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We present a demonstration of a highly scalable, spatially explicit infectious disease simulation that models the spread of disease across all 220,000+ census block groups in the United States using a compartmental Susceptible-Infectious-Recovered (SIR) epidemiological framework. To achieve this unprecedented scale and resolution, our system leverages efficient sparse matrix and vector operations alongside statistical approximations of large numbers of independent random events via Poisson and Normal distributions. The resulting simulation produces realistic spatiotemporal dynamics that align with empirical patterns observed in major epidemics, including the COVID-19 outbreak. Our live demonstration at the conference will highlight the simulation's computational efficiency and interactive capabilities. Starting from the conference venue in Minneapolis, participants will be able to configure disease parameters and observe the geographic spread of infection in real time, offering both an educational and analytical perspective on pandemic modeling. 

Pathogen genome data offers valuable structure for spatial models, but its utility is limited by incomplete sequencing coverage. We propose a probabilistic framework for inferring genetic distances between unsequenced cases and known sequences within defined transmission chains, using time-aware evolutionary distance modeling. The method estimates pairwise divergence from collection dates and observed genetic distances, enabling biologically plausible imputation grounded in observed divergence patterns, without requiring sequence alignment or known transmission chains. Applied to highly pathogenic avian influenza A/H5 cases in wild birds in the United States, this approach supports scalable, uncertainty-aware augmentation of genomic datasets and enhances the integration of evolutionary information into spatiotemporal modeling workflows. 

The increased availability of datasets during the COVID-19 pandemic enabled machine-learning approaches for modeling and forecasting infectious diseases. However, such approaches are known to amplify the bias in the data they are trained on. Bias in such input data like clinical case data for COVID-19 is difficult to measure due to disparities in testing availability, reporting standards, and healthcare access among different populations and regions. Furthermore, the way such biases may propagate through the modeling pipeline to decision-making is relatively unknown. Therefore, we present a system that leverages a highly detailed agent-based model (ABM) of infectious disease spread in a city to simulate the collection of biased clinical case data where the bias is known. Our system allows users to load either a pre-selected region or select their own (using OpenStreetMap data for the environment and census data for the population), specify population and infectious disease parameters, and the degree(s) to which different populations will be overrepresented or underrepresented in the case data. In addition to the system, we provide a large number of benchmark datasets that produce case data at different levels of bias for different regions. Wehope that infectious disease modelers will use these datasets to investigate how well their models are robust to data bias or whether their model is overfit to biased data. 

Our ability to extract knowledge from evolving spatial phenomena and make it actionable is often impaired by unreliable, erroneous, obsolete, imprecise, sparse, and noisy data. Integrating the impact of this uncertainty is a paramount when estimating the reliability/confidence of any time-varying query result from the underlying input data. The goal of this advanced seminar is to survey solutions for managing, querying and mining uncertain spatial and spatio-temporal data. We survey different models and show examples of how to efficiently enrich query results with reliability information. We discuss both analytical solutions as well as approximate solutions based on geosimulation. 

Location-Based Services are often used to find proximal Points of Interest PoI - e.g., nearby restaurants and museums, police stations, hospitals, etc. - in a plethora of applications. An important recently addressed variant of the problem not only considers the distance/proximity aspect, but also desires semantically diverse locations in the answer-set. For instance, rather than picking several close-by attractions with similar features - e.g., restaurants with similar menus; museums with similar art exhibitions - a tourist may be more interested in a result set that could potentially provide more diverse types of experiences, for as long as they are within an acceptable distance from a given (current) location. Towards that goal, in this work we propose a novel approach to efficiently retrieve a path that will maximize the semantic diversity of the visited PoIs that are within distance limits along a given road network. We introduce a novel indexing structure - the Diversity Aggregated R-tree, based on which we devise efficient algorithms to generate the answer-set - i.e., the recommended locations among a set of given PoIs - relying on a greedy search strategy. Our experimental evaluations conducted on real datasets demonstrate the benefits of proposed methodology over the baseline alternative approaches.