Public health monitoring traditionally relies on active reporting from diverse data sources, including clinical and administrative data, disease registries, and population-based surveys. Yet these surveillance methods often face challenges such as incomplete reporting, time lags, and variable population coverage. Meanwhile, diagnostic laboratories routinely generate vast volumes of operational data that are currently untapped for public health monitoring. As these data are not collected for scientific inquiry or population-level surveillance, they often lack formal validation and may contain sensitive information. We developed a Bayesian hierarchical model to decompose aggregated laboratory assay volume data for 1.1 billion clinician-ordered assays across the U.S. from October 2019 to March 2023 into interpretable epidemiological and health system signals. The signals generated by these models were compared with known perturbances to health systems, such as the COVID-19 pandemic. The method does not rely on assay outcomes or individual-level data, providing quantitative signals of epidemiological trends and health system responses while protecting both the privacy of patients and commercially sensitive information. Temporal analysis reveals qualitatively different responses of assay volumes to major public health events, identifying assays whose use paralleled surges in hospitalization rates during the COVID-19 pandemic documented through traditional public health reporting structures. This framework suggests that routine operational data can be used to augment traditional surveillance by identifying anomalous patterns for expert epidemiological investigation. To be truly effective, data from multiple vendors must be integrated to create a comprehensive real-time national or supranational public health surveillance platform. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement All authors were funded in whole or in part by Siemens Healthcare Diagnostics, Inc. (Siemens Healthineers-Harvard Chan RCA, 8317147-01). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data supporting the conclusions in this paper is commercially sensitive and was made available to researchers at Harvard University under Non-disclosure Agreement. The code underlying the analyses, including an application to publicly available infectious disease data from Project Tycho, can be found at https://github.com/onnela-lab/sentinel.

Extracting low-dimensional summary statistics from large datasets is essential for efficient (likelihood-free) inference. We characterize three different classes of summaries and demonstrate their importance for correctly analyzing dimensionality reduction algorithms. We demonstrate that minimizing the expected posterior entropy (EPE) under the prior predictive distribution of the model provides a unifying principle that subsumes many existing methods; they are shown to be equivalent to, or special or limiting cases of, minimizing the EPE. We offer a unifying framework for obtaining informative summaries and propose a practical method using conditional density estimation to learn high-fidelity summaries automatically. We evaluate this approach on diverse problems, including a challenging benchmark model with a multi-modal posterior, a population genetics model, and a dynamic network model of growing trees. The results show that EPE-minimizing summaries can lead to posterior inference that is competitive with, and in some cases superior to, dedicated likelihood-based approaches, providing a powerful and general tool for practitioners.

Large-scale network data can pose computational challenges, be expensive to acquire, and compromise the privacy of individuals in social networks. We show that the locations and scales of latent space cluster models can be inferred from the number of connections between groups alone. We demonstrate this modelling approach using synthetic data and apply it to friendships between students collected as part of the Add Health study, eliminating the need for node-level connection data. The method thus protects the privacy of individuals and simplifies data sharing. It also offers performance advantages over node-level latent space models because the computational cost scales with the number of clusters rather than the number of nodes.

Zusammenfassung Die Anämiebehandlung durch Versorgung mit Eisen, Folsäure und Vitamin B12 und Stimulation der Erythropoese mit Erythropoetin bei Patienten, welche Bluttransfusionen ablehnen, ist eine paradigmatische Herausforderung an das Patient Blood Management (PBM), insbesondere in Notfallsituationen. Je nach Anämieausprägung muss mit mindestens 14 Tagen für eine vollständige Anämiekorrektur gerechnet werden. Wir stellen die Dynamik der Anämie-Entwicklung und -Erholung bei unter ‚real life‘-Bedingungen verzögertem Patient Blood Management im Fall eines Patienten vor, der Transfusionen aus religiösen Gründen ablehnte.

