arXiv subjects

Andreas Zufle

Publications and source records attributed to Andreas Zufle.

10 recordsLinked to original sources

arXiv2026

Mobility and Contact Networks Shape Epidemic Outcomes: A Large-Scale Agent-Based Modeling Study

Human mobility plays a central role in shaping contact patterns that drive infectious disease transmission, yet mobility is often simplified in agent-based models (ABMs) due to data and computational constraints. The effects of these simplifications on model outputs are poorly understood. In this study, we systematically examined how alternative mobility assumptions influence emergent contact networks and epidemic dynamics within a large-scale ABM. Using a synthetic population of one million agents representing an urban environment, we implemented five mobility models varying along two dimensions: activity patterns (empirically derived vs. randomized) and destination choice mechanisms (empirical popularity, distance-based, or random). Holding disease parameters constant, we found that mobility assumptions alone produced substantially different contact network structures and epidemic trajectories, including differences in peak incidence and shifts in outbreak timing. Importantly, these differences could not be attributed to agents simply moving more or less overall since aggregate movement volumes were broadly comparable across models. Instead, the contrasting dynamics arose from how mobility generates contact opportunities: specifically, who meets whom, where, and how often. These results suggest that in models where mobility has not been carefully calibrated, simulated epidemic outcomes and evaluations of interventions may reflect mobility assumptions as much as underlying disease parameters. Our findings underscore the importance of mobility model calibration and validation, particularly in policy-facing applications.

physics.soc-ph↗

arXiv2026

PlaceRep: Geospatial Place Representation Learning from Large-Scale Point-of-Interest Data

Learning effective representations of urban environments requires capturing spatial structure beyond fixed administrative boundaries. Existing geospatial representation learning approaches typically aggregate Points of Interest (POIs) into pre-defined administrative regions such as census units or ZIP code areas, assigning a single embedding to each region. However, POIs often form semantically meaningful groups that extend across, within, or beyond these boundaries, defining places that better reflect human activity and urban function. To address this limitation, we propose PlaceRep, a geospatial representation learning method that constructs place-level representations by clustering spatially and semantically related POIs. PlaceRep summarizes large-scale POI graphs from U.S. Foursquare data to produce general-purpose urban region embeddings while automatically identifying places across multiple spatial scales. By eliminating model pre-training, PlaceRep provides a scalable and efficient solution for multi-granular geospatial analysis. Experiments using the tasks of population density estimation and housing price prediction as downstream tasks show that PlaceRep outperforms most state-of-the-art graph-based geospatial representation learning methods and achieves up to a x100 speedup in generating region-level representations on large-scale POI graphs. The implementation of PlaceRep is available at https://github.com/mohammadhashemii/PlaceRep.

cs.LG↗

arXiv2026

An Infectious Disease Spread Simulation Based on Large Language Model Decision Making

Modelling individual decision-making during infectious disease outbreaks is crucial for understanding behavioural dynamics and informing effective public health interventions. Prior work has shown that large language models can simulate realistic human behaviour by generating agent decisions based on demographic prompts and situational context. We build on this foundation with a spatially grounded, agent-based simulation framework that integrates LLM-generated decisions about self-reported influenza-like illness into a census-based synthetic population of agents. Location is treated as a central feature: agents are assigned to spatial units within cities, capturing the spatial distributions of different demographic groups using real-world census data and enabling geographically diverse behavioural modelling. We implement and compare three decision scenarios, independent reasoning, household influence, and message framing, and simulate self-reporting outcomes in San Francisco and Atlanta. Results reveal that income and education are the dominant drivers of reporting rate variation, with smaller but consistent effects from geography, LLM model choice, and message framing. Our framework generates synthetic data that captures both social and geographic heterogeneity, supporting spatial epidemiological modelling and bias-aware behavioural analysis.

cs.AI↗

arXiv2025

All Models Are Wrong, But Can They Be Useful? Lessons from COVID-19 Agent-Based Models: A Systematic Review

