About: SABCoM: A Spatial Agent-Based Covid-19 Model

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

About: SABCoM: A Spatial Agent-Based Covid-19 Model Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	SABCoM: A Spatial Agent-Based Covid-19 Model
Creator	Davids, Allan Du Rand, Gideon Georg, Co-Pierre Koziol, Tina Schasfoort, Joeri
source	MedRxiv
abstract	How effective are 'lockdown' measures and other policy interventions to curb the spread of Covid-19 in emerging market cities that are characterized by large heterogeneity and high levels of informality? The most commonly used models to predict the spread of Covid-19 are SEIR models which lack the spatial resolution necessary to answer this question. We develop an agent-based model of social interactions in which the distribution of agents across wards, as well as their travel and interactions are calibrated to real data for Cape Town, South Africa. We characterize the elasticity of various policy interventions including increased likelihood to self-isolate, travel restrictions, assembly bans, and behavioural interventions like washing hands or wearing masks. Even in an informal setting, where agents' ability to self-isolate is compromised, a lockdown remains an effective intervention. In our model, the lockdown enacted in South Africa reduced expected fatalities in Cape Town by 26% and the expected demand for intensive care beds by 46%. However, our best calibration predicts a substantially higher case load, demand for ICU beds, and expected number of deaths than the current best estimate published for Cape Town.
has issue date	2020-08-01(xsd:dateTime)
bibo:doi	10.1101/2020.07.30.20164855
has license	medrxiv
sha1sum (hex)	5ee3f78b98a0d067cbfb7ffb2445d768a7e21d7b
schema:url	https://doi.org/10.1101/2020.07.30.20164855
resource representing a document's title	SABCoM: A Spatial Agent-Based Covid-19 Model
resource representing a document's body	covid:5ee3f78b98a0d067cbfb7ffb2445d768a7e21d7b#body_text
is schema:about of	named entity 'South Africa' named entity 'SOCIAL INTERACTIONS' named entity 'MODELS' named entity 'USED' named entity 'ANSWER' »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