Simulating the landscape eco-evolution of host-pathogen systems with CDMetaPOP

Simulating the eco-evolutionary feedbacks between wildlife hosts and pathogens in complex landscapes remains a major computational challenge. While universal simulation languages enable custom model development, few integrated frameworks exist that mechanistically link disease dynamics, landscape-scale demography, and forward-time population genetics. We address this gap by introducing CDMetaPOP-Disease, an open-source, spatially explicit, individual-based demo-genetic module. The module's primary informatics advance is its native ability to mechanistically link individual genotypes to epidemiological transition rates, enabling the simulation of host disease response strategies, such as resistance (reducing infection probability) and tolerance (reducing mortality). We verified the module's core features against classic SIR-type models, achieving high fidelity (Nash-Sutcliffe Efficiency $\ge$ 0.98) to theoretical expectations. We demonstrated the model's utility by simulating the emergence of a novel pathogen in a realistic bat-pathogen system, showcasing the tool's power to track both pathogen diffusion and the resulting spatial-genetic signatures of host adaptation. Optimized for performance and modularity, CDMetaPOP-Disease provides a flexible platform for the ecological informatics community to investigate complex feedbacks and inform proactive management of infectious diseases in realistic landscapes.