The COVID-19 pandemic prompted a surge in computational models to simulate disease dynamics and guide interventions. Agent-based models (ABMs) are well-suited to capture population and environmental heterogeneity, but their rapid deployment raised questions about utility for health policy. We systematically reviewed 536 COVID-19 ABM studies published from January 2020 to December 2023, retrieved from Web of Science, PubMed, and Wiley on January 30, 2024. Studies were included if they used ABMs to simulate COVID-19 transmission, where reviews were excluded. Studies were assessed against nine criteria of model usefulness, including transparency and re-use, interdisciplinary collaboration and stakeholder engagement, and evaluation practices. Publications peaked in late 2021 and were concentrated in a few countries. Most models explored behavioral or policy interventions (n = 294, 54.85%) rather than real-time forecasting (n = 9, 1.68%). While most described model assumptions (n = 491, 91.60%), fewer disclosed limitations (n = 349, 65.11%), shared code (n = 219, 40.86%), or built on existing models (n = 195, 36.38%). Standardized reporting protocols (n = 36, 6.72%) and stakeholder engagement were rare (13.62%, n = 73). Only 2.24% (n = 12) described a comprehensive validation framework, though uncertainty was often quantified (n = 407, 75.93%). Limitations of this review include underrepresentation of non-English studies, subjective data extraction, variability in study quality, and limited generalizability. Overall, COVID-19 ABMs advanced quickly, but lacked transparency, accessibility, and participatory engagement. Stronger standards are needed for ABMs to serve as reliable decision-support tools in future public health crises.

cs.MA↗

arXiv2025

From Points to Places: Towards Human Mobility-Driven Spatiotemporal Foundation Models via Understanding Places

Capturing human mobility is essential for modeling how people interact with and move through physical spaces, reflecting social behavior, access to resources, and dynamic spatial patterns. To support scalable and transferable analysis across diverse geographies and contexts, there is a need for a generalizable foundation model for spatiotemporal data. While foundation models have transformed language and vision, they remain limited in handling the unique challenges posed by the spatial, temporal, and semantic complexity of mobility data. This vision paper advocates for a new class of spatial foundation models that integrate geolocation semantics with human mobility across multiple scales. Central to our vision is a shift from modeling discrete points of interest to understanding places: dynamic, context-rich regions shaped by human behavior and mobility that may comprise many places of interest. We identify key gaps in adaptability, scalability, and multi-granular reasoning, and propose research directions focused on modeling places and enabling efficient learning. Our goal is to guide the development of scalable, context-aware models for next-generation geospatial intelligence. These models unlock powerful applications ranging from personalized place discovery and logistics optimization to urban planning, ultimately enabling smarter and more responsive spatial decision-making.

cs.AI↗

arXiv2025

Deep Identification of Propagation Trees

Understanding propagation structures in graph diffusion processes, such as epidemic spread or misinformation diffusion, is a fundamental yet challenging problem. While existing methods primarily focus on source localization, they cannot reconstruct the underlying propagation trees i.e., "who infected whom", which are substantial for tracking the propagation pathways and investigate diffusion mechanisms. In this work, we propose Deep Identification of Propagation Trees (DIPT), a probabilistic framework that infers propagation trees from observed diffused states. DIPT models local influence strengths between nodes and leverages an alternating optimization strategy to jointly learn the diffusion mechanism and reconstruct the propagation structure. Extensive experiments on five real-world datasets demonstrate the effectiveness of DIPT in accurately reconstructing propagation trees.

cs.SI↗

arXiv2024

Transferable Unsupervised Outlier Detection Framework for Human Semantic Trajectories

Semantic trajectories, which enrich spatial-temporal data with textual information such as trip purposes or location activities, are key for identifying outlier behaviors critical to healthcare, social security, and urban planning. Traditional outlier detection relies on heuristic rules, which requires domain knowledge and limits its ability to identify unseen outliers. Besides, there lacks a comprehensive approach that can jointly consider multi-modal data across spatial, temporal, and textual dimensions. Addressing the need for a domain-agnostic model, we propose the Transferable Outlier Detection for Human Semantic Trajectories (TOD4Traj) framework.TOD4Traj first introduces a modality feature unification module to align diverse data feature representations, enabling the integration of multi-modal information and enhancing transferability across different datasets. A contrastive learning module is further pro-posed for identifying regular mobility patterns both temporally and across populations, allowing for a joint detection of outliers based on individual consistency and group majority patterns. Our experimental results have shown TOD4Traj's superior performance over existing models, demonstrating its effectiveness and adaptability in detecting human trajectory outliers across various datasets.

