Speaker
Description
We present an in development simulation-based framework for evaluating inference and decision procedures in multi-host, One Health infectious disease systems. The framework integrates surveillance design, forecasting, and scenario analysis within a unified pipeline built around mechanistic transmission models that serve as synthetic data-generating processes and known ground truth. Structured multi-population differential equation models represent coupled human, animal, and vector dynamics, incorporating demographic turnover, trait structure, and spatial heterogeneity. Empirical estimates and expert elicitation inform parameterization, while a stochastic observation layer maps latent states to synthetic surveillance data through explicit reporting and sampling processes.
This generative environment enables systematic comparison of mechanistic, statistical, and machine-learning approaches under controlled observation error, partial observability, and model misspecification, with performance assessed using robustness and decision-relevant metrics. We illustrate the framework using a multi-host ODE-PDE hybrid model of Crimean–Congo hemorrhagic fever virus, a tick-born orthonirovirus, in Uganda, demonstrating the utility our our framework for studying identifiability, forecasting, and surveillance optimization across heterogeneous one-health systems.
