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Background Differences in the genetic material of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants may result in altered virulence characteristics. Assessing the disease severity caused by newly emerging variants is essential to estimate their impact on public health. However, causally inferring the intrinsic severity of infection with variants using observational data is a challenging process on which guidance is still limited. We describe potential limitations and biases that researchers are confronted with and evaluate different methodological approaches to study the severity of infection with SARS-CoV-2 variants. Methods We reviewed the literature to identify limitations and potential biases in methods used to study the severity of infection with a particular variant. The impact of different methodological choices is illustrated by using real-world data of Belgian hospitalized COVID-19 patients. Results We observed different ways of defining coronavirus disease 2019 (COVID-19) disease severity (e.g., admission to the hospital or intensive care unit versus the occurrence of severe complications or death) and exposure to a variant (e.g., linkage of the sequencing or genotyping result with the patient data through a unique identifier versus categorization of patients based on time periods). Different potential selection biases (e.g., overcontrol bias, endogenous selection bias, sample truncation bias) and factors fluctuating over time (e.g., medical expertise and therapeutic strategies, vaccination coverage and natural immunity, pressure on the healthcare system, affected population groups) according to the successive waves of COVID-19, dominated by different variants, were identified. Using data of Belgian hospitalized COVID-19 patients, we were able to document (i) the robustness of the analyses when using different variant exposure ascertainment methods, (ii) indications of the presence of selection bias and (iii) how important confounding variables are fluctuating over time. Conclusions When estimating the unbiased marginal effect of SARS-CoV-2 variants on the severity of infection, different strategies can be used and different assumptions can be made, potentially leading to different conclusions. We propose four best practices to identify and reduce potential bias introduced by the study design, the data analysis approach, and the features of the underlying surveillance strategies and data infrastructure.

Precision medicine, also known as personalized medicine, enables the provision of tailored health services to patients. In the prevention, early detection, and treatment of cancers, precision medicine is highly promising, given the increasing use of genomic profiling for diagnosis and adapting therapies in several tumor types. Artificial Intelligence (AI) can support this process by analyzing vast amounts of relevant data. However, high-quality data and financial investments in the health system are essential for the implementation of precision medicine and AI solutions in routine cancer care. Building on the quantitative outcomes of a foresight exercise published in another study, this article collects qualitative data to gain more detailed insights into the future of precision oncology in Belgium and discusses the role of AI in this field. It reports the results of a series of expert workshops, focusing on four hypothetical future scenarios that are centered around technological and economic issues that must be overcome for the widespread use of precision oncology in Belgium. The study concludes that all four scenarios discussed in the workshops would require supportive policy measures in Belgium, which should go beyond mere technological and economic considerations, such as involving patient associations and the public in policy design or creating multi-disciplinary expert groups for precision medicine. To the best of our knowledge, this is the first study to employ foresight methodology to illustrate possible future scenarios, scrutinize feasible approaches for implementing precision oncology in Belgium, and discuss the use of AI in this context.

Background Precision medicine represents a paradigm shift in health systems, moving from a one-size-fits-all approach to a more individualized form of care, spanning multiple scientific disciplines including drug discovery, genomics, and health communication. This study aims to explore the contextual factors influencing the equitable implementation of precision medicine in Belgium for incorporating precision medicine into routine cancer care within the Belgian health system. Methods As part of a foresight study, our approach evaluates critical factors affecting the implementation of precision oncology. The study scrutinizes contextual, i.e. demographic, economic, societal, technological, environmental, and political/policy-related (DESTEP) factors, identified through a comprehensive literature review and validated by a multidisciplinary group at the Belgian Cancer Center, Sciensano. An expert survey further assesses the importance and likelihood of these factors, illuminating potential barriers and facilitators to implementation. Results Based on the expert survey, five key elements (rising cancer rates, dedicated healthcare reimbursement budgets, increasing healthcare expenditures, advanced information technology solutions for data transfer, and demand for high-quality data) are expected to influence the equitable implementation of precision medicine in routine cancer care in Belgium in the future. Conclusions This work contributes to the knowledge base on precision medicine in Belgium and public health foresight, exploring the implementation challenges and suggesting solutions with an emphasis on the importance of comparative analyses of health systems, evaluation of health technology assessment methods, and the exploration of ethical issues in data privacy and equity.

