Institut für Praxisforschung und Projektberatung

Abstract Epidemics are shaped by the interplay between host and pathogen population characteristics, which are themselves intertwined. In particular, host behavior and immunity profile shape pathogen population structure by affecting both the likelihood of new variant emergence and its subsequent dynamics. Theoretical studies provided a fragmented description of this complex dynamical dependency, and the empirical evidence is limited. The SARS-CoV-2 pandemic presents an unprecedented opportunity to study the emergence of new variants. The relative growth of emerging variants over the resident ones, i.e., the selection coefficient, showed spatiotemporal variations that could be associated with population immunity and the mean and dispersion of contacts, which varied greatly according to epidemic intensity and human response. We first investigated the impact of these three features on the selection coefficient using a stochastic network-based model of new variant emergence, which incorporates tunable connectivity and heterogeneity. Results systematically chart the parameter space, uncover the boundaries of previously known associations, and quantify their strength. The mean number of contacts was positively associated with the selection coefficient, the effect being more robust for low immune-escape variants. The impact of immunity diminished as immunity increased. Importantly, greater contact dispersion slowed down the spread of variants lacking immune escape, but this effect quickly reversed once immune escape became non-zero. We then analysed the emergence of the SARS-CoV-2 Alpha variant in the United States at the state level, examining the association of the selection coefficient with the three population features under study, reconstructed from serological, vaccination, and contact survey data. Regression analyses revealed a strong effect of population characteristics. Comparing empirical trends with model predictions showed consistency and suggested that the selection coefficient was more affected by contact statistics than by immunity. These results shed light on how human population structure mediates variant dynamics and help interpret the heterogeneity observed in variant emergence.

Abstract Background Assessing the controlled direct effect (CDE) of an exposure on a disease from observational data is challenging when the DAG is unknown. In such cases, causal discovery methods can be used to infer a partiallly oriented DAG from which potential adjustment sets for effect estimation can be deduced. How-ever, classical causal discovery approaches that attempt to reconstruct the entire graph often face important limitations, including strong assumptions, numerous statistical tests, and large sample size requirements, which are further exacerbated by missing data. To address these issues, we rely on a local causal discovery algorithm, which focuses on identifying only the relevant part of the graph. We apply this approach to cohort data from patients with viral hepatitis to estimate the CDE of diabetes on severe liver fibrosis. Methods We investigated the CDE of diabetes on liver fibrosis among patients infected with HBV or HCV using baseline data from the French ANRS CO22 HEPATHER cohort. We used a local causal discovery algorithm, called, LocalPC-CDE. This method focuses on learning only the part of the graph necessary to answer the target causal question, thereby avoiding some unrealistic assumptions, reducing the number of required tests, and automatically selecting the adjustment set for valid CDE estimation. To further enhance robustness, we incorporated a bootstrap augmentation of the algorithm to quantify the stability of each identified direct cause (variables of the adjustment set). Only the variables least affected by sampling fluctuations were retained in the final adjustment set. When the CDE was identifiable, it was estimated using logistic regression, yielding odds ratios (OR) with 95% confidence intervals (CI). Results A total of 20858 patients were included for causal discovery, and the estimation was performed on 8,130 complete-case observations. The set of direct causes of severe fibrosis (excluding diabetes) identified by the causal discovery algorithm comprised 9 variables: diabetes, geographical origin, age, type of hepatitis, total cholesterol, HDL cholesterol, past alcohol consumption, blood glucose, and sex, which constituted the adjustment set. The CDE of diabetes on severe fibrosis among patients with viral hepatitis was significantly positive, with an estimated odds ratio of 1.63 (95% CI [1.40, 1.90]). Conclusions Even after adjusting for confounding using a targeted, data-driven approach, diabetes still appears to have a direct and statistically significant effect on liver fibrosis in patients with chronic viral hepatitis. This suggests that managing diabetes may be a direct lever for mitigating fibrosis progression in patients with chronic viral hepatitis, not just an indirect one through other metabolic and lifestyle factors.

Summary Background Lyme borreliosis and tick-borne encephalitis (TBE) are tick-borne zoonoses of public health importance, transmitted in Western Europe by the same tick vector Ixodes ricinus . Roe deer ( Capreolus capreolus ) is a widespread feeding host for I. ricinus stages. Here, we explored the spatial extent of both pathogens in the wild in Northern France by assessing serological status in roe deer. Methods We collected 3,147 blood samples (2019-2022), and searched antibodies against both pathogens using ELISA tests. We fitted logistic models to investigate exposure to both or either pathogens, and explored spatial patterns using Kulldorf clustering scan tests. Results The overall ELISA IgG prevalence was 23.24% (95%CI [21.77-34.72]) for Borrelia and 8.64% (95%CI [7.45-9.86]) for TBEV. The median Borrelia seroprevalence varied from 4% to 42% between departments, with two high-risk clusters of exposure identified. TBEV seroprevalence did not show any spatial pattern, and positives by seroneutralisation tests were reported in eight departments. Discussion Our findings suggest the presence of both pathogens over large areas. Wider spatial sampling including areas at lower risk and improved serological methods would be beneficial to further validate those estimates.

The AWAKE project at CERN successfully demonstrated the use of a proton driver to accelerate an electron witness in plasma*. One of the key goals for AWAKE Run2 is to better control this acceleration, separating the proton-beam-modulation and electron-acceleration stages in order to achieve high energy electrons with high beam quality. Controlled acceleration additionally requires careful tuning of the witness bunch parameters at the injection point. In this work, we use particle-in-cell simulations to study the tolerances for this matching, and discuss techniques to loosen these constraints.

Im Rahmen unseres vom Bundesministerium fur Bildung und Forschung (BMBF) geforderten Forschungsprojekts konnten zusatzlich zu der empirisch abgeleiteten Entwicklung des Selbstevaluierungsinstruments IPSE auch die Qualitat seiner Anwendbarkeit sowie seine Aussagekraft in Bezug auf die Erreichung von Praventionszielen uberpruft werden. Die vorrangige Perspektive dieser Forschungsphase bestand darin, moglichst viele anwendungsbezogene Informationen zu sammeln, die zu einer fundierten Weiterentwicklung des Instruments fuhren sollten. Konkret sollte es dabei um die systematische Erfassung von Anregungen aus der Praxis gehen, auf deren Basis Modifikationen der einzelnen Teile von IPSE sowie Prazisierungen von Instruktionen und Informationen im Sinne einer moglichst nutzerfreundlichen Durchfuhrung von Selbstevaluierungen vorgenommen werden konnen.

Die folgende Analyse der wesentlichen Strukturen und Charakteristika der Gewalt sowie die Konzeptualisierung der Folgen basieren auf einer Detailanalyse von Interviews mit neun Betroffenen, die verschiedene Schulerjahrgange reprasentieren.