SIM-D: An Agent-Based Simulator for Modeling Contagion in Population

Abstract

The spread of infectious diseases such as COVID-19, flu influenza, malaria, dengue, mumps, and rubella in a population is a big threat to public health. The infectious diseases spread from one person to another person through close contact. Without proper planning, an infectious disease can become an epidemic and can result in large human and financial losses. To better respond to the spread of infectious disease and take measures for its control, the public health authorities need models and simulations to study the spread of such diseases. In this paper, an agent-based simulation engine is presented that models the spread of infectious diseases in the population. The simulation takes as an input the human-to-human interactions, population dynamics, disease transmissibility and disease states and shows the spread of disease over time. The simulation engine supports non-pharmaceutical interventions and shows its impact on the disease spread across locations. A unique feature of this tool is that it is generic; therefore, it can simulate a wide variety of infectious disease models (SIR), susceptible-infectious-susceptible (SIS) and susceptible-infectious (SI). The proposed simulation engine will help the policy-makers and public health authorities study the behavior of disease spreading; thus, allowing for better planning.

An Improved Agent-Based Model Using Discrete Event Simulation for Nonpharmaceutical Interventions

“…The ABM using the discrete event simulation method is proposed due to its industry recognition [13][14][15][16][17][18][19][20], [38][39] .…”

“…Crooks [16] used data from a refugee camp and geographic information science (GIS) elevation data for an ABM on the spread of cholera. Waleed [18] presented an agent-based simulation engine that uses human-to-human interactions, population dynamics, disease transmissibility and disease states as inputs, which can model the spread of infectious diseases in a population. Gopalan [21] developed an agentbased simulation framework in Python and used it to compare the performance of three testing policies, i.e., Random Symptomatic Testing (RST), Contact Tracing (CT), and a new Location-Based Testing (LBT) policy.…”

“…Notably, however, their models all run in supercomputing centers. Actually, "agent-based models are computationally intensive" is industry-recognized [13][14][15][16][17][18][19][20], [38][39] , which is the primary reason for the work presented in this paper. In terms of the nature of the model, an ABM is a pseudodistributed system [27] , i.e., it is a collection of objects that are concurrently active and communicate with each other (despite the concurrency being simulated by the engine).…”

Experimenting with Agent-Based Model Simulation Tools

“…Nowadays, ABMs are widely used by researchers in various disciplines, allowing them to test and assess new theories and observe and notice mechanisms never considered before. Applications domains for ABMs include social science [6], economics [7,8], climate change [9,10], epidemiology [11,12], transportation and logistics [13,14], and many others [2,5,15]. The widespread use of ABMs in these various application fields created the need for tools to easily and quickly develop simulations without requiring significant coding skills.…”