Acute viral infections pose many practical challenges for the accurate assessment of the impact of novel therapies on viral growth and decay. Using the example of influenza A, we illustrate how the measurement of infection-related quantities that determine the dynamics of viral load within the human host, can inform investigators on the course and severity of infection and the efficacy of a novel treatment. We estimated the values of key infection-related quantities that determine the course of natural infection from viral load data, using Markov Chain Monte Carlo methods. The data were placebo group viral load measurements collected during volunteer challenge studies, conducted by Roche, as part of the oseltamivir trials. We calculated the values of the quantities for each patient and the correlations between the quantities, symptom severity and body temperature. The greatest variation among individuals occurred in the viral load peak and area under the viral load curve. Total symptom severity correlated positively with the basic reproductive number. The most sensitive endpoint for therapeutic trials with the goal to cure patients is the duration of infection. We suggest laboratory experiments to obtain more precise estimates of virological quantities that can supplement clinical endpoint measurements.

Linking genomic data with population-level observational data sources offers a powerful approach to advance public health genomics research, providing a more comprehensive view of health outcomes by incorporating information on various health determinants. However, integrating data from scattered sources poses significant challenges. Genomic data, with its unique identifying properties and ethical concerns, is particularly sensitive and requires strict security measures and transparent participant communication to ensure confidentiality and maintain public trust. In Belgium, a pilot study has been set up to evaluate genetic and non-genetic health determinants associated with cancer. As a crucial step towards this scientific objective, the study also aims to assess the feasibility and complexity of linking genomics data with national population-based datasets. The process, ranging from conceptualisation and data discovery to securing approvals and finalising agreements, took two years. Each phase of this process offers opportunities to improve efficiency, enhance coordination between stakeholders, and address legal and ethical challenges. Establishing comprehensive, interoperable data catalogues can facilitate data discovery, while standardising data access requests can simplify processes. Pre-established partnerships or agreements can reduce administrative burdens and consequently, improve the timeliness of the research. Additionally, planning for sustainability in advance and rethinking consent procedures could reduce ad hoc approval procedures and support structural solutions for secondary use and linkage of health data in the public interest. This paper highlights practical challenges and considerations relevant to data linkage studies in general, offering insights for researchers conducting integrated public health genomics research.

Abstract The European Health Data Space (EHDS) will help researchers use health data across EU Member States (MS). Currently, cross-border research faces heterogeneous data access processes. Using a real-world use case, this paper analyses challenges and opportunities brought by the upcoming implementation of the EHDS, assessing the situation before and after the regulation comes into force. The use case focused on metastatic colorectal cancer, analysing the relations between mutational signatures and clinical trajectories while addressing data access procedures across MS. The regulatory landscape and the challenges that need to be addressed for the EHDS to enable the secondary use of health data, particularly genomic data, are complex and heterogeneous across MS. We describe the pathway from data application to access to pseudonymized data in secure processing environments, emphasizing the legal requirements, including the role of ethics committees. Finally, we analyse the success factors for achieving access to the data and the reasons for access denial to support shaping the upcoming EHDS implementation. Several challenges remain unaddressed for cross-border data use, especially in the context of genomic data, where the complexity and heterogeneity of informed consent can impact or even impede data-sharing efforts. While EHDS can simplify processes across MS, it is crucial to ensure that additional safeguards do not negatively impact or block access to health data and that EHDS infrastructure is ready for effective and affordable processing of large volumes of genomic and other data.