Alcohol intoxication is known to affect blood coagulation. The specific effects remain poorly understood. Here, we investigate the impact of severe alcohol intoxication on blood clotting by comprehensive coagulation testing. A prospective study included 21 patients admitted to the emergency department of University Hospital Düsseldorf with severe alcohol intoxication (target blood alcohol concentration > 2 g/l). Platelet function and coagulation was compared between states of alcohol intoxication and soberness using multiple platelet function analysis, thrombelastography and determination of single coagulation factors. The same test panel was used to study in vitro-effects of ethanol on coagulation. Blood alcohol was correlated with impaired platelet aggregation determined in vivo by functional testing employing ADP and ASPI stimulation. Blood alcohol-associated coagulation impairment was not detectable by thrombelastography or clotting factor measurements. Blood alcohol was negatively correlated with von Willebrand factor ratio and clot strength. The association of elevated blood alcohol with impaired coagulation could not be replicated in vitro. Our findings suggest that alcohol impairs primary hemostasis by reducing platelet function, while secondary hemostasis remains largely unaffected. Reversion of effects upon sobering suggest a rather direct impact of alcohol on platelet function. That effect was, however, not replicated in vitro possibly implicating involvement of vascular factors. Blood alcohol has a potentially negative impact on platelet function, which should be considered in the clinical management of intoxicated patients, especially in emergency settings. Potential bleeding risks due to increased blood alcohol are possibly detected by analysis of platelet function, while not by thrombelastography or plasmatic coagulation tests.

Mechanistic network models can capture salient characteristics of empirical networks using a small set of domain-specific, interpretable mechanisms. Yet inference remains challenging because the likelihood is often intractable. We show that, for a broad class of growing network models, information about model parameters is localized in the network, i.e., the likelihood can be expressed in terms of small subgraphs. We take a Bayesian perspective to inference and develop neural density estimators (NDEs) to approximate the posterior distribution of model parameters using graph neural networks (GNNs) with limited receptive size, i.e., the GNN can only "see" small subgraphs. We characterize nine growing network models in terms of their localization and demonstrate that localization predictions agree with NDEs on simulated data. Even for non-localized models, NDEs can infer high-fidelity posteriors matching model-specific inference methods at a fraction of the cost. Our findings establish information localization as a fundamental property of network growth, theoretically justifying the analysis of local subgraphs embedded in larger, unobserved networks and the use of GNNs with limited receptive field for likelihood-free inference.

The treatment of anaemia by supplementation with iron, folic acid, vitamin B12 and stimulation of erythropoiesis with erythropoietin in patients who refuse blood transfusions offers a paradigmatic challenge for Patient Blood Management (PBM), particularly in emergency situations. Depending on anaemia severity, a minimum of 14 days must be anticipated for complete anaemia correction. We present the dynamics of anaemia development and recovery in a patient in a case with delayed Patient Blood Management under 'real-world' conditions. The patient refused transfusions on religious grounds.

ABSTRACT:Thrombosis and thrombocytopenia syndromes (TTS) describe immune-mediated thrombotic adverse reactions after vaccination against COVID-19. Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a well-known subentity of TTS, caused by adenovirus vector-based vaccines. VITT is mediated by anti-platelet factor 4 (PF4) immunoglobulin G (IgG) antibodies, activating platelets via Fc-γ IIa receptors (FcγRIIa). We describe clinical and serological features of 18 patients with anti-PF4/heparin enzyme-linked immunosorbent assay (ELISA)-negative TTS in temporal relationship to messenger RNA (mRNA)-based COVID-19 vaccination. Symptoms began at a median of 7 (range 1 - 61) days after vaccination. Patients showed thrombocytopenia (platelet count 59 × 103/μL; range, 0 to 127 × 103/μL); petechiae (n = 7), venous thromboembolism (n = 11), arterial thrombosis (n = 6), disseminated intravascular coagulation (n = 1), and combined arterial and venous thromboses (n = 1). Twelve sera-induced FcγRIIa-dependent and caspase-independent procoagulant activation of platelets indicated by phosphatidylserine exposure and CD62P expression. We found histones precipitated with IgG fractions of TTS sera. Antibodies binding to histones were found in 8 of 12 platelet-activating sera. Ex vivo-generated histone/antihistone IgG complexes strongly activated platelets via FcγRIIa, whereas antihistone IgG alone did not. Platelet autoantibodies were detected in 7 of 12 sera targeting glycoprotein (GP) IIb/IIIa (n = 5), GPIb/IX (n = 5), and GPIa/IIa (n = 3). However, sera containing platelet anti-GPIIb/IIIa autoantibodies activated also platelets from a patient with Glanzmann thrombasthenia, making it unlikely that these autoantibodies are causative for platelet activation. Finally, 2 of 114 healthy vaccinees developed antihistone antibodies after mRNA-based COVID-19 vaccination. Our data indicate a new subentity of TTS associated with platelet-activating histone/antihistone IgG complexes. Further studies are warranted to characterize the biological and clinical role of post-mRNA-based vaccination antihistone antibodies. The SeCo trial was registered at www.ClinicalTrials.gov as #NCT04370119.

