arXiv subjects

Reiden Magdaleno

Publications and source records attributed to Reiden Magdaleno.

4 recordsLinked to original sources

arXiv2026

Reservoir: A Large-Scale Simulated Dataset for Training and Evaluating Epidemiological Models

Large-scale, standardized datasets have driven many advances in AI-based scientific modeling, from protein structure prediction to natural language processing. Infectious disease epidemiology is increasingly adopting AI methods for forecasting, surveillance, and outbreak analytics, but the time-series data available to train them remains orders of magnitude smaller than the corpora behind the advances seen in other fields. Because the scope of real-world epidemiological data cannot practically reach the scale needed to train truly large-scale AI methods, simulated data provides a possible alternative. Here we introduce Reservoir, a large open simulator and dataset of realistic epidemic simulations in which every trajectory carries complete ground-truth labels, including quantities that cannot be measured directly in a real outbreak, such as true infection counts, time-varying reproduction numbers, and counterfactual intervention effects. Reservoir is generated by a stochastic simulator with realistic noise and reporting artifacts, together with interventions with configurable timing, compliance, and age-dependent efficacy. The current release contains 500,000 outbreak trajectories spanning one billion simulated days across diverse pathogen characteristics, population structures, and intervention regimes. Reservoir enables counterfactual experiments, surveillance-design studies, and training of epidemic models at a scale real-world datasets cannot provide.

q-bio.PE↗

arXiv2026

Prediction Markets Underperform Simple Baselines For Infectious Disease Forecasting

Prediction markets (e.g., Polymarket, Kalshi) allow participants to bet on future events, producing real-time forecasts based on collective judgment. In domains such as elections and finance, markets have been effective at aggregating information, often rivaling or outperforming expert forecasters or polls. Whether this performance extends to infectious disease dynamics is unclear. Participants are self-selected and typically lack epidemiological expertise. However, markets can respond in real time to emerging news and unstructured signals in ways that standard forecasting pipelines cannot. Also, substantial financial stakes encourage participants to make an effort to be accurate. We evaluate Polymarket forecasts during 2025 and 2026 for two settings: weekly cumulative influenza hospitalizations in the US, which have an established expert-curated forecasting ensemble (CDC FluSight), and monthly measles cases, which do not. Across both settings, prediction markets fail to outperform standard benchmarks. For influenza, markets are competitive with low-performing individual FluSight models but are dominated by the FluSight ensemble: even when we combine market forecasts with the ensemble, the best combination puts zero weight on the markets. For measles, markets are outperformed by simple statistical baselines. We diagnose two sources of market inefficiency: placement of probability mass on impossible outcomes (e.g., decreasing values in cumulative forecasts) and low trading volume. These results suggest that current prediction markets are not reliable forecasters of infectious disease dynamics on their own or useful as complementary features for existing forecasting systems.

stat.AP↗

arXiv2025

Mantis: A Foundation Model for Mechanistic Disease Forecasting

Infectious disease forecasting in novel outbreaks or low-resource settings is hampered by the need for large disease and covariate data sets, bespoke training, and expert tuning, all of which can hinder rapid generation of forecasts for new settings. To help address these challenges, we developed Mantis, a foundation model trained entirely on mechanistic simulations, which enables out-of-the-box forecasting across diseases, regions, and outcomes, even in settings with limited historical data. We evaluated Mantis against 78 forecasting models across sixteen diseases with diverse modes of transmission, assessing both point forecast accuracy (mean absolute error) and probabilistic performance (weighted interval score and coverage). Despite using no real-world data during training, Mantis achieved lower mean absolute error than all models in the CDC's COVID-19 Forecast Hub when backtested on early pandemic forecasts which it had not previously seen. Across all other diseases tested, Mantis consistently ranked in the top two models across evaluation metrics. Mantis further generalized to diseases with transmission mechanisms not represented in its training data, demonstrating that it can capture fundamental contagion dynamics rather than memorizing disease-specific patterns. These capabilities illustrate that purely simulation-based foundation models such as Mantis can provide a practical foundation for disease forecasting: general-purpose, accurate, and deployable where traditional models struggle.

cs.AI↗

arXiv2025

Simulation as Supervision: Mechanistic Pretraining for Scientific Discovery

Scientific modeling faces a tradeoff between the interpretability of mechanistic theory and the predictive power of machine learning. While existing hybrid approaches have made progress by incorporating domain knowledge into machine learning methods as functional constraints, they can be limited by a reliance on precise mathematical specifications. When the underlying equations are partially unknown or misspecified, enforcing rigid constraints can introduce bias and hinder a model's ability to learn from data. We introduce Simulation-Grounded Neural Networks (SGNNs), a framework that incorporates scientific theory by using mechanistic simulations as training data for neural networks. By pretraining on diverse synthetic corpora that span multiple model structures and realistic observational noise, SGNNs internalize the underlying dynamics of a system as a structural prior. We evaluated SGNNs across multiple disciplines, including epidemiology, ecology, social science, and chemistry. In forecasting tasks, SGNNs outperformed both standard data-driven baselines and physics-constrained hybrid models. They nearly tripled the forecasting skill of the average CDC models in COVID-19 mortality forecasts and accurately forecasted high-dimensional ecological systems. SGNNs demonstrated robustness to model misspecification, performing well even when trained on data with incorrect assumptions. Our framework also introduces back-to-simulation attribution, a method for mechanistic interpretability that explains real-world dynamics by identifying their most similar counterparts within the simulated corpus. By unifying these techniques into a single framework, we demonstrate that diverse mechanistic simulations can serve as effective training data for robust scientific inference.

cs.LG↗