The electronic structure of DNA is determined by its nucleotide sequence, which is for instance exploited in molecular electronics. Here we demonstrate that also the DNA strand breakage induced by low-energy electrons (18 eV) depends on the nucleotide sequence. To determine the absolute cross sections for electron induced single strand breaks in specific 13 mer oligonucleotides we used atomic force microscopy analysis of DNA origami based DNA nanoarrays. We investigated the DNA sequences 5′-TT(XYX) 3 TT with X = A, G, C and Y = T, BrU 5-bromouracil and found absolute strand break cross sections between 2.66 · 10 −14  cm 2 and 7.06 · 10 −14  cm 2 . The highest cross section was found for 5′-TT(ATA) 3 TT and 5′-TT(ABrUA) 3 TT, respectively. BrU is a radiosensitizer, which was discussed to be used in cancer radiation therapy. The replacement of T by BrU into the investigated DNA sequences leads to a slight increase of the absolute strand break cross sections resulting in sequence-dependent enhancement factors between 1.14 and 1.66. Nevertheless, the variation of strand break cross sections due to the specific nucleotide sequence is considerably higher. Thus, the present results suggest the development of targeted radiosensitizers for cancer radiation therapy.

Artificial Intelligence (AI) is already a reality in health systems, bringing benefits to patients, healthcare providers, and other stakeholders in the health care. To further leverage AI in health, Belgium is advised to make policy-level decisions about how to fund, design and undertake actions focussing on data access and inclusion, IT-infrastructure, legal and ethical frameworks, public and professional trust, in addition to education and interpretation. EU initiatives, such as European Health data space (EHDS), the Genomics Data Infrastructure (GDI) and the EU Cancer Imaging Infrastructure (EUCAIM) are building EU data infrastructures. To continue these positive developments, Belgium should continue to invest and support existing European data infrastructures. At the national level, a clear vision and strategy need to be developed and infrastructures need to be harmonized at the European level.

About 90% of drugs fail in clinical development. The question is whether trials fail because of insufficient efficacy of the new treatment, or rather because of poor trial design that is unable to detect the true efficacy. The variance of the measured endpoints is a major, largely underestimated source of uncertainty in clinical trial design, particularly in acute viral infections. We use a clinical trial simulator to demonstrate how a thorough consideration of the variability inherent in clinical trials of novel therapies for acute viral infections can improve trial design. We developed a clinical trial simulator to analyse the impact of three different types of variation on the outcome of a challenge study of influenza treatments for infected patients, including individual patient variability in the response to the drug, the variance of the measurement procedure, and the variance of the lower limit of quantification of endpoint measurements. In addition, we investigated the impact of protocol variation on clinical trial outcome. We found that the greatest source of variance was inter-individual variability in the natural course of infection. Running a larger phase II study can save up to $38 million, if an unlikely to succeed phase III trial is avoided. In addition, low-sensitivity viral load assays can lead to falsely negative trial outcomes. Due to high inter-individual variability in natural infection, the most important variable in clinical trial design for challenge studies of potential novel influenza treatments is the number of participants. 100 participants are preferable over 50. Using more sensitive viral load assays increases the probability of a positive trial outcome, but may in some circumstances lead to false positive outcomes. Clinical trial simulations are powerful tools to identify the most important sources of variance in clinical trials and thereby help improve trial design.

We investigated effectiveness of (1) mRNA booster vaccination versus primary vaccination only and (2) heterologous (viral vector–mRNA) versus homologous (mRNA–mRNA) prime-boost vaccination against severe outcomes of BA.1, BA.2, BA.4 or BA.5 Omicron infection (confirmed by whole genome sequencing) among hospitalized COVID-19 patients using observational data from national COVID-19 registries. In addition, it was investigated whether the difference between the heterologous and homologous prime-boost vaccination was homogenous across Omicron sub-lineages. Regression standardization (parametric g-formula) was used to estimate counterfactual risks for severe COVID-19 (combination of severity indicators), intensive care unit (ICU) admission, and in-hospital mortality under exposure to different vaccination schedules. The estimated risk for severe COVID-19 and in-hospital mortality was significantly lower with an mRNA booster vaccination as compared to only a primary vaccination schedule (RR = 0.59 [0.33; 0.85] and RR = 0.47 [0.15; 0.79], respectively). No significance difference was observed in the estimated risk for severe COVID-19, ICU admission and in-hospital mortality with a heterologous compared to a homologous prime-boost vaccination schedule, and this difference was not significantly modified by the Omicron sub-lineage. Our results support evidence that mRNA booster vaccination reduced the risk of severe COVID-19 disease during the Omicron-predominant period.