An Extended Epidemic Model on Interconnected Networks for COVID-19 to Explore the Epidemic Dynamics

• URL: http://arxiv.org/abs/2104.04695v1
• Date: Sat, 10 Apr 2021 06:46:01 GMT
• Title: An Extended Epidemic Model on Interconnected Networks for COVID-19 to Explore the Epidemic Dynamics
• Authors: Ou Deng, Kiichi Tago, Qun Jin
• Abstract summary: 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.
• Score: 2.89591830279936
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: COVID-19 has resulted in a public health global crisis. 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 in response to this issue. This framework consists of an extended epidemic model in interconnected networks and a dynamic structure that has major human mobility. The networked causal analysis focuses on the stochastic processing mechanism. It highlights the social infectivity as the intervention estimator between the observable effect (the number of daily new cases) and unobservable causes (the number of infectious persons). According to an experiment on the dataset for Tokyo metropolitan areas, the computational results indicate the propagation features of the symptomatic and asymptomatic infectious persons. These new spatiotemporal findings can be beneficial for policy decision making.

Related papers

• A Review of Graph Neural Networks in Epidemic Modeling [14.28921518883576]
  Since the onset of the COVID-19 pandemic, there has been a growing interest in studying epidemiological models. Graph Neural Networks (GNNs) have emerged as a progressively popular tool in epidemic research.
  arXiv  Detail & Related papers  (2024-03-28T21:54:48Z)
• Agent-Based Model: Simulating a Virus Expansion Based on the Acceptance of Containment Measures [65.62256987706128]
  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.
  arXiv  Detail & Related papers  (2023-07-28T08:01:05Z)
• Metapopulation Graph Neural Networks: Deep Metapopulation Epidemic Modeling with Human Mobility [14.587916407752719]
  We propose a novel hybrid model called MepoGNN for multi-step multi-region epidemic forecasting. Our model can not only predict the number of confirmed cases but also explicitly learn the epidemiological parameters.
  arXiv  Detail & Related papers  (2023-06-26T17:09:43Z)
• Data-Centric Epidemic Forecasting: A Survey [56.99209141838794]
  This survey delves into various data-driven methodological and practical advancements. We enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems.
  arXiv  Detail & Related papers  (2022-07-19T16:15:11Z)
• Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics [2.705025060422369]
  Infectious diseases remain among the top contributors to human illness and death worldwide. Forecasts of epidemics can assist stakeholders in tailoring countermeasures to the situation at hand.
  arXiv  Detail & Related papers  (2022-06-21T19:31:25Z)
• Digital Epidemiology: A review [0.0]
  The epidemiology has recently witnessed great advances based on computational models. Big Data along with apps are enabling for validating and refining models with real world data at scale. Ebolas have to be approached from the lens of complexity as they require systemic solutions.
  arXiv  Detail & Related papers  (2021-04-08T08:45:20Z)
• Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread [61.9008166652035]
  We study the spread of COVID-19 using a semi-supervised neural network. We assume a passive part of the population remains isolated from the virus dynamics.
  arXiv  Detail & Related papers  (2020-10-10T19:33:53Z)
• 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)
• A Data-driven Understanding of COVID-19 Dynamics Using Sequential Genetic Algorithm Based Probabilistic Cellular Automata [4.36572039512405]
  This study proposes that for an accurate data-driven modeling of this infection spread, cellular automata provides an excellent platform. Elaborate analyses for COVID-19 statistics of forty countries from different continents have been performed. The substantial predictive power of this model has been established with conclusions on the key players in this pandemic dynamics.
  arXiv  Detail & Related papers  (2020-08-27T09:53:21Z)
• 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)
• Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [102.02672176520382]
  Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. We develop deep diffusion processes to model "dynamic comorbidity networks"
  arXiv  Detail & Related papers  (2020-01-08T15:47:08Z)

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.