scholarly journals Investigating Dynamics of COVID-19 Spread and Containment with Agent-Based Modeling

2021 ◽  
Vol 11 (12) ◽  
pp. 5367
Author(s):  
Amirarsalan Rajabi ◽  
Alexander V. Mantzaris ◽  
Ece C. Mutlu ◽  
Ozlem O. Garibay
Keyword(s):  
Disease Model ◽  
Agent Based Modeling ◽  
Contact Tracing ◽  
Test Bed ◽  
Policy Makers ◽  
Agent Based ◽  
Contact Behavior ◽  
Epidemic Curve ◽  
Behavioral Adaptations ◽  
Travel Restrictions

Governments, policy makers, and officials around the globe are working to mitigate the effects of the COVID-19 pandemic by making decisions that strive to save the most lives and impose the least economic costs. Making these decisions require comprehensive understanding of the dynamics by which the disease spreads. In traditional epidemiological models, individuals do not adapt their contact behavior during an epidemic, yet adaptive behavior is well documented (i.e., fear-induced social distancing). In this work we revisit Epstein’s “coupled contagion dynamics of fear and disease” model in order to extend and adapt it to explore fear-driven behavioral adaptations and their impact on efforts to combat the COVID-19 pandemic. The inclusion of contact behavior adaptation endows the resulting model with a rich dynamics that under certain conditions reproduce endogenously multiple waves of infection. We show that the model provides an appropriate test bed for different containment strategies such as: testing with contact tracing and travel restrictions. The results show that while both strategies could result in flattening the epidemic curve and a significant reduction of the maximum number of infected individuals; testing should be applied along with tracing previous contacts of the tested individuals to be effective. The results show how the curve is flattened with testing partnered with contact tracing, and the imposition of travel restrictions.

scholarly journals Investigating dynamics of COVID-19 spread and containment with agent-based modeling

2020 ◽  
Author(s):  
Amirarsalan Rajabi ◽  
Alexander V. Mantzaris ◽  
Ece C. Mutlu ◽  
Ivan Garibay
Keyword(s):  
Agent Based Modeling ◽  
Contact Tracing ◽  
Model Description ◽  
Test Bed ◽  
Comprehensive Understanding ◽  
Policy Makers ◽  
Agent Based ◽  
Epidemic Curve ◽  
Travel Restrictions ◽  
Travel Restriction

AbstractGovernments, policy makers and officials around the globe are trying to mitigate the effects and progress of the COVID-19 pandemic by making decisions which will save the most lives and impose the least costs. Making these decisions needs a comprehensive understanding about the dynamics by which the disease spreads. In this work, we propose an epidemic agent-based model that simulates the spread of the disease. We show that the model is able to generate an important aspect of the pandemic: multiple waves of infection. A key point in the model description is the aspect of ’fear’ which can govern how agents behave under different conditions. We also show that the model provides an appropriate test-bed to apply different containment strategies and this work presents the results of applying two such strategies: testing, contact tracing, and travel restriction. The results show that while both strategies could result in flattening the epidemic curve and significantly reduce the maximum number of infected individuals; testing should be applied along with tracing previous contacts of the tested individuals to be effective. The results show how the curve is flattened with testing partnered with contact tracing, and the imposition of travel restrictions.

Agent-based Modeling for Financial Markets

2018 ◽  
Author(s):  
Giulia Iori ◽  
James Porter
Keyword(s):  
Financial Markets ◽  
Data Gathering ◽  
Simple Zero ◽  
Agent Based Modeling ◽  
Policy Makers ◽  
Agent Based ◽  
Macro Scale ◽  
Learning Capabilities ◽  
Active Data ◽  
Zero Intelligence

This chapter discusses a step in the evolution of agent-based model (ABM) research in finance. Agent-based modeling has concentrated on the development of stylized market models, which have been extremely useful for understanding how complex macro-scale phenomena emerge from micro-rules. In order to further develop ABMs from proof of concept into robust tools for policy makers, to control and forecast complex real-world financial markets, it is essential to permit agents to behave as active data-gathering decision makers with sophisticated learning capabilities. The main focus of this chapter is to show how agent based models (ABMs) in financial markets have evolved from simple zero- intelligence agents that follow arbitrary rules of thumb into sophisticated agents described by microfounded rules of behavior. The chapter then briefly looks at the challenges posed by and approaches to model calibration and provides examples of how ABMs have been successful at offering useful insights for policy making.