Characterizing sexual contact networks is essential for understanding sexually transmitted infections, but principled parameter inference for mechanistic network models remains challenging. We develop a discrete-time simulation framework that enables parameter estimation using approximate Bayesian computation. The interpretable model incorporates relationship formation, dissolution, concurrency, casual contacts, and population turnover. Applying our framework to survey data from 403 men who have sex with men in Stockholm, we provide principled uncertainty quantification for key network dynamics. Our analysis estimates the timescale for seeking a new steady relationship at 25 weeks and for relationship dissolution at 42 weeks. Casual contacts occur more frequently for single individuals (every 1.8 weeks) than for partnered individuals (every 4.5 weeks). However, while cross-sectional data constrains these parameters, migration rates remain poorly identified. We demonstrate that simple longitudinal data can resolve this issue. Tracking participant retention between survey waves directly informs migration rates, though survey dropout is a potential confounder. Furthermore, simple binary survey questions can outperform complex timeline follow-back methods for estimating contact frequencies. This framework provides a foundation for uncertainty quantification in network epidemiology and offers practical strategies to improve inference from surveys, the primary data source for studying sexual behavior.

Despite much research on early detection of anomalies from surveillance data, a systematic framework for appropriately acting on these signals is lacking. We addressed this gap by formulating a hidden Markov-style model for time-series surveillance, where the system state, the observed data, and the decision rule are all binary. We incur a delayed cost, c , whenever the system is abnormal and no action is taken, or an immediate cost, k , with action, where k < c . If action costs are too high, then surveillance is detrimental, and intervention should never occur. If action costs are sufficiently low, then surveillance is detrimental, and intervention should always occur. Only when action costs are intermediate and surveillance costs are sufficiently low is surveillance beneficial. Our equations provide a framework for assessing which approach may apply under a range of scenarios and, if surveillance is warranted, facilitate methodical classification of intervention strategies. Our model thus offers a conceptual basis for designing real-world public health surveillance systems.

Gaussian processes (GPs) are sophisticated distributions to model functional data. Whilst theoretically appealing, they are computationally cumbersome except for small datasets. We implement two methods for scaling GP inference in Stan: First, a general sparse approximation using a directed acyclic dependency graph; second, a fast, exact method for regularly spaced data modeled by GPs with stationary kernels using the fast Fourier transform. Based on benchmark experiments, we offer guidance for practitioners to decide between different methods and parameterizations. We consider two real-world examples to illustrate the package. The implementation follows Stan's design and exposes performant inference through a familiar interface. Full posterior inference for ten thousand data points is feasible on a laptop in less than 20 seconds. Details on how to get started using the popular interfaces cmdstanpy for Python and cmdstanr for R are provided.

