About: Computational model on COVID-19 Pandemic using Probabilistic Cellular Automata

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Computational model on COVID-19 Pandemic using Probabilistic Cellular Automata Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Computational model on COVID-19 Pandemic using Probabilistic Cellular Automata
Creator	Bhattacharya, Saumik Ghosh, Sayantari
source	ArXiv
abstract	Coronavirus disease (COVID-19) which is caused by SARS-COV2 has become a pandemic. This disease is highly infectious and potentially fatal, causing a global public health concern. To contain the spread of COVID-19, governments are adopting nationwide interventions, like lockdown, containment and quarantine, restrictions on travel, cancelling social events and extensive testing. To understand the effects of these measures on the control of the epidemic in a data-driven manner, we propose a probabilistic cellular automata (PCA) based modified SEIQR model. The transitions associated with the model is driven by data available on chronology, symptoms, pathogenesis and transmissivity of the virus. By arguing that the lattice-based model captures the features of the dynamics along with the existing fluctuations, we perform rigorous computational analyses of the model to take into account of the spatial dynamics of social distancing measures imposed on the people. Considering the probabilistic behavioural aspects associated with mitigation strategies, we study the model considering factors like population density and testing efficiency. Using the model, we focus on the variability of epidemic dynamics data for different countries and point out the reasons behind these contrasting observations. To the best of our knowledge, this is the first attempt to model COVID-19 spread using PCA that gives us both spatial and temporal variations of the infection spread with the insight about the contributions of different infection parameters.
has issue date	2020-06-19(xsd:dateTime)
has license	arxiv
sha1sum (hex)	d191973bef177c96eb844747dfaa9addfe147f3d
resource representing a document's title	Computational model on COVID-19 Pandemic using Probabilistic Cellular Automata
resource representing a document's body	covid:d191973bef177c96eb844747dfaa9addfe147f3d#body_text
is schema:about of	named entity 'PCA' named entity 'spatial' named entity 'social distancing' named entity 'existing' named entity 'extensive' »more»

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software