scholarly journals Policy Modeling and Applications: State-of-the-Art and Perspectives

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Bernardo A. Furtado ◽  
Miguel A. Fuentes ◽  
Claudio J. Tessone
Keyword(s):  
Public Policy ◽  
Evolutionary Game ◽  
Network Models ◽  
Complexity Science ◽  
Agent Based Modeling ◽  
Urban Systems ◽  
Policy Makers ◽  
Agent Based ◽  
Corporate Strategies ◽  
Actual Field

The range of application of methodologies of complexity science, interdisciplinary by nature, has spread even more broadly across disciplines after the dawn of this century. Specifically, applications to public policy and corporate strategies have proliferated in tandem. This paper reviews the most used complex systems methodologies with an emphasis on public policy. We briefly present examples, pros, and cons of agent-based modeling, network models, dynamical systems, data mining, and evolutionary game theory. Further, we illustrate some specific experiences of large applied projects in macroeconomics, urban systems, and infrastructure planning. We argue that agent-based modeling has established itself as a strong tool within scientific realm. However, adoption by policy-makers is still scarce. Considering the huge amount of exemplary, successful applications of complexity science across the most varied disciplines, we believe policy is ready to become an actual field of detailed and useful applications.

Creativity in a theory of entrepreneurship

2019 ◽  
Vol 8 (4) ◽  
pp. 442-469 ◽  
Author(s):  
James Lee Caton
Keyword(s):  
Computational Simulation ◽  
Building Blocks ◽  
Agent Based Modeling ◽  
Competitive System ◽  
Policy Makers ◽  
Content Type ◽  
Agent Based ◽  
The Social ◽  
New Knowledge ◽  
The Individual

Purpose The purpose of this paper is to integrate a detailed theory of perception and action with a theory of entrepreneurship. It considers how new knowledge is developed by entrepreneurs and how the level of creativity is regulated by a competitive system. It also shows how new knowledge may create value for the innovator as well as for other entrepreneurs in the system. Design/methodology/approach The theory builds on existing literature on creativity and entrepreneurship. It considers how transformation of mental technologies occurs at the individual and system levels, and how this transformation influences value creation. Findings Under a competitive system, the level of creativity is regulated by the need for new ways of doing things. Periods of crisis wherein old means of coordination begin to fail often precipitate an increase in creativity, whereas a lack of crisis often allows the system to settle to a stable equilibrium with lower levels of creativity. Research limitations/implications The combination of methodology and methods facilitates a description of discrete building blocks that guide perception and enable creativity. This framing enables consideration of how a changing set of knowledge interacts with a system of prices. Practical implications Policy makers must take care not to encumber markets with costs that unnecessarily constrain creativity, as experimentation makes the economic system robust to shocks. Social implications This work provides a framing of cognition that allows for a linking of agent understanding that permits explicit description of coordination between agents. It relates perception and ends of the individual to constraints enforced by the social system. Originality/value As far as the author is concerned, no other work ties together a robust framing of cognition with computational simulation of market processes. This research deepens understanding in multiple fields, most prominently for agent-based modeling and entrepreneurship.

scholarly journals Estimated Time to Restoration of Hurricane Sandy in a Future Climate

2020 ◽  
Vol 12 (16) ◽  
pp. 6502
Author(s):  
Tara C. Walsh ◽  
David W. Wanik ◽  
Emmanouil N. Anagnostou ◽  
Jonathan E. Mellor
Keyword(s):  
Critical Infrastructure ◽  
Hurricane Sandy ◽  
Agent Based Modeling ◽  
Power Outage ◽  
Policy Makers ◽  
Agent Based ◽  
Emergency Managers ◽  
Past Experiences ◽  
The Impact ◽  
The Relationship

