Martin Zach

The Ethics Session of the International Primary Immunodeficiency Congress (IPIC), held in November 2025 and organised by the International Patient Organisation for Primary Immunodeficiencies (IPOPI), examined one of the most challenging developments emerging from genomic-enhanced newborn screening: the identification of serious, non-treatable disorders as incidental detection in programmes originally designed to detect severe combined immunodeficiency (SCID). Using a fictionalised clinical scenario based on the recent literature, the session explored the early diagnosis of ataxia–telangiectasia (AT) detected through TREC-based screening. The discussion highlighted the clinical value and psychological risks associated with presymptomatic detection, the persistent shortcomings in parental consent processes, the systemic pressures created by expanding genomic testing, and the ethical challenges surrounding the reporting and management of incidental findings in screening. The debate underscored the need for internationally coordinated frameworks to guide the management of incidental detection in newborn screening as genomic technologies become more deeply embedded in routine public health practice.

Shaped by advances in scientific instrumentation and experimental techniques, the concept of antibody has undergone profound transformations throughout the history of immunology. Serological assays, separation techniques, protein fragmentation techniques, molecular biology techniques, and other methodological innovations did not only serve to produce data on the structure and function of these molecules but, by framing antibodies into a unique facet of experimental investigation, were effectively redefining and reconceptualizing these molecules for the scientific community. The characteristics and properties of antibodies observed in experimental settings were often directly extrapolated to their presumed nature in living organisms, as exemplified by the literal identification of antibodies with a gamma electrophoretic fraction in the 1930s. Stemming from parallel advances in related fields such as molecular biology and biochemistry, the introduction of novel techniques was driving shifts in the field of immunology, establishing novel frameworks of theoretical conceptualization and understanding. Technological innovation in experimental techniques continues to shape our view of these molecules, driving progress in both basic immunology and therapeutic applications.

Scientific knowledge relies heavily on models, shaped by simplifying assumptions, with common categories being abstraction and idealization. This article aims to expose conceptual challenges inherent in conventional interpretations of these concepts, particularly in their practical application to scientific modeling. The primary hurdle emerges in applying these categories to real­world instances of scientific modeling, which we illustrate with examples of non­causal explanations. Key issues revolve around (i) the ambiguous distinction between abstraction and idealization and (ii) the application of the simplifying assumption of abstraction. Our hypothesis posits that non­causal explanations face unintelligibility due to an unclear understanding of the role of simplifying assumptions in them. To test this, we analyze selected examples, ranging from (toy­)examples to real­world instances, scrutinizing the alignment with the standard notions of abstraction and idealization. Throughout, we investigate the influence of simplifying assumptions on these explanations, assessing their adherence or deviation from conventional concepts.

Many microbes responsible for infectious diseases are known to run an asymptomatic course in a significant portion of the population. By highlighting the conceptual complexities of host-microbe interactions, this paper elucidates the fact that while many infections remain asymptomatic, this does not necessarily mean that such infections are of no concern for health. The paper builds on the so-called damage-response framework and considers several developments required to gain a more comprehensive perspective on infections and their relationship to diseases. Irrespective of their (short-term) clinical manifestation, infections leave an imprint with consequences for health. Finally, these considerations regarding host-microbe interactions must be incorporated into policy decisions and public understanding of health if we hope to handle future pandemics such as Covid-19 better.

In this paper we address the issue of how to think about immunity. Many immunological writings suggest a straightforward option: the view that the immune system is primarily a system of defense, which naturally invites the talk of strong immunity and strong immune response. Despite their undisputable positive role in immunology, such metaphors can also pose a risk of establishing a narrow perspective, omitting from consideration phenomena that do not neatly fit those powerful metaphors. Building on this analysis, we argue two things. First, we argue that the immune system is involved not only in defense. Second, by disentangling various possible meanings of ‘strength’ and ‘weakness’ in immunology, we also argue that such a construal of immunity generally contributes to the distortion of the overall picture of what the immune system is, what it does, and why it sometimes fails. Instead, we propose to understand the nature of the immune system in terms of contextuality, regulation, and trade-offs. We suggest that our approach provides lessons for a general understanding of the organizing principles of the immune system in health and disease. For all this to work, we discuss a wide range of immunological phenomena.

Agent-based models (ABMs) are one of the main sources of evidence for decisions regarding mitigation and suppression measures against the spread of SARS-CoV-2. These models have not been previously included in the hierarchy of evidence put forth by the evidence-based medicine movement, which prioritizes those research methods that deliver results less susceptible to the risk of confounding. We point out the need to assess the quality of evidence delivered by ABMs and ask the question of what is the risk that assumptions entertained in ABMs do not include all the key factors and make model predictions susceptible to the problem of confounding.

Background: Our purpose is to assess epidemiological agent‐based models—or ABMs—of the SARS‐CoV‐2 pandemic methodologically. The rapid spread of the outbreak requires fast‐paced decision‐making regarding mitigation measures. However, the evidence for the efficacy of non‐pharmaceutical interventions such as imposed social distancing and school or workplace closures is scarce: few observational studies use quasi‐experimental research designs, and conducting randomized controlled trials seems infeasible. Additionally, evidence from the previous coronavirus outbreaks of SARS and MERS lacks external validity, given the significant differences in contagiousness of those pathogens relative to SARS‐CoV‐2. To address the pressing policy questions that have emerged as a result of COVID‐19, epidemiologists have produced numerous models that range from simple compartmental models to highly advanced agent‐based models. These models have been criticized for involving simplifications and lacking empirical support for their assumptions.Methods: To address these voices and methodologically appraise epidemiological ABMs, we consider AceMod (the model of the COVID‐19 epidemic in Australia) as a case study of the modelling practice.Results: Our example shows that, although epidemiological ABMs involve simplifications of various sorts, the key characteristics of social interactions and the spread of SARS‐CoV‐2 are represented sufficiently accurately. This is the case because these modellers treat empirical results as inputs for constructing modelling assumptions and rules that the agents follow; and they use calibration to assert the adequacy to benchmark variables.Conclusions: Given this, we claim that the best epidemiological ABMs are models of actual mechanisms and deliver both mechanistic and difference‐making evidence. Consequently, they may also adequately describe the effects of possible interventions. Finally, we discuss the limitations of ABMs and put forward policy recommendations.