scholarly journals Impact of Timing of and Adherence to Social Distancing Measures on COVID-19 Burden in the US: A Simulation Modeling Approach

2020 ◽  
Author(s):  
Oguzhan Alagoz ◽  
Ajay K. Sethi ◽  
Brian W. Patterson ◽  
Matthew Churpek ◽  
Nasia Safdar
Keyword(s):  
New York ◽  
Simulation Model ◽  
Disease Transmission ◽  
Major Effect ◽  
Agent Based Simulation ◽  
Population Demographics ◽  
Agent Based ◽  
Social Distancing ◽  
The People ◽  
The Impact

ABSTRACTBackgroundAcross the U.S., various social distancing measures were implemented to control COVID-19 pandemic. However, there is uncertainty in the effectiveness of such measures for specific regions with varying population demographics and different levels of adherence to social distancing. The objective of this paper is to determine the impact of social distancing measures in unique regions.MethodsWe developed COVid-19 Agent-based simulation Model (COVAM), an agent-based simulation model (ABM) that represents the social network and interactions among the people in a region considering population demographics, limited testing availability, imported infections from outside of the region, asymptomatic disease transmission, and adherence to social distancing measures. We adopted COVAM to represent COVID-19-associated events in Dane County, Wisconsin, Milwaukee metropolitan area, and New York City (NYC). We used COVAM to evaluate the impact of three different aspects of social distancing: 1) Adherence to social distancing measures; 2) timing of implementing social distancing; and 3) timing of easing social distancing.ResultsWe found that the timing of social distancing and adherence level had a major effect on COVID-19 occurrence. For example, in NYC, implementing social distancing measures on March 5, 2020 instead of March 12, 2020 would have reduced the total number of confirmed cases from 191,984 to 43,968 as of May 30, whereas a 1-week delay in implementing such measures could have increased the number of confirmed cases to 1,299,420. Easing social distancing measures on June 1, 2020 instead of June 15, 2020 in NYC would increase the total number of confirmed cases from 275,587 to 379,858 as of July 31.ConclusionThe timing of implementing social distancing measures, adherence to the measures, and timing of their easing have major effects on the number of COVID-19 cases.Primary Funding SourceNational Institute of Allergy and Infectious Diseases Institute

scholarly journals Application of Agent-based Modeling for Cultural Information-based Decisions Making

2020 ◽  
Vol 88 ◽  
pp. 8-28
Author(s):  
Rimvydas Laužikas ◽  
Darius Plikynas ◽  
Vytautas Dulskis ◽  
Leonidas Sakalauskas ◽  
Arūnas Miliauskas
Keyword(s):  
Social Capital ◽  
Simulation Model ◽  
Cultural Participation ◽  
Agent Based Simulation ◽  
Research Issues ◽  
Agent Based ◽  
Cultural Events ◽  
The Social ◽  
Cultural Processes ◽  
The Impact

The impact of cultural processes on personal and social changes is one of the important research issues not only in contemporary social sciences but also for simulation of future development scenarios and evidence-based policy decision making. In the context of the theoretical concept of cultural values, based on the system theory and theory of social capital, the impact of cultural events could be analyzed and simulated by focussing on the construction/deconstruction of social capital, which takes place throughout the actor’s cultural participation. The main goal of this research is the development of measuring metrics, and agent-based simulation model aimed at investigation of the social impact of cultural processes.  This paper provides new insights of modeling the social capital changes in a society and its groups, depending on cultural participation. The proposed measurement metrics provide the measurement facility of three key components: actors, cultural events and events flow and social capital. It provides the initial proof of concept simulation results, - simplified agent-based simulation model showcase. The NetLogo MAS platform is used as a simulation environment.  

Agent Based Model to Analyze Consumer Behavior in Consuming the Electricity Energy

2015 ◽  
Vol 72 (4) ◽  
Author(s):  
Erma Suryani ◽  
Rully Agus Hendrawan ◽  
Umi Salama ◽  
Lily Puspa Dewi
Keyword(s):  
Consumer Behavior ◽  
Simulation Model ◽  
Environmental Awareness ◽  
Electricity Consumption ◽  
Point Of View ◽  
Agent Based Model ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Save Energy ◽  
The Impact

Several studies have been conducted regarding save energy in consuming the electricity through the simple changes in routines and habits. In the case of electricity consumption, consumer behavior might influenced by several factors such as consumer profession, season, and environmental awareness. In this paper, we developed an Agent Based Model (ABM) to analyze the behavior of different agents in consuming the electricity energy for each type of profession (agent) as well as their interaction with the environment. This paper demonstrates a prototype agent based simulation model to estimate the electricity consumption based on the existing condition and some scenarios to reduce the electricity consumption from consumer point of view. From the scenario results, we analyzed the impact of the save energy to increase the electrification ratio. 

