An agent-based computational framework for simulation of global pandemic and social response on planet X
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ORIGINAL PAPER
An agent-based computational framework for simulation of global pandemic and social response on planet X T. I. Zohdi1 Received: 24 June 2020 / Accepted: 12 July 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The increase in readily available computational power raises the possibility that direct agent-based modeling can play a key role in the analysis of epidemiological population dynamics. Specifically, the objective of this work is to develop a robust agent-based computational framework to investigate the emergent structure of Susceptible-Infected-Removed/Recovered (SIR)-type populations and variants thereof, on a global planetary scale. To accomplish this objective, we develop a planetwide model based on interaction between discrete entities (agents), where each agent on the surface of the planet is initially uninfected. Infections are then seeded on the planet in localized regions. Contracting an infection depends on the characteristics of each agent—i.e. their susceptibility and contact with the seeded, infected agents. Agent mobility on the planet is dictated by social policies, for example such as “shelter in place”, “complete lockdown”, etc. The global population is then allowed to evolve according to infected states of agents, over many time periods, leading to an SIR population. The work illustrates the construction of the computational framework and the relatively straightforward application with direct, non-phenomenological, input data. Numerical examples are provided to illustrate the model construction and the results of such an approach. Keywords Pandemic · Agent-based · Simulation
1 Introduction The COVID-19 pandemic of 2020 has led to a significant increase in research in the area of modeling and simulation of infectious diseases. There are numerous aspects associated with this epoch-changing event that is now facing humanity. Macroscale (planetary) disease propagation, in addition to the related issues of logistical and political responses, is a central issue. Accordingly, the objective of this work is to develop a computationally-amenable agent-based model to investigate the behavior of an infected population by directly working at the individual-to-individual level of interaction. The wide-spread availability of computational power now raises the possibility that robust agent-based modeling can play a significant role in the analysis of infectious disease propagation. The key feature of agent-based modeling is
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T. I. Zohdi [email protected] Department of Mechanical Engineering, University of California, 6195 Etcheverry Hall, Berkeley, CA 94720-1740, USA
that discrete entities (agents) are used to directly represent a population (Fig. 1). This enables the detailed analysis of epidemiological population dynamics and the ability to investigate the emergent structure SIR-type (SusceptibleInfected-Removed/Recovered) populations, as well more complex extensions, due to initially localized infections within a population on a global planetary scale
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