, allows for combining epidemiological data with contextual elements such as socioeconomic determinants to create a more complete picture of disease patterns. The approach is critical for predicting outbreaks, determining disease burden, and evaluating interventions. The use of contextual information improves real-time intelligence and risk assessments, allowing for faster outbreak responses. This report captures the growing acknowledgment of data integration importance in boosting public health intelligence and readiness and show examples of how global institutions are strengthening initiatives to respond to this need. However, obstacles persist, including interoperability, data standardization, and ethical considerations. The success of future data integration efforts will be determined by the development of a common technical and legal framework, the promotion of global collaboration, and the protection of sensitive data. Ultimately, effective data integration can potentially transform public health intelligence and our way to successfully respond to future pandemics…(More)”.
Practitioner perspectives on informing decisions in One Health sectors with predictive models
Paper by Kim M. Pepin: “Every decision a person makes is based on a model. A model is an idea about how a process works based on previous experience, observation, or other data. Models may not be explicit or stated (Johnson-Laird, 2010), but they serve to simplify a complex world. Models vary dramatically from conceptual (idea) to statistical (mathematical expression relating observed data to an assumed process and/or other data) or analytical/computational (quantitative algorithm describing a process). Predictive models of complex systems describe an understanding of how systems work, often in mathematical or statistical terms, using data, knowledge, and/or expert opinion. They provide means for predicting outcomes of interest, studying different management decision impacts, and quantifying decision risk and uncertainty (Berger et al. 2021; Li et al. 2017). They can help decision-makers assimilate how multiple pieces of information determine an outcome of interest about a complex system (Berger et al. 2021; Hemming et al. 2022).
People rely daily on system-level models to reach objectives. Choosing the fastest route to a destination is one example. Such a decision may be based on either a mental model of the road system developed from previous experience or a traffic prediction mapping application based on mathematical algorithms and current data. Either way, a system-level model has been applied and there is some uncertainty. In contrast, predicting outcomes for new and complex phenomena, such as emerging disease spread, a biological invasion risk (Chen et al. 2023; Elderd et al. 2006; Pepin et al. 2022), or climatic impacts on ecosystems is more uncertain. Here public service decision-makers may turn to mathematical models when expert opinion and experience do not resolve enough uncertainty about decision outcomes. But using models to guide decisions also relies on expert opinion and experience. Also, even technical experts need to make modeling choices regarding model structure and data inputs that have uncertainty (Elderd et al. 2006) and these might not be completely objective decisions (Bedson et al. 2021). Thus, using models for guiding decisions has subjectivity from both the developer and end-user, which can lead to apprehension or lack of trust about using models to inform decisions.
Models may be particularly advantageous to decision-making in One Health sectors, including health of humans, agriculture, wildlife, and the environment (hereafter called One Health sectors) and their interconnectedness (Adisasmito et al. 2022)…(More)”.
National engagement on public trust in data use for single patient record and GP health record published
HTN Article: “A large-scale public engagement report commissioned by NHSE on building and maintaining public trust in data use across health and care has been published, focusing on the approach to creating a single patient record and the secondary use of GP data.
It noted “relief” and “enthusiasm” from participants around not having to repeat their health history when interacting with different parts of the health and care system, and highlighted concerns about data accuracy, privacy, and security.
120 participants were recruited for tier one, with 98 remaining by the end, for 15 hours of deliberation over three days in locations including Liverpool, Leicester, Portsmouth, and South London. Inclusive engagement for tier two recruited 76 people from “seldom heard groups” such as those with health needs or socially marginalised groups for interviews and small group sessions. A nationally representative ten-minute online survey with 2,000 people was also carried out in tier three.
“To start with, the concept of a single patient record was met with relief and enthusiasm across Tier 1 and Tier 2 participants,” according to the report….
When it comes to GP data, participants were “largely unaware” of secondary uses, but initially expressed comfort in the idea of it being used for saving lives, improving care, prevention, and efficiency in delivery of services. Concerns were broadly similar to those about the single patient record: concerns about data breaches, incorrect data, misuse, sensitivity of data being shared, bias against individuals, and the potential for re-identification. Some participants felt GP data should be treated differently because “it is likely to contain more intimate information”, offering greater risk to the individual patient if data were to be misused. Others felt it should be included alongside secondary care data to ensure a “comprehensive dataset”.
Participants were “reassured” overall by safeguards in place such as de-identification, staff training in data handling and security, and data regulation such as GDPR and the Data Protection Act. “There was a widespread feeling among Tier 1 and Tier 2 participants that the current model of the GP being the data controller for both direct care and secondary uses placed too much of a burden on GPs when it came to how data is used for secondary purposes,” findings show. “They wanted to see a new model which would allow for greater consistency of approach, transparency, and accountability.” Tier one participants suggested this could be a move to national or regional decision-making on secondary use. Tier three participants who only engaged with the topic online were “more resistant” to moving away from GPs as sole data controllers, with the report stating: “This greater reluctance to change demonstrates the need for careful communication with the public about this topic as changes are made, and continued involvement of the public.”..(More)”.