scholarly journals Investigation and prediction of open source software evolution using automated parameter mining for agent-based simulation

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
Daniel Honsel ◽  
Verena Herbold ◽  
Stephan Waack ◽  
Jens Grabowski
Keyword(s):  
Simulation Model ◽  
Software Evolution ◽  
Prediction Models ◽  
Structural Evolution ◽  
Simulation Models ◽  
Agent Based Simulation ◽  
Specific Data ◽  
Agent Based ◽  
Project Data ◽  
The Impact

AbstractTo guide software development, the estimation of the impact of decision making on the development process can be helpful in planning. For this estimation, often prediction models are used which can be learned from project data. In this paper, an approach for the usage of agent-based simulation for the prediction of software evolution trends is presented. The specialty of the proposed approach lies in the automated parameter estimation for the instantiation of project-specific simulation models. We want to assess how well a baseline model using average (commit) behavior of the agents (i.e., the developers) performs compared to models where different amount of project-specific data is fed into the simulation model. The approach involves the interplay between the mining framework and simulation framework. Parameters to be estimated include, e.g., file change probabilities of developers and the team constellation reflecting different developer roles. The structural evolution of software projects is observed using change coupling graphs based on common file changes. For the validation of simulation results, we compare empirical with simulated results. Our results showed that an average simulation model can mimic general project growth trends like the number of commits and files well and thus, can help project managers in, e.g., controlling the onboarding of developers. Besides, the simulated co-change evolution could be improved significantly using project-specific data.

scholarly journals INFEKTA—An agent-based model for transmission of infectious diseases: The COVID-19 case in Bogotá, Colombia

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0245787
Author(s):  
Jonatan Gomez ◽  
Jeisson Prieto ◽  
Elizabeth Leon ◽  
Arles Rodríguez
Keyword(s):  
Infectious Diseases ◽  
Disease Transmission ◽  
Computational Models ◽  
Compartmental Model ◽  
Transmission Dynamics ◽  
Agent Based Model ◽  
Agent Based ◽  
Social Distancing ◽  
The Impact ◽  
The Town

The transmission dynamics of the coronavirus—COVID-19—have challenged humankind at almost every level. Currently, research groups around the globe are trying to figure out such transmission dynamics under special conditions such as separation policies enforced by governments. Mathematical and computational models, like the compartmental model or the agent-based model, are being used for this purpose. This paper proposes an agent-based model, called INFEKTA, for simulating the transmission of infectious diseases, not only the COVID-19, under social distancing policies. INFEKTA combines the transmission dynamic of a specific disease, (according to parameters found in the literature) with demographic information (population density, age, and genre of individuals) of geopolitical regions of the real town or city under study. Agents (virtual persons) can move, according to its mobility routines and the enforced social distancing policy, on a complex network of accessible places defined over an Euclidean space representing the town or city. The transmission dynamics of the COVID-19 under different social distancing policies in Bogotá city, the capital of Colombia, is simulated using INFEKTA with one million virtual persons. A sensitivity analysis of the impact of social distancing policies indicates that it is possible to establish a ‘medium’ (i.e., close 40% of the places) social distancing policy to achieve a significant reduction in the disease transmission.

scholarly journals 2418. Determining the Impact of Visitor Contact Precautions on Hospital-Onset Clostridioides difficile Infection Rates: An Agent-Based Simulation Modeling Approach

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S835-S835
Author(s):  
Oguzhan Alagoz ◽  
Elizabeth Scaria ◽  
Anna K Barker ◽  
Nasia Safdar
Keyword(s):  
Hand Hygiene ◽  
Simulation Model ◽  
Healthcare Worker ◽  
Healthcare Workers ◽  
Agent Based Simulation ◽  
Environmental Cleaning ◽  
Agent Based ◽  
Contact Precautions ◽  
Clostridioides Difficile ◽  
The Impact

