SPARSEMODr: Rapid simulations of spatially explicit and stochastic models infectious diseases, including COVID-19
medRxiv (Cold Spring Harbor Laboratory)(2022)
Abstract
Building realistically complex models of infectious disease transmission that are relevant for informing public health is conceptually challenging and requires knowledge of coding architecture that can implement key modeling conventions. For example, many of the models built to understand COVID-19 dynamics have included stochasticity, transmission dynamics that change throughout the epidemic due to changes in host behavior or public health interventions, and spatial structures that account for important spatio-temporal heterogeneities. Here we introduce an R package, SPARSEMODr, that allows users to simulate disease models that are stochastic and spatially explicit, including a model for COVID-19 that was useful in the early phases of the epidemic. SPARSEMOD stands for SPA tial R esolution- SE nsitive M odels of O utbreak D ynamics, and our goal is to demonstrate particular conventions for rapidly simulating the dynamics of more complex, spatial models of infectious disease. In this report, we outline the features and workflows of our software package that allow for user-customized simulations. We believe the example models provided in our package will be useful in educational settings, as the coding conventions are adaptable, and will help new modelers to better understand important assumptions that were built into sophisticated COVID-19 models.
### Competing Interest Statement
The authors have declared no competing interest.
### Funding Statement
This material is based upon work supported by the National Science Foundation under Grant No. 2028629.
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Key words
infectious diseases,rapid simulations
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