Power outage restoration following extreme storms is a complicated process that couples engineering processes and human decisions. Emergency managers typically rely on past experiences and have limited access to computer simulations to aid in decision-making. Climate scientists predict that although hurricane frequency may decrease, the intensity of storms may increase. Increased damage from hurricanes will result in new restoration challenges that emergency managers may not have experience solving. Our study uses agent-based modeling (ABM) to determine how restoration might have been impacted for 30 different scenarios of Hurricane Sandy for a climate in 2112 (Sandy2112). These Sandy2112 scenarios were obtained from a previous study that modeled how outages from Hurricane Sandy in 2012 might have been affected in the future as climate change intensified both wind and precipitation hazards. As the number of outages increases, so does the expected estimated time to restoration for each storm. The impact of increasing crews is also studied to determine the relationship between the number of crews and outage durations (or restoration curves). Both the number of outages and the number of crews impact the variability in time to restoration. Our results can help emergency managers and policy makers plan for future hurricanes that are likely to become stronger and more impactful to critical infrastructure.

scholarly journals An Agent Based Modeling of COVID-19: Validation, Analysis, and Recommendations

2020 ◽  
Author(s):  
Md. Salman Shamil ◽  
Farhanaz Farheen ◽  
Nabil Ibtehaz ◽  
Irtesam Mahmud Khan ◽  
M. Sohel Rahman
Keyword(s):  
New York ◽  
New York City ◽  
Emergency Service ◽  
Service Providers ◽  
Real Data ◽  
Agent Based Modeling ◽  
Contact Tracing ◽  
Agent Based Model ◽  
Agent Based ◽  
The City

AbstractThe Coronavirus disease 2019 (COVID-19) has resulted in an ongoing pandemic worldwide. Countries have adopted Non-pharmaceutical Interventions (NPI) to slow down the spread. This study proposes an Agent Based Model that simulates the spread of COVID-19 among the inhabitants of a city. The Agent Based Model can be accommodated for any location by integrating parameters specific to the city. The simulation gives the number of daily confirmed cases. Considering each person as an agent susceptible to COVID-19, the model causes infected individuals to transmit the disease via various actions performed every hour. The model is validated by comparing the simulation to the real data of Ford county, Kansas, USA. Different interventions including contact tracing are applied on a scaled down version of New York city, USA and the parameters that lead to a controlled epidemic are determined. Our experiments suggest that contact tracing via smartphones with more than 60% of the population owning a smartphone combined with a city-wide lock-down results in the effective reproduction number (Rt) to fall below 1 within three weeks of intervention. In the case of 75% or more smartphone users, new infections are eliminated and the spread is contained within three months of intervention. Contact tracing accompanied with early lock-down can suppress the epidemic growth of COVID-19 completely with sufficient smartphone owners. In places where it is difficult to ensure a high percentage of smartphone ownership, tracing only emergency service providers during a lock-down can go a long way to contain the spread. No particular funding was available for this project.

scholarly journals Increasing efficacy of contact-tracing applications by user referrals and stricter quarantining

2020 ◽  
Author(s):  
Leslie Ann Goldberg ◽  
Joost Jorritsma ◽  
Júlia Komjáthy ◽  
John Lapinskas
Keyword(s):  
Mobile Phone ◽  
Random Graphs ◽  
Contact Tracing ◽  
Epidemiological Models ◽  
Agent Based ◽  
Inhomogeneous Random Graphs ◽  
Travel Restrictions ◽  
Geometric Space ◽  
Phone Contact

AbstractWe study the effects of two mechanisms which increase the efficacy of contact-tracing applications (CTAs) such as the mobile phone contact-tracing applications that have been used during the COVID-19 epidemic. The first mechanism is the introduction of user referrals. We compare four scenarios for the uptake of CTAs — (1) the p% of individuals that use the CTA are chosen randomly, (2) a smaller initial set of randomly-chosen users each refer a contact to use the CTA, achieving p% in total, (3) a small initial set of randomly-chosen users each refer around half of their contacts to use the CTA, achieving p% in total, and (4) for comparison, an idealised scenario in which the p% of the population that uses the CTA is the p% with the most contacts. Using agent-based epidemiological models incorporating a geometric space, we find that, even when the uptake percentage p% is small, CTAs are an effective tool for mitigating the spread of the epidemic in all scenarios. Moreover, user referrals significantly improve efficacy. In addition, it turns out that user referrals reduce the yearly quarantine load. The second mechanism for increasing the efficacy of CTAs is tuning the severity of quarantine measures. Our modelling shows that using CTAs with mild quarantine measures is effective in reducing the maximum hospital load and the number of people who become ill, but leads to a relatively high quarantine load, which may cause economic disruption. Fortunately, under stricter quarantine measures, the advantages are maintained but the quarantine load is reduced. Our models incorporate geometric inhomogeneous random graphs to study the effects of the presence of super-spreaders and of the absence of long-distant contacts (e.g., through travel restrictions) on our conclusions.

