Studies of COVID-19 Outbreak Control Using Agent-Based Modeling (original) (raw)
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A Flexible Agent-Based Model to Study COVID-19 Outbreak - A Generic Approach
2021
Understanding dynamics of an outbreak like that of COVID-19 is important in designing effective control measures. This study aims to develop an agent based model that compares changes in infection progression by manipulating different parameters in a synthetic population. Model input includes population characteristics like age, sex, working status etc. of each individual and other factors influencing disease dynamics. Depending on number of epicentres of infection, location of primary cases, sensitivity, proportion of asymptomatic and frequency or duration of lockdown, our simulator tracks every individual and hence infection progression through community over time. In a closed community of 10000 people, it is seen that without any lockdown, number of cases peak around 6th week and wanes off around 15th week. If primary case is located inside dense population cluster like slums, cases peak early and wane off slowly. With introduction of lockdown, cases peak at slower rate. If sensi...
A local Agent-Based Model of COVID-19 spreading and interventions
HAL (Le Centre pour la Communication Scientifique Directe), 2023
This research presents a simulation model that combines metapopulation geospatial data with the SEIR epidemiological model to simulate a city of up to 250,000 residents while considering various factors such as virus transmission rate, disease severity, and prevention and control measures. This model can assist decision-makers in exploring different pandemic response strategies, including lockdowns, social distancing, mass testing, contact tracing, and vaccination. This simulation aims to provide decision-makers with a better understanding of the implications of their choices and enable them to make informed real-time decisions to manage a health crisis.
An Agent-Based Model of COVID-19 Diffusion to Plan and Evaluate Intervention Policies
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A model of interacting agents, following plausible behavioral rules into a world where the Covid19 epidemic is affecting the actions of everyone. The model works with (i) infected agents categorized as symptomatic or asymptomatic and (ii) the places of contagion specified in a detailed way. The infection transmission is related to three factors: the characteristics of both the infected person and the susceptible one, plus those of the space in which contact occurs. The model includes the structural data of Piedmont, an Italian region, but we can easily calibrate it for other areas. The micro-based structure of the model allows factual, counterfactual, and conditional simulations to investigate both the spontaneous or controlled development of the epidemic. The model is generative of complex epidemic dynamics emerging from the consequences of agents’ actions and interactions, with high variability in outcomes and stunning realistic reproduction of the successive contagion waves in th...
We have created a model based on artificial agents that describes some aspects of the diffusion dynamics of COVID-19, especially aspects related to the link between social behaviour and contagion dynamics. Starting from the first part of this article [1], published on April 2, we investigated how we can link a particular socio-cultural attitude to some effects on the lethality of the epidemic. As a starting data, we show the effect that the epidemic had in terms of difference between the number of deaths in the first quarter of 2019 and the first quarter of 2020, in 1,698 Italian municipalities [2]. Once we have established how the incidence of the virus was very irregular, we assume that one of the causes may be some social behaviour, through the simulation model described in the previous publication [1], which has been appropriately revised.
Results in physics, 2021
Currently, there is a global pandemic of COVID-19. To assess its prevalence, it is necessary to have adequate models that allow real-time modeling of the impact of various quarantine measures by the state. The SIR model, which is implemented using a multi-agent system based on mobile cellular automata, was improved. The paper suggests ways to improve the rules of the interaction and behavior of agents. Methods of comparing the parameters of the SIR model with real geographical, social and medical indicators have been developed. That allows the modeling of the spatial distribution of COVID-19 as a single location and as the whole country consisting of individual regions that interact with each other by transport, taking into account factors such as public transport, supermarkets, schools, universities, gyms, churches, parks. The developed model also allows us to assess the impact of quarantine, restrictions on transport connections between regions, to take into account such factors as the incubation period, the mask regime, maintaining a safe distance between people, and so on. A number of experiments were conducted in the work, which made it possible to assess both the impact of individual measures to stop the pandemic and their comprehensive application. A method of comparing computertime and dynamic parameters of the model with real data is proposed, which allowed assessing the effectiveness of the government in stopping the pandemic in the Chernivtsi region, Ukraine. A simulation of the pandemic spread in countries such as Slovakia, Turkey and Serbia was also conducted. The calculations showed the highaccuracy matching of the forecast model with real data.
An agent based model for assessing transmission dynamics and health systems burden for COVID-19
Indonesian Journal of Electrical Engineering and Computer Science
Coronavirus disease of 2019 (COVID-19) pandemic has caused over 230 million infections with more than 4 million deaths worldwide. Researches have been using various mathematical and simulation techniques to estimate the future trends of the pandemic to help the policymakers and healthcare fraternity. Agent-based models (ABM) could provide accurate projections than the compartmental models that have been largely used. The present study involves a simulation of ABM using a synthetic population from India to analyze the effects of interventions on the spread of the disease. A disease model with various states representing the possible progression of the disease was developed and simulated using AnyLogic. The results indicated that imposing stricter non-pharmaceutical interventions (NPI) lowered the peak values of infections, the proportion of critical patients, and the deceased. Stricter interventions offer a larger time window for the healthcare fraternity to enhance preparedness. The...
Model-M: An agent-based epidemic model of a middle-sized municipality
2021
This report presents a technical description of our agent-based epidemic model of a particular middle-sized municipality. We have developed a realistic model with 56 thousand inhabitants and 2.7 million of social contacts. These form a multi-layer social network that serves as a base of our epidemic simulation. The disease is modeled by our extended SEIR model with parameters fitted to real epidemics data for Czech Republic. The model is able to simulate a whole range of non-pharmaceutical interventions on individual level, such as protective measures and physical distancing, testing, contact tracing, isolation and quarantine. The effect of government-issued measures such as contact restrictions in different environments (schools, restaurants, vendors, etc.) can also be simulated. The model is implemented in Python and is available as open source at: www.github.com/epicity-cz/model-m/releases
AGILE: GIScience Series, 2021
Abstract. Geography has long sought to explain spatial relationships between social and physical processes, including the spread of infectious diseases, within the context of modelling human-environment interactions. The spread of the recent COVID-19 pandemic, and its devastating effects on human activity and welfare, represent but examples of such complex human-environment interactions. In this paper, we discuss the value of agent-based models for simulating the spread of the COVID-19 virus to support decision-making with regards to non-pharmaceutical interventions, e.g., lock-down. We also develop a prototype agent-based model using a minimal set of rules regarding patterns of human mobility within a hypothetical town, and couple that with an epidemiological model of infectious disease spread. The coupled model is used to: (a) create synthetic trajectories corresponding to daily and weekly activities postulated between a set of predefined points of interest (e.g., home, work), and...
2020
Since its discovery in the Hubei province of China, the global spread of the novel coronavirus SARS-CoV-2 has resulted in millions of COVID-19 cases and hundreds of thousands of deaths. The spread throughout Asia, Europe, and the Americas has presented one of the greatest infectious disease threats in recent history and has tested the capacity of global health infrastructures. Since no effective vaccine is available, isolation techniques to prevent infection such as home quarantine and social distancing while in public have remained the cornerstone of public health interventions. While government and health officials were charged with implementing stay-at-home strategies, many of which had little guidance as to the consequences of how quickly to begin them. Moreover, as the local epidemic curves have been flattened, the same officials must wrestle with when to ease or cease such restrictions as to not impose economic turmoil. To evaluate the effects of quarantine strategies during t...