Employing Agent-based Modeling to Mitigate Infectious Disease Spread

2021 ◽  
Vol 65 (1) ◽  
pp. 390-394
Author(s):  
Michael Schwartz ◽  
Paul Oppold ◽  
Boniface Noyongoyo ◽  
Peter Hancock
Keyword(s):  
Infectious Disease ◽  
Viral Infections ◽  
Agent Based Modeling ◽  
Disease Spread ◽  
Effective Measure ◽  
Agent Based ◽  
Personal Learning ◽  
Business Operations ◽  
Interpersonal Factors ◽  
Reduce Risk

The current pandemic has tested systems in place as to how to fight infectious diseases in many countries. COVID-19 spreads quickly and is deadly. However, it can be controlled through different measures such as physical distancing. The current project examines through simulation model of the UCF Global building the potential spread of an infectious disease via AnyLogic Personal Learning Edition (PLE) 8.7.0 on a laptop running Windows 10. The goal is to determine the environmental and interpersonal factors that could be modified to reduce risk of illness while maintaining typical business operations. Multiple experiments were ran to see when there is a potential change in infection and spread rate. Results show that increases occur with density between 400 and 500. To curtail the spread it is therefore important to limit contact through physical distancing for it has been proven an effective measure for reducing the spread of viral infections.

scholarly journals Examining the Interplay between Face Mask Usage, Asymptomatic Transmission, and Social Distancing on the Spread of COVID-19

2020 ◽  
Author(s):  
Adam Catching ◽  
Sara Capponi ◽  
Ming Te Yeh ◽  
Simone Bianco ◽  
Raul Andino
Keyword(s):  
Virus Transmission ◽  
Face Mask ◽  
Agent Based Modeling ◽  
Disease Spread ◽  
Social Functions ◽  
Agent Based ◽  
Social Distancing ◽  
Worldwide Population ◽  
Asymptomatic Individuals ◽  
High Virus

AbstractThe COVID-19 global crisis is facilitated by high virus transmission rates and high percentages of asymptomatic and presymptomatic infected individuals. Containing the pandemic hinged on combinations of social distancing and face mask use. Here we examine the efficacy of these measures, using an agent-based modeling approach that evaluates face masks and social distancing in realistic confined spaces scenarios. By explicitly considering different fractions of asymptomatic individuals, as well as a realistic hypothesis of face mask protection during inhaling and exhaling, we find that face masks are more effective than social distancing in curbing the infection. Importantly, combining face masks with even moderate social distancing provides optimal protection. The finding that widespread usage of face masks limits COVID-19 outbreaks can inform policies to reopening of social functions.Author summaryThe COVID-19 outbreak has created an enormous burden on the worldwide population. Among the various ways of preventing the spread of the virus, face masks have been proposed as a main way of reducing transmission. Yet, the interplay between the usage of face mask and other forms of Non-Pharmaceutical Intervention is still not completely clear. In this paper we introduce a stochastic individual-based model which aims at producing realistic scenarios of disease spread when mask wearing with different inward and outward efficacy and social distancing are enforced. The model elucidates the conditions which make the two forms of intervention synergistic in preventing the spread of the disease.

scholarly journals Adapting an Agent-Based Model of Infectious Disease Spread in an Irish County to COVID-19

Systems ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 41
Author(s):  
Elizabeth Hunter ◽  
John D. Kelleher
Keyword(s):  
Complex System ◽  
Infectious Disease ◽  
Emerging Infectious Diseases ◽  
Disease Outbreaks ◽  
Basic Reproductive Number ◽  
Disease Spread ◽  
Reproductive Number ◽  
Agent Based Model ◽  
School Closures ◽  
Agent Based

The dynamics that lead to the spread of an infectious disease through a population can be characterized as a complex system. One way to model such a system, in order to improve preparedness, and learn more about how an infectious disease, such as COVID-19, might spread through a population, is agent-based epidemiological modelling. When a pandemic is caused by an emerging disease, it takes time to develop a completely new model that captures the complexity of the system. In this paper, we discuss adapting an existing agent-based model for the spread of measles in Ireland to simulate the spread of COVID-19. The model already captures the population structure and commuting patterns of the Irish population, and therefore, once adapted to COVID-19, it can provide important insight on the pandemic, specifically in Ireland. We first investigate the different disease parameters that need to be adjusted to simulate the spread of COVID-19 instead of measles and then run a set of experiments initially comparing the model output for our original measles model with that from the adjusted COVID-19 model. We then report on experiments on how the different values of the basic reproductive number, R0, influence the simulated outbreaks, and find that our model behaves as expected: the higher the R0, the more agents are infected. Then, we demonstrate how different intervention strategies, such as vaccinations and school closures, influence the spread of measles and COVID-19 and how we can simulate real pandemic timings and interventions in our model. We show that with the same society, environment and transportation components among the different disease components lead to very different results for the two diseases, and that our COVID-19 model, when run for Leitrim County, Ireland, predicts a similar outbreak length to a real outbreak in Leitrim County, Ireland, but the model results in a higher number of infected agents compared to the real outbreak. This difference in cases is most likely due to identifying all cases of COVID-19 in the model opposed to only those tested. Once an agent-based model is created to simulate a specific complex system or society, the disease component can be adapted to simulate different infectious disease outbreaks. This makes agent-based models a powerful tool that can be used to help understand the spread of new and emerging infectious diseases.

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

2020 ◽  
Vol 10 (21) ◽  
pp. 7745
Author(s):  
Muhammad Waleed ◽  
Tai-Won Um ◽  
Tariq Kamal ◽  
Aftab Khan ◽  
Zaka Ullah Zahid
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Infectious Diseases ◽  
Close Contact ◽  
Disease Spread ◽  
Agent Based ◽  
Disease States ◽  
Simulation Engine ◽  
Health Authorities ◽  
Public Health Authorities

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.

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