Understanding the Impacts of Generative AI Use on Children
Primer by The Alan Turing Institute and LEGO Foundation: “There is a growing body of research looking at the potential positive and negative impacts of generative AI and its associated risks. However, there is a lack of research that considers the potential impacts of these technologies on children, even though generative AI is already being deployed within many products and systems that children engage with, from games to educational platforms. Children have particular needs and rights that must be accounted for when designing, developing, and rolling out new technologies, and more focus on children’s rights is needed. While children are the group that may be most impacted by the widespread deployment of generative AI, they are simultaneously the group least represented in decision-making processes relating to the design, development, deployment or governance of AI. The Alan Turing Institute’s Children and AI and AI for Public Services teams explored the perspectives of children, parents, carers and teachers on generative AI technologies. Their research is guided by the ‘Responsible Innovation in Technology for Children’ (RITEC) framework for digital technology, play and children’s wellbeing established by UNICEF and funded by the LEGO Foundation and seeks to examine the potential impacts of generative AI on children’s wellbeing. The utility of the RITEC framework is that it allows for the qualitative analysis of wellbeing to take place by foregrounding more specific factors such as identity and creativity, which are further explored in each of the work packages.
The project provides unique and much needed insights into impacts of generative AI on children through combining quantitative and qualitative research methods…(More)”.
Comparative evaluation of behavioral epidemic models using COVID-19 data
Paper by Nicolò Gozzi, Nicola Perra, and Alessandro Vespignani: “Characterizing the feedback linking human behavior and the transmission of infectious diseases (i.e., behavioral changes) remains a significant challenge in computational and mathematical epidemiology. Existing behavioral epidemic models often lack real-world data calibration and cross-model performance evaluation in both retrospective analysis and forecasting. In this study, we systematically compare the performance of three mechanistic behavioral epidemic models across nine geographies and two modeling tasks during the first wave of COVID-19, using various metrics. The first model, a Data-Driven Behavioral Feedback Model, incorporates behavioral changes by leveraging mobility data to capture variations in contact patterns. The second and third models are Analytical Behavioral Feedback Models, which simulate the feedback loop either through the explicit representation of different behavioral compartments within the population or by utilizing an effective nonlinear force of infection. Our results do not identify a single best model overall, as performance varies based on factors such as data availability, data quality, and the choice of performance metrics. While the Data-Driven Behavioral Feedback Model incorporates substantial real-time behavioral information, the Analytical Compartmental Behavioral Feedback Model often demonstrates superior or equivalent performance in both retrospective fitting and out-of-sample forecasts. Overall, our work offers guidance for future approaches and methodologies to better integrate behavioral changes into the modeling and projection of epidemic dynamics…(More)”.
Sentinel Cities for Public Health
Article by Jesse Rothman, Paromita Hore & Andrew McCartor: “In 2017, a New York City health inspector visited the home of a 5-year-old child with an elevated blood lead level. With no sign of lead paint—the usual suspect in such cases—the inspector discovered dangerous levels of lead in a bright yellow container of “Georgian Saffron,” a spice obtained in the family’s home country. It was not the first case associated with the use of lead-containing Georgian spices—the NYC Health Department shared their findings with authorities in Georgia, which catalyzed a survey of children’s blood lead levels in Georgia, and led to increased regulatory enforcement and education. Significant declines in spice lead levels in the country have had ripple effects in NYC also: not only a drop in spice samples from Georgia containing detectable lead but also a significant reduction in blood lead levels among NYC children of Georgian ancestry.
This wasn’t a lucky break—it was the result of a systematic approach to transform local detection into global impact. Findings from local NYC surveillance are, of course, not limited to Georgian spices. Surveillance activities have identified a variety of lead-containing consumer products from around the world, from cosmetics and medicines to ceramics and other goods. Routinely surveying local stores for lead-containing products has resulted in the removal of over 30,000 hazardous consumer products from NYC store shelves since 2010.
How can we replicate and scale up NYC’s model to address the global crisis of lead poisoning?…(More)”.
Facilitating the secondary use of health data for public interest purposes across borders
OECD Paper: “Recent technological developments create significant opportunities to process health data in the public interest. However, the growing fragmentation of frameworks applied to data has become a structural impediment to fully leverage these opportunities. Public and private stakeholders suggest that three key areas should be analysed to support this outcome, namely: the convergence of governance frameworks applicable to health data use in the public interest across jurisdictions; the harmonisation of national procedures applicable to secondary health data use; and the public perceptions around the use of health data. This paper explores each of these three key areas and concludes with an overview of collective findings relating specifically to the convergence of legal bases for secondary data use…(More)”.
