A Flexible Agent-Based Model to Study COVID-19 Outbreak - A Generic Approach (original) (raw)
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An agent-based model was developed to study outbreaks and outbreak control for COVID-19, mainly in urban communities. Rules for people’s interactions and virus infectiousness were derived based on previous sociology studies and recently published data-driven analyses of COVID-19 epidemics. The calculated basic reproduction number of epidemics from the developed model coincided with reported values. There were three control measures considered in this paper: social distancing, self-quarantine and community quarantine. Each control measure was assessed individually at first. Later on, an artificial neural network was used to study the effects of different combinations of control measures. To help quantify the impacts of self-quarantine and community quarantine on outbreak control, both were scaled respectively. The results showed that self-quarantine was more effective than the others, but any individual control measure was ineffective in controlling outbreaks in urban communities. Th...
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...
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Governments, 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 n...
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During the Covid-19 pandemic, most governments across the world imposed policies like lock-down of public spaces and restrictions on people's movements to minimize the spread of the virus through physical contact. However, such policies have grave social and economic costs, and so it is important to pre-assess their impacts. In this work we aim to visualize the dynamics of the pandemic in a city under different intervention policies, by simulating the behavior of the residents. We develop a very detailed agent-based model for a city, including its residents, physical and social spaces like homes, marketplaces, workplaces, schools/colleges etc. We parameterize our model for Kolkata city in India using ward-level demographic and civic data. We demonstrate that under appropriate choice of parameters, our model is able to reproduce the observed dynamics of the Covid-19 pandemic in Kolkata, and also indicate the counter-factual outcomes of alternative intervention policies.
2020
COVID-19 disease, caused by SARS-CoV-2 virus, has infected over four million people globally. It has been declared as a “Public Health Emergency of International Concern” (PHE 1C), by the World Health Organization (1). Several mathematical models, mostly based on compartmental modeling, are being used for projections for COVID-19 in India. These projections are being used for policy level decisions and public health prevention activities (2,3). Unlike compartmental models, which consider population averages, Agent Based Models (ABM) consider individual behavior in the models for disease projections. ABMs, provide better insights into projections compared to compartmental models (4).We present an ABM approach with a synthetic population from Rangareddy district, Telangana state, India, to examine the patterns and trends of the COVID-19 in terms of infected, admitted, critical cases requiring intensive care and/ or ventilator support, mortality and recovery. The model is developed bas...
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.
Multi-agent simulation model updating and forecasting for the evaluation of COVID-19 transmission
Scientific Reports, 2022
Agent-based models have been an emerging approach in epidemiological modelling, specifically in investigating the COVID-19 virus. However, there are challenges to its validation due to the absence of real data on specific socioeconomic and cognitive aspects. Therefore, this work aims to present a strategy for updating, verifying and validating these models based on applying the particle swarm optimization algorithm to better model a real case. For such application, this work also presents a new framework based on multi-agents, whose significant contribution consists of forecasting needed hospital resources, population adaptative immunization and reports concerning demographic density, including physical and socioeconomic aspects of a real society in the modelling task. Evaluation metrics such as the data's Shape Factor (SF), Mean Square Error (RMSE), and statistical and sensitivity analyses of the responses obtained were applied for comparison with the real data. The Brazilian municipality of Passa Vinte, located in the State of Minas Gerais (MG), was used as a case study. The model was updated in cumulative cases until the 365th day of the pandemic. The statistical and sensitivity analysis results showed similar patterns around the actual data up to the 500th day of the pandemic. Their mean values of SF and RMSE were 0.96 and 7.22, respectively, showing good predictability and consistency, serving as an adequate tool for decision-making in health policies. Multi-agent-based models have aroused the interest of many researchers in several applications. Its approach consists of a method for analyzing "complex social systems" characterized by multiple interacting parts and may present non-linear behaviours. Policymakers and researchers have applied these models exhaustively, seeking answers to epidemiologic and biological issues. For instance, to investigate the effects of a given pathogen on society and the human body, experimental testing conditions would put the safety of people at risk, creating ethical problems and/or biological disasters. Thus, multi-agent models consist of autonomous agents who work toward goals (as opposed to discrete tasks) in a dynamic environment (where change is the norm), without continuous direct supervision or control, and exhibit a significant degree of flexibility 6. Therefore, the multi-agent approach becomes a reasonable and suitable proposal for modelling due to the variety of effects caused by the Severe Acute Respiratory Syndrome-2 (SARS-CoV-2) virus in the human body and society Corona Virus Disease (COVID-19) spreading. However, it is worth mentioning the following question: how may these models be evaluated and validated? Using multi-agent models to model real-world and social systems has attracted criticism due to the challenges involved in their validation 20. This work studied an intra-state conflict multi-agent-based model to give a balanced perspective on those criticisms. Indeed, multi-agent models for social systems are most useful when the connection between micro-behaviours and macro-behaviours is not well-understood. Another point is when data collection from the real-world system is prohibitively expensive for time or money or if it puts human lives at risk. Despite the criticisms regarding agent-based models, this approach is advantageous when agents are heterogeneous, and the heterogeneity of the agents affects the overall performance of the system. Also, these models provide more detailed information than equation-based models or many other approaches. These agents' interactions are frequently non-linear, changing both spatially and temporally. The emphasis is on modelling, capturing, and replicating new emergent properties in a complex system of individual components that evolve
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.
2020
Objectives: To evaluate the transmission dynamics and health systems burden of COVID-19 with and without interventions, using an Agent Based Modeling (ABM) approach on a localized synthetic population. Study design: A synthetic population of Rangareddy district, Telangana state, India, with 5,48,323 agents and simulated using an ABM approach for three different scenarios. Methods: The patterns and trends of the COVID-19 in terms of infected, admitted, critical cases requiring intensive care and/ or ventilator support, mortality and recovery were examined. The model was simulated over a period of 365 days for a no lockdown scenario and two Non-Pharmaceutical Intervention (NPI) scenarios i.e., 50% lockdown and 75% lockdown scenarios. Sensitivity Analysis was performed to compare the effect of change of parameters on disease dynamics. Results: Results revealed that the peak values and slope of the curve declined as NPI became more stringent. The peak values could facilitate policymaker...
An Agent-Based Model of COVID-19 Diffusion to Plan and Evaluate Intervention Policies
2021
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...