comokit4py : a python package to ease COMOKIT agent based model simulation integration into a high performance computing workflow
For researchers using COMOKIT for epidemiological simulations, this package reduces the complexity of running large-scale experiments on HPC infrastructure.
The paper presents a Python package that simplifies integrating the COMOKIT agent-based model into HPC workflows, enabling efficient generation, exploration, and reporting of experiments for COVID-19 simulation scenarios.
Agent-based model (ABM) are a kind of computer model that makes it possible to simulate a set of autonomous interacting programs called agents in a shared virtual environment. Among other application field, it has been commonly used to simulate social phenomena such as urban segregation, opinion dynamic or epidemiological crisis [1]. Recently, a research emphasis has been put on ABM to study in silico the impact of non-pharmaceutical interventions to mitigate the SARS-CoV-2 outbreak of 2020, with few of them that had a great impact on global political responses [2]. Among the model used COMOKIT [3] has been design to simulate the every-day-life of inhabitant of various cities in Vietnam and test policy interventions for various COVID-19 spread scenarios. Such endeavor required huge computational power to handle a huge number of simulation replication over a large set of parameters. In this proposal we present a python package that enables to easily generate, explore and build reports for any COMOKIT experiment to be launched over High-Performance Computing (HPC) infrastructure.