Integrating Data Governance and Mental Health Equity: Insights from ‘Towards a Set of Universal Data Principles’
Article by Cindy Hansen: “This recent scholarly work, “Towards a Set of Universal Data Principles” by Steve MacFeely et al (2025), delves comprehensively into the expansive landscape of data management and governance. It is noteworthy to acknowledge the intricate processes through which humans collect, manage, and disseminate vast quantities of data. …To truly democratize digital mental healthcare, it’s crucial to empower individuals in their data journey. By focusing on Digital Self-Determination, people can participate in a transformative shift where control over personal data becomes a fundamental right, aligning with the proposed universal data principles. One can envision a world where mental health data, collected and used responsibly, contributes not only to personal well-being but also to the greater public good, echoing the need for data governance to serve society at large.
This concept of digital self-determination empowers individuals by ensuring they have the autonomy to decide who accesses their mental health data and how it’s utilized. Such empowerment is especially significant in the context of mental health, where data sensitivity is high, and privacy is paramount. Giving people the confidence to manage their data fosters trust and encourages them to engage more openly with digital health services, promoting a culture of trust which is a core element of the proposed data governance frameworks.
Holistic Research Canada’s Outcome Monitoring System honors this ethos, allowing individuals to control how their data is accessed, shared, and used while maintaining engagement with healthcare providers. With this system, people can actively participate in their mental health decisions, supported by data that offers transparency about their progress and prognoses, which is crucial in realizing the potential of data to serve both individual and broader societal interests.
Furthermore, this tool provides actionable insights into mental health journeys, promoting evidence-based practices, enhancing transparency, and ensuring that individuals’ rights are safeguarded throughout. These principles are vital to transforming individuals from passive subjects into active stewards of their data, consistent with the proposed principles of safeguarding data quality, integrity, and security…(More)”.
‘We are flying blind’: RFK Jr.’s cuts halt data collection on abortion, cancer, HIV and more
Article by Alice Miranda Ollstein: “The federal teams that count public health problems are disappearing — putting efforts to solve those problems in jeopardy.
Health Secretary Robert F. Kennedy Jr.’s purge of tens of thousands of federal workers has halted efforts to collect data on everything from cancer rates in firefighters to mother-to-baby transmission of HIV and syphilis to outbreaks of drug-resistant gonorrhea to cases of carbon monoxide poisoning.
The cuts threaten to obscure the severity of pressing health threats and whether they’re getting better or worse, leaving officials clueless on how to respond. They could also make it difficult, if not impossible, to assess the impact of the administration’s spending and policies. Both outside experts and impacted employees argue the layoffs will cost the government more money in the long run by eliminating information on whether programs are effective or wasteful, and by allowing preventable problems to fester.
“Surveillance capabilities are crucial for identifying emerging health issues, directing resources efficiently, and evaluating the effectiveness of existing policies,” said Jerome Adams, who served as surgeon general in the first Trump’s administration. “Without robust data and surveillance systems, we cannot accurately assess whether we are truly making America healthier.”..(More)”.
The Future of Health Is Preventive — If We Get Data Governance Right
Article by Stefaan Verhulst: “After a long gestation period of three years, the European Health Data Space (EHDS) is now coming into effect across the European Union, potentially ushering in a new era of health data access, interoperability, and innovation. As this ambitious initiative enters the implementation phase, it brings with it the opportunity to fundamentally reshape how health systems across Europe operate. More generally, the EHDS contains important lessons (and some cautions) for the rest of the world, suggesting how a fragmented, reactive model of healthcare may transition to one that is more integrated, proactive, and prevention-oriented.
For too long, health systems–in the EU and around the world–have been built around treating diseases rather than preventing them. Now, we have an opportunity to change that paradigm. Data, and especially the advent of AI, give us the tools to predict and intervene before illness takes hold. Data offers the potential for a system that prioritizes prevention–one where individuals receive personalized guidance to stay healthy, policymakers access real-time evidence to address risks before they escalate, and epidemics are predicted weeks in advance, enabling proactive, rapid, and highly effective responses.
But to make AI-powered preventive health care a reality, and to make the EHDS a success, we need a new data governance approach, one that would include two key components:
- The ability to reuse data collected for other purposes (e.g., mobility, retail sales, workplace trends) to improve health outcomes.
- The ability to integrate different data sources–clinical records and electronic health records (EHRS), but also environmental, social, and economic data — to build a complete picture of health risks.
In what follows, we outline some critical aspects of this new governance framework, including responsible data access and reuse (so-called secondary use), moving beyond traditional consent models to a social license for reuse, data stewardship, and the need to prioritize high-impact applications. We conclude with some specific recommendations for the EHDS, built from the preceding general discussion about the role of AI and data in preventive health…(More)”.