Agent-Based Model: Simulating a Virus Expansion Based on the Acceptance of Containment Measures

• URL: http://arxiv.org/abs/2307.15723v1
• Date: Fri, 28 Jul 2023 08:01:05 GMT
• Title: Agent-Based Model: Simulating a Virus Expansion Based on the Acceptance of Containment Measures
• Authors: Alejandro Rodr\'iguez-Arias, Amparo Alonso-Betanzos, Bertha Guijarro-Berdi\~nas, Noelia S\'anchez-Marro\~no
• Abstract summary: Compartmental epidemiological models categorize individuals based on their disease status. We propose an ABM architecture that combines an adapted SEIRD model with a decision-making model for citizens. We illustrate the designed model by examining the progression of SARS-CoV-2 infections in A Coruna, Spain.
• Score: 65.62256987706128
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Compartmental epidemiological models categorize individuals based on their disease status, such as the SEIRD model (Susceptible-Exposed-Infected-Recovered-Dead). These models determine the parameters that influence the magnitude of an outbreak, such as contagion and recovery rates. However, they don't account for individual characteristics or population actions, which are crucial for assessing mitigation strategies like mask usage in COVID-19 or condom distribution in HIV. Additionally, studies highlight the role of citizen solidarity, interpersonal trust, and government credibility in explaining differences in contagion rates between countries. Agent-Based Modeling (ABM) offers a valuable approach to study complex systems by simulating individual components, their actions, and interactions within an environment. ABM provides a useful tool for analyzing social phenomena. In this study, we propose an ABM architecture that combines an adapted SEIRD model with a decision-making model for citizens. In this paper, we propose an ABM architecture that allows us to analyze the evolution of virus infections in a society based on two components: 1) an adaptation of the SEIRD model and 2) a decision-making model for citizens. In this way, the evolution of infections is affected, in addition to the spread of the virus itself, by individual behavior when accepting or rejecting public health measures. We illustrate the designed model by examining the progression of SARS-CoV-2 infections in A Coru\~na, Spain. This approach makes it possible to analyze the effect of the individual actions of citizens during an epidemic on the spread of the virus.

Related papers

• Multi-scale simulation of COVID-19 epidemics [0.0]
  We are still facing the COVID-19 epidemics over a year after the start of the COVID-19 epidemics. It is hard to correctly predict its future spread over weeks to come, as well as the impacts of potential political interventions. Current epidemic models mainly fall in two approaches: compartmental models, divide the population in epidemiological classes and rely on the mathematical resolution of differential equations.
  arXiv  Detail & Related papers  (2021-12-02T12:34:11Z)
• An Agent-Based Model of COVID-19 Diffusion to Plan and Evaluate Intervention Policies [0.09236074230806579]
  The model includes the structural data of Piedmont, an Italian region. The model is generative of complex epidemic dynamics emerging from the consequences of agents' actions and interactions.
  arXiv  Detail & Related papers  (2021-08-19T19:23:17Z)
• A k-mer Based Approach for SARS-CoV-2 Variant Identification [55.78588835407174]
  We show that preserving the order of the amino acids helps the underlying classifiers to achieve better performance. We also show the importance of the different amino acids which play a key role in identifying variants and how they coincide with those reported by the USA's Centers for Disease Control and Prevention (CDC)
  arXiv  Detail & Related papers  (2021-08-07T15:08:15Z)
• An Extended Epidemic Model on Interconnected Networks for COVID-19 to Explore the Epidemic Dynamics [2.89591830279936]
  The pandemic control necessitates epidemic models that capture the trends and impacts on infectious individuals. Many exciting models can implement this but they lack practical interpretability. This study combines the epidemiological and network theories and proposes a framework with causal interpretability.
  arXiv  Detail & Related papers  (2021-04-10T06:46:01Z)
• Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records [57.75125067744978]
  We propose a data augmentation method to facilitate domain adaptation. adversarially generated samples are used during domain adaptation. Results confirm the effectiveness of our method and the generality on different tasks.
  arXiv  Detail & Related papers  (2021-01-13T03:20:20Z)
• Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19 [75.99038202534628]
  We propose CALI-Net, a neural transfer learning architecture which allows us to'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist. Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic.
  arXiv  Detail & Related papers  (2020-09-23T22:35:43Z)
• COVID-ABS: An Agent-Based Model of COVID-19 Epidemic to Simulate Health and Economic Effects of Social Distancing Interventions [0.0]
  The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has directly impacted the public health and economy worldwide. This paper proposes the COVID-ABS, a new SEIR (Susceptible-Exposed-Infected-Recovered) agent-based model that aims to simulate the pandemic dynamics.
  arXiv  Detail & Related papers  (2020-06-09T03:44:48Z)
• A General Framework for Survival Analysis and Multi-State Modelling [70.31153478610229]
  We use neural ordinary differential equations as a flexible and general method for estimating multi-state survival models. We show that our model exhibits state-of-the-art performance on popular survival data sets and demonstrate its efficacy in a multi-state setting.
  arXiv  Detail & Related papers  (2020-06-08T19:24:54Z)
• Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach [55.41644538483948]
  This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units. The aim is to support decision making addressed at reducing the incidence rate of infections.
  arXiv  Detail & Related papers  (2020-05-07T16:13:12Z)
• Simulation of Covid-19 epidemic evolution: are compartmental models really predictive? [0.0]
  This paper addresses the question whether a SIR epidemiological model, enriched with asymptomatic and dead individual compartments, could provide reliable predictions on the epidemic evolution. A machine learning approach based on particle swarm optimization (PSO) is proposed to automatically identify the model parameters based on a training set of data of progressive increasing size. The analysis of the scatter in the forecasts shows that model predictions are quite sensitive to the size of the dataset used for training, and that further data are still required to achieve convergent -- and therefore reliable -- predictions.
  arXiv  Detail & Related papers  (2020-04-14T08:42:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.