COVID-ABS: An Agent-Based Model of COVID-19 Epidemic to Simulate Health and Economic Effects of Social Distancing Interventions

Abstract

The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has directly impacted the public health and economy worldwide. To overcome this problem, countries have adopted different policies and non-pharmaceutical interventions for controlling the spread of the virus. This paper proposes the COVID-ABS, a new SEIR (Susceptible-Exposed-Infected-Recovered) agent-based model that aims to simulate the pandemic dynamics using a society of agents emulating people, business and government. Seven different scenarios of social distancing interventions were analyzed, with varying epidemiological and economic effects: (1) do nothing, (2) lockdown, (3) conditional lockdown, (4) vertical isolation, (5) partial isolation, (6) use of face masks, and (7) use of face masks together with 50% of adhesion to social isolation. In the impossibility of implementing scenarios with lockdown, which present the lowest number of deaths and highest impact on the economy, scenarios combining the use of face masks and partial isolation can be the more realistic for implementation in terms of social cooperation. The COVID-ABS model was implemented in Python programming language, with source code publicly available. The model can be easily extended to other societies by changing the input parameters, as well as allowing the creation of a multitude of other scenarios. Therefore, it is a useful tool to assist politicians and health authorities to plan their actions against the COVID-19 epidemic.

37 pages, 17 figures

References in corpus (8)

• Mathematical Modeling of COVID-19 Transmission Dynamics with a Case Study of Wuhan
• Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil
• Mobility Changes in Response to COVID-19
• Universal Masking is Urgent in the COVID-19 Pandemic: SEIR and Agent Based Models, Empirical Validation, Policy Recommendations
• Accurate closed-form solution of the SIR epidemic model
• Strong correlations between power-law growth of COVID-19 in four continents and the inefficiency of soft quarantine strategies
• Comparing Stochastic Differential Equations and Agent-Based Modelling and Simulation for Early-stage Cancer
• The propagation of the economic impact through supply chains: The case of a mega-city lockdown against the spread of COVID-19

Cited by in corpus (26)

• Non-pharmaceutical interventions during the COVID-19 pandemic: a rapid review
• Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review
• Agent-based modeling of COVID-19 outbreaks for New York state, UK and Novosibirsk region
• EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control
• Simulation-free estimation of an individual-based SEIR model for evaluating nonpharmaceutical interventions with an application to COVID-19 in Iowa
• Inference in epidemiological agent-based models using ensemble-based data assimilation
• A non-homogeneous Markov early epidemic growth dynamics model. Application to the SARS-CoV-2 pandemic
• Fuzzy Expert Systems for Prediction of ICU Admission in Patients with COVID-19
• Efficacy versus abundancy: Comparing vaccination schemes
• Predicting the diversity of early epidemic spread on networks
• The Role of Masks in Mitigating Viral Spread on Networks
• Global and Local Reduced Models for Interacting, Heterogeneous Agents
• Simplified model on the timing of easing the lockdown
• Modeling the interplay between epidemics and regional socio-economics
• Corona Games: Masks, Social Distancing and Mechanism Design
• Modelling the COVID-19 virus evolution with Incremental Machine Learning
• Recent trends in socio-epidemic modelling: behaviours and their determinants
• ArchABM: an agent-based simulator of human interaction with the built environment. $C{O}_2$ and viral load analysis for indoor air quality
• Whom to Test? Active Sampling Strategies for Managing COVID-19
• The mathematics of contagious diseases and their limitations in forecasting
• A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learning
• SARS-CoV-2 Dissemination using a Network of the United States Counties
• A Multi-Task Learning Framework for COVID-19 Monitoring and Prediction of PPE Demand in Community Health Centres
• Fuzzy Multi-Agent Simulation of COVID-19 Pandemic Spreading
• Endogenous viral mutations, evolutionary selection, and containment policy design
• City-scale Simulation of Covid-19 Pandemic and Intervention Policies using Agent-based Modelling