cs.LG↗

arXiv2024

The Patterns of Life Human Mobility Simulation

We demonstrate the Patterns of Life Simulation to create realistic simulations of human mobility in a city. This simulation has recently been used to generate massive amounts of trajectory and check-in data. Our demonstration focuses on using the simulation twofold: (1) using the graphical user interface (GUI), and (2) running the simulation headless by disabling the GUI for faster data generation. We further demonstrate how the Patterns of Life simulation can be used to simulate any region on Earth by using publicly available data from OpenStreetMap. Finally, we also demonstrate recent improvements to the scalability of the simulation allows simulating up to 100,000 individual agents for years of simulation time. During our demonstration, as well as offline using our guides on GitHub, participants will learn: (1) The theories of human behavior driving the Patters of Life simulation, (2) how to simulate to generate massive amounts of synthetic yet realistic trajectory data, (3) running the simulation for a region of interest chosen by participants using OSM data, (4) learn the scalability of the simulation and understand the properties of generated data, and (5) manage thousands of parallel simulation instances running concurrently.

cs.MA↗

arXiv2024

Urban Anomalies: A Simulated Human Mobility Dataset with Injected Anomalies

Human mobility anomaly detection based on location is essential in areas such as public health, safety, welfare, and urban planning. Developing models and approaches for location-based anomaly detection requires a comprehensive dataset. However, privacy concerns and the absence of ground truth hinder the availability of publicly available datasets. With this paper, we provide extensive simulated human mobility datasets featuring various anomaly types created using an existing Urban Patterns of Life Simulation. To create these datasets, we inject changes in the logic of individual agents to change their behavior. Specifically, we create four of anomalous agent behavior by (1) changing the agents' appetite (causing agents to have meals more frequently), (2) changing their group of interest (causing agents to interact with different agents from another group). (3) changing their social place selection (causing agents to visit different recreational places) and (4) changing their work schedule (causing agents to skip work), For each type of anomaly, we use three degrees of behavioral change to tune the difficulty of detecting the anomalous agents. To select agents to inject anomalous behavior into, we employ three methods: (1) Random selection using a centralized manipulation mechanism, (2) Spread based selection using an infectious disease model, and (3) through exposure of agents to a specific location. All datasets are split into normal and anomalous phases. The normal phase, which can be used for training models of normalcy, exhibits no anomalous behavior. The anomalous phase, which can be used for testing for anomalous detection algorithm, includes ground truth labels that indicate, for each five-minute simulation step, which agents are anomalous at that time. Datasets are generated using the maps (roads and buildings) for Atlanta and Berlin, having 1k agents in each simulation.

cs.SI↗

arXiv2024

GeoLife+: Large-Scale Simulated Trajectory Datasets Calibrated to the GeoLife Dataset

Analyzing individual human trajectory data helps our understanding of human mobility and finds many commercial and academic applications. There are two main approaches to accessing trajectory data for research: one involves using real-world datasets like GeoLife, while the other employs simulations to synthesize data. Real-world data provides insights from real human activities, but such data is generally sparse due to voluntary participation. Conversely, simulated data can be more comprehensive but may capture unrealistic human behavior. In this Data and Resource paper, we combine the benefit of both by leveraging the statistical features of real-world data and the comprehensiveness of simulated data. Specifically, we extract features from the real-world GeoLife dataset such as the average number of individual daily trips, average radius of gyration, and maximum and minimum trip distances. We calibrate the Pattern of Life Simulation, a realistic simulation of human mobility, to reproduce these features. Therefore, we use a genetic algorithm to calibrate the parameters of the simulation to mimic the GeoLife features. For this calibration, we simulated numerous random simulation settings, measured the similarity of generated trajectories to GeoLife, and iteratively (over many generations) combined parameter settings of trajectory datasets most similar to GeoLife. Using the calibrated simulation, we simulate large trajectory datasets that we call GeoLife+, where + denotes the Kleene Plus, indicating unlimited replication with at least one occurrence. We provide simulated GeoLife+ data with 182, 1k, and 5k over 5 years, 10k, and 50k over a year and 100k users over 6 months of simulation lifetime.

cs.DB↗