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Other. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Wastewater catchment areas in Great BritainAuthorsTillHoffmanniDSarahBunneyBarbaraKasprzyk-HordernAndrewSingeriDSee all authors Till HoffmanniDCorresponding Author• Submitting AuthorImperial College LondoniDhttps://orcid.org/0000-0003-4403-0722view email addressThe email was not providedcopy email addressSarah BunneyImperial College Londonview email addressThe email was not providedcopy email addressBarbara Kasprzyk-HordernUniversity of Bathview email addressThe email was not providedcopy email addressAndrew SingeriDUK Centre for Ecology & HydrologyiDhttps://orcid.org/0000-0003-4705-6063view email addressThe email was not providedcopy email address

Mechanistic models can provide an intuitive and interpretable explanation of network growth by specifying a set of generative rules. These rules can be defined by domain knowledge about real-world mechanisms governing network growth or may be designed to facilitate the appearance of certain network motifs. In the formation of real-world networks, multiple mechanisms may be simultaneously involved; it is then important to understand the relative contribution of each of these mechanisms. In this paper, we propose the use of a conditional density estimator, augmented with a graph neural network, to perform inference on a flexible mixture of network-forming mechanisms. This event-wise mixture-of-mechanisms model assigns mechanisms to each edge formation event rather than stipulating node-level mechanisms, thus allowing for an explanation of the network generation process, as well as the dynamic evolution of the network over time. We demonstrate that our approximate Bayesian approach yields valid inferences for the relative weights of the mechanisms in our model, and we utilize this method to investigate the mechanisms behind the formation of a variety of real-world networks.

Network science explores intricate connections among objects, employed in diverse domains like social interactions, fraud detection, and disease spread. Visualization of networks facilitates conceptualizing research questions and forming scientific hypotheses. Networks, as mathematical high-dimensional objects, require dimensionality reduction for (planar) visualization. Visualizing empirical networks present additional challenges. They often contain false positive (spurious) and false negative (missing) edges. Traditional visualization methods don't account for errors in observation, potentially biasing interpretations. Moreover, contemporary network data includes rich nodal attributes. However, traditional methods neglect these attributes when computing node locations. Our visualization approach aims to leverage nodal attribute richness to compensate for network data limitations. We employ a statistical model estimating the probability of edge connections between nodes based on their covariates. We enhance the Fruchterman-Reingold algorithm to incorporate estimated dyad connection probabilities, allowing practitioners to balance reliance on observed versus estimated edges. We explore optimal smoothing levels, offering a natural way to include relevant nodal information in layouts. Results demonstrate the effectiveness of our method in achieving robust network visualization, providing insights for improved analysis.

Network models are increasingly used to study infectious disease spread. Exponential Random Graph models have a history in this area, with scalable inference methods now available. An alternative approach uses mechanistic network models. Mechanistic network models directly capture individual behaviors, making them suitable for studying sexually transmitted diseases. Combining mechanistic models with Approximate Bayesian Computation allows flexible modeling using domain-specific interaction rules among agents, avoiding network model oversimplifications. These models are ideal for longitudinal settings as they explicitly incorporate network evolution over time. We implemented a discrete-time version of a previously published continuous-time model of evolving contact networks for men who have sex with men and proposed an ABC-based approximate inference scheme for it. As expected, we found that a two-wave longitudinal study design improves the accuracy of inference compared to a cross-sectional design. However, the gains in precision in collecting data twice, up to 18%, depend on the spacing of the two waves and are sensitive to the choice of summary statistics. In addition to methodological developments, our results inform the design of future longitudinal network studies in sexually transmitted diseases, specifically in terms of what data to collect from participants and when to do so.