Abstract Background Visitor contact precautions (VCP) have been suggested to reduce the transmission of Clostridiodes difficile at healthcare institutions. However, there are no data describing the impact of VCP on hospital-acquired C. difficile infection (HO-CDI) rates. Enforcing VCP for CDI control is also controversial, as VCP are poorly implemented and highly variable. Methods We developed an agent-based simulation model of C. difficile transmission at a model 200-bed acute-care adult hospital. Our agent-based simulation model represented interactions among the physicians, nurses, patients, visitors, and physical environment. We used the agent-based simulation model to evaluate the impact of VCP on reducing HO-CDI considering many different hospital settings and various assumptions on patient susceptibility, adherence rates to other infection control practices, interactions between healthcare workers and patients. Results VCP did not reduce the CDC-defined HO-CDI rates by more than 1% in any of the tested scenarios and hospital settings. Increasing the adherence of hand hygiene of healthcare workers to 56% from a baseline estimate of 55%, or compliance to room cleaning to 50% from a baseline estimate of 47% have led to higher rates of reduction in CDI compared with VCP. Conclusion This is the first mathematical model to quantify the reduction in HO-CDI with VCP. The agent-based simulation model suggests that the impact of VCP on hospital-onset CDI is minimal and hospitals can achieve a higher rate of reduction for HO-CDI by implementing other interventions such as healthcare worker hand hygiene, environmental cleaning and healthcare worker contact precautions. Further studies are needed to evaluate the impact of VCP on C. difficile colonization in community. Disclosures All authors: No reported disclosures.

scholarly journals Agent-Based Simulation Framework for Epidemic Forecasting during Hajj Seasons in Saudi Arabia

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 325
Author(s):  
Sultanah Mohammed Alshammari ◽  
Mohammed Hassan Ba-Aoum ◽  
Nofe Ateq Alganmi ◽  
Arwa AbdulAziz Allinjawi
Keyword(s):  
Public Health ◽  
Saudi Arabia ◽  
Disease Transmission ◽  
Simulation Framework ◽  
Agent Based Simulation ◽  
Agent Based ◽  
The World ◽  
Hajj Season ◽  
The Impact ◽  
Epidemic Forecasting

The religious pilgrimage of Hajj is one of the largest annual gatherings in the world. Every year approximately three million pilgrims travel from all over the world to perform Hajj in Mecca in Saudi Arabia. The high population density of pilgrims in confined settings throughout the Hajj rituals can facilitate infectious disease transmission among the pilgrims and their contacts. Infected pilgrims may enter Mecca without being detected and potentially transmit the disease to other pilgrims. Upon returning home, infected international pilgrims may introduce the disease into their home countries, causing a further spread of the disease. Computational modeling and simulation of social mixing and disease transmission between pilgrims can enhance the prevention of potential epidemics. Computational epidemic models can help public health authorities predict the risk of disease outbreaks and implement necessary intervention measures before or during the Hajj season. In this study, we proposed a conceptual agent-based simulation framework that integrates agent-based modeling to simulate disease transmission during the Hajj season from the arrival of the international pilgrims to their departure. The epidemic forecasting system provides a simulation of the phases and rituals of Hajj following their actual sequence to capture and assess the impact of each stage in the Hajj on the disease dynamics. The proposed framework can also be used to evaluate the effectiveness of the different public health interventions that can be implemented during the Hajj, including size restriction and screening at entry points.

scholarly journals The impact of vaccination to control COVID-19 burden in the United States: A simulation modeling approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254456
Author(s):  
Oguzhan Alagoz ◽  
Ajay K. Sethi ◽  
Brian W. Patterson ◽  
Matthew Churpek ◽  
Ghalib Alhanaee ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Simulation Modeling ◽  
Vaccine Coverage ◽  
The United States ◽  
Agent Based Simulation ◽  
Vaccination Programs ◽  
Agent Based ◽  
Nonpharmaceutical Interventions ◽  
The Impact

Introduction Vaccination programs aim to control the COVID-19 pandemic. However, the relative impacts of vaccine coverage, effectiveness, and capacity in the context of nonpharmaceutical interventions such as mask use and physical distancing on the spread of SARS-CoV-2 are unclear. Our objective was to examine the impact of vaccination on the control of SARS-CoV-2 using our previously developed agent-based simulation model. Methods We applied our agent-based model to replicate COVID-19-related events in 1) Dane County, Wisconsin; 2) Milwaukee metropolitan area, Wisconsin; 3) New York City (NYC). We evaluated the impact of vaccination considering the proportion of the population vaccinated, probability that a vaccinated individual gains immunity, vaccination capacity, and adherence to nonpharmaceutical interventions. We estimated the timing of pandemic control, defined as the date after which only a small number of new cases occur. Results The timing of pandemic control depends highly on vaccination coverage, effectiveness, and adherence to nonpharmaceutical interventions. In Dane County and Milwaukee, if 50% of the population is vaccinated with a daily vaccination capacity of 0.25% of the population, vaccine effectiveness of 90%, and the adherence to nonpharmaceutical interventions is 60%, controlled spread could be achieved by June 2021 versus October 2021 in Dane County and November 2021 in Milwaukee without vaccine. Discussion In controlling the spread of SARS-CoV-2, the impact of vaccination varies widely depending not only on effectiveness and coverage, but also concurrent adherence to nonpharmaceutical interventions.

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