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Motivation for epymorph

Metapopulation models are one of the most powerful tools developed to study and understand infectious disease dynamics, forecast pathogen spread during outbreaks, and guide the timing and nature of public health interventions in managing outbreaks. Outbreaks of pathogens including influenza virus, Ebola virus, West Nile virus, Mpox, and SARS-CoV-2 have emphasized the need for a responsive modeling community and robust modeling infrastructure, capable of quickly developing models that accurately characterize and forecast the dynamics of pathogen outbreaks across the globe.

Modelers have created increasingly sophisticated models and demonstrated their efficacy. Modeling code infrastructures, however, are often custom, hand-built, and tailored to particular pathogens and localities. More often then not, the modeling pipelines are also private. These issues make it challenging to understand model assumptions and dynamics, compare why one model may produce different dynamics than another model, and evaluate a model’s applicability to other locales or scenarios.

We do not know of any wholistic modeling softwares that capture the full model lifecycle, particularly for spatial disease models. Specifically, we define the modeling lifecycle to extend from specification of compartment model equations, to specification of host movement dynamics, to gathering of data relevant to geographic attributes (e.g., node locations, population sizes, distances between nodes, environmental characteristics across nodes, etc.), to the simulation of models, to estimation of dynamic model parameters against real surveillance data.

The epymorph package is the product of the EpiMoRPH (Epidemiological Modeling Resources for Public Health) project, and aims to fill this major gap by providing a simplified, streamlined framework for building, testing, fitting, and evaluating metapopulation models for infectious pathogens that is easily accessible to beginning modelers, while also sophisticated enough to allow rapid design and execution of complex modeling experiments by highly experienced modelers. Specific aims include dramatic streamlining of model building speed, increased model transparency, automated fitting of models to observed data, and easy transportability of models across temporal and geographic scenarios.

Acknowledgments

The development of epymorph was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under award number R01AI168144 to PI Joseph Mihaljevic, and by the Southwest Health Equity Research Collaborative at Northern Arizona University (U54MD012388), which was sponsored by the National Institute on Minority Health and Health Disparities (NIMHD).