BACKGROUND:Direct oral anticoagulants (DOAC) such as Xa inhibitors rivaroxaban (riva) and apixaban (apix) are increasingly replacing Vitamin K antagonists in prophylaxis and treatment of venous thromboembolism (VTE). Measurements of DOAC plasma levels may be necessary in certain clinical conditions to determine the further dosage. Making decisions is made more difficult by the fact that the peak and trough plasma levels are subject to strong inter-individual fluctuations with overlapping reference ranges. We wanted to find out whether the peak and trough levels can be narrowed if they are determined based on age and gender.METHODS:Therefore, we collected data on peak and trough anti-Xa concentrations in patients treated with either rivaroxaban (n = 93) or apixaban (n = 51) in one center. After exclusion of blood samples of uncertain oral intake, 83 samples for rivaroxaban and 49 samples for apixaban remained for further analysis. Differences between male (riva n = 42, apix n = 28) and female (riva n = 41 and apix n = 21) as well as young (≤ 60 years, riva n = 44, apix n = 23) and elder (> 60 years) patients (riva n = 39 and apix n = 26) were analyzed by Student`s t-test and retrospective regression.RESULTS:We found no differences in age and gender for the apix peak levels. But women had significantly higher riva peak concentrations than men (308.8 ± 178.1 ng/mL versus 206.4 ± 80 ng/mL, p = 0.013). Patients older than 60 years had significantly higher riva peak levels than those younger than 60 (293.7 ± 126.7 ng/mL versus 211.7 ± 158.4 ng/mL, p = 1.29 x 10-8).CONCLUSIONS:In search of narrowing standard peak and trough levels in patients' sera we found significant differ-ences between patients below and above sixty years of age. Gender-associated differences were found in rivaroxa-ban levels possibly explaining DOAC associated hypermenorrhea. In conclusion, gender and age should be included in the determination of peak blood concentration references.

BACKGROUND:Rapid diagnosis and treatment has improved outcome of patients with vaccine-induced immune thrombocytopenia and thrombosis (VITT). However, after the acute episode, many questions on long-term management of VITT remained unanswered.OBJECTIVES:To analyze, in patients with VITT, the long-term course of anti-platelet factor 4 (PF4) antibodies; clinical outcomes, including risk of recurrent thrombosis and/or thrombocytopenia; and the effects of new vaccinations.METHODS:71 patients with serologically confirmed VITT in Germany were enrolled into a prospective longitudinal study and followed for a mean of 79 weeks from March 2021 to January 2023. The course of anti-PF4 antibodies was analyzed by consecutive anti-PF4/heparin immunoglobulin G enzyme-linked immunosorbent assay and PF4-enhanced platelet activation assay.RESULTS:Platelet-activating anti-PF4 antibodies became undetectable in 62 of 71 patients (87.3%; 95% CI, 77.6%-93.2%). In 6 patients (8.5%), platelet-activating anti-PF4 antibodies persisted for >18 months. Five of 71 patients (7.0%) showed recurrent episodes of thrombocytopenia and/or thrombosis; in 4 of them (80.0%), alternative explanations beside VITT were present. After further COVID-19 vaccination with a messenger RNA vaccine, no reactivation of platelet-activating anti-PF4 antibodies or new thrombosis was observed. No adverse events occurred in our patients subsequently vaccinated against influenza, tick-borne encephalitis, varicella, tetanus, diphtheria, pertussis, and polio. No new thrombosis occurred in the 24 patients (33.8%) who developed symptomatic SARS-CoV-2 infection following recovery from acute VITT.CONCLUSION:Once the acute episode of VITT has passed, patients appear to be at low risk for recurrent thrombosis and/or thrombocytopenia.

The COVID-19 pandemic has challenged both health care administrators and public health policymakers in unprecedented ways. The dearth of actionable clinical data nationwide impeded prompt evidence-based policymaking and adjustments to health care systems. This highlighted the importance of accessible high-quality real-time clinical data at the local and national level for tackling emerging public health threats. Current methods of data collection for public health are tedious and time-consuming, limiting the speed at which effective measures are determined. Meanwhile, diagnostic companies are increasingly embracing digital technologies, which could put anonymized aggregate diagnostic data, and effective analytics at the heart of health care policy and management. The key to achieving that is-in part-crafting effective public-private partnerships at the federal and international level. Carefully carving out the terms of such partnerships will be crucial to their success. The benefits for governments and their citizens are likely to be worthwhile as public health threats continue to arise, spending remains overstretched, and health care systems overburdened.