scholarly journals Increasing efficacy of contact-tracing applications by user referrals and stricter quarantining

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250435
Author(s):  
Leslie Ann Goldberg ◽  
Joost Jorritsma ◽  
Júlia Komjáthy ◽  
John Lapinskas
Keyword(s):  
Mobile Phone ◽  
Random Graphs ◽  
Contact Tracing ◽  
Epidemiological Models ◽  
Agent Based ◽  
Inhomogeneous Random Graphs ◽  
Travel Restrictions ◽  
Geometric Space ◽  
Phone Contact

We study the effects of two mechanisms which increase the efficacy of contact-tracing applications (CTAs) such as the mobile phone contact-tracing applications that have been used during the COVID-19 epidemic. The first mechanism is the introduction of user referrals. We compare four scenarios for the uptake of CTAs—(1) the p% of individuals that use the CTA are chosen randomly, (2) a smaller initial set of randomly-chosen users each refer a contact to use the CTA, achieving p% in total, (3) a small initial set of randomly-chosen users each refer around half of their contacts to use the CTA, achieving p% in total, and (4) for comparison, an idealised scenario in which the p% of the population that uses the CTA is the p% with the most contacts. Using agent-based epidemiological models incorporating a geometric space, we find that, even when the uptake percentage p% is small, CTAs are an effective tool for mitigating the spread of the epidemic in all scenarios. Moreover, user referrals significantly improve efficacy. In addition, it turns out that user referrals reduce the quarantine load. The second mechanism for increasing the efficacy of CTAs is tuning the severity of quarantine measures. Our modelling shows that using CTAs with mild quarantine measures is effective in reducing the maximum hospital load and the number of people who become ill, but leads to a relatively high quarantine load, which may cause economic disruption. Fortunately, under stricter quarantine measures, the advantages are maintained but the quarantine load is reduced. Our models incorporate geometric inhomogeneous random graphs to study the effects of the presence of super-spreaders and of the absence of long-distant contacts (e.g., through travel restrictions) on our conclusions.

scholarly journals Differential impacts of contact tracing and lockdowns on outbreak size in COVID-19 model applied to China

2020 ◽  
Author(s):  
Cameron J. Browne ◽  
Hayriye Gulbudak ◽  
Joshua C. Macdonald
Keyword(s):  
Disease Model ◽  
Critical Time ◽  
Fundamental Principle ◽  
Contact Tracing ◽  
Final Size ◽  
Infectious Disease Model ◽  
Epidemic Curve ◽  
Time Period ◽  
Current Infection ◽  
Outbreak Size

Rapid growth of the COVID-19 epidemic in China induced extensive efforts of contact tracing and social-distancing/lockdowns, which quickly contained the outbreak and has been replicated to varying degrees around the world. We construct a novel infectious disease model incorporating these distinct quarantine measures (contact tracing and self-quarantine) as reactionary interventions dependent on current infection levels. Derivation of the final outbreak size leads to a simple inverse proportionality relationship with self-quarantine rate, revealing a fundamental principle of exponentially increasing cumulative cases when delaying mass quarantine or lockdown measures beyond a critical time period. In contrast, contact tracing results in a proportional reduction in reproduction number, flattening the epidemic curve but only having sizable impact on final size when a large proportion of contacts are “perfectly” traced. We fit the mathematical model to data from China on reported cases and quarantined contacts, finding that lockdowns had an overwhelming influence on outbreak size and duration, whereas contact tracing played a role in reducing peak number of infected. Sensitivity analysis and simulations under different re-opening scenarios illustrate the differential effects that responsive contact tracing and lockdowns can have on current and second wave outbreaks.

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