About: Bayesian Spatio-Temporal Modeling of COVID-19: Inequalities on Case-Fatality Risk

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About: Bayesian Spatio-Temporal Modeling of COVID-19: Inequalities on Case-Fatality Risk 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
has title	Bayesian Spatio-Temporal Modeling of COVID-19: Inequalities on Case-Fatality Risk
Creator	Durán, Sandra Benavides Ortiz, Efrain Carlos Villamil, Luis Felipe, Julián Martin, Laura Mera Acosta, Carlos Meza Carvajalino, Carlos Meza Martinez, Juan Nicolás Bobadilla, Juan Penagos, Marco Polanco Palencia, Natalia Polo, Gina Rodriguez, Martha Soler-Tovar, Diego Villamil, Porras
Source	MedRxiv
abstract	The ongoing outbreak of COVID-19 challenges health systems and epidemiological responses of all countries worldwide. Although mitigation measures have been globally considered, the spatial heterogeneity of its effectiveness is evident, underscoring global health inequalities. Using Bayesian-based Markov chain Monte Carlo simulations, we evidenced that factors contributing to poverty are also risk factors for COVID-19 case-fatality, and unexpectedly, their impact on the case-fatality risk is comparable to that produced by health factors. Additionally, we confirm that both case-fatality risk and multidimensional poverty index have a heterogeneous spatial distribution, where the lastest consists of health, educational, dwelling, and employment dimensions. Spatio-temporal analysis reveals that the spatial heterogeneity in case-fatalities is associated with the percentage contribution of the health (RR 1.89 95%CI=1.43-2.48) and dwelling (RR 2.01 95%CI=1.37-2.63) dimensions to the multidimensional poverty, but also with the educational (RR 1.21 95%CI=1.03-1.49), and employment (RR 1.23 95%CI=1.02-1.47) dimensions. This spatial correlation indicates that the case-fatality risk increase by 189% and 201% in regions with a higher contribution of the health dimension (i.e., lack of health insurance and self-reporting) and dwelling dimension (i.e., lack of access to safe water, inadequate disposal of human feces, poor housing construction, and critical overcrowding), respectively. These findings assist policy-makers in the spatial and temporal planning of strategies focused on mitigating the case-fatality risk in most vulnerable communities and preparing for future pandemics by progressively reducing the factors that generate health inequality.
has issue date	2020-08-21(xsd:dateTime)
bibo:doi	10.1101/2020.08.18.20171074
has license	medrxiv
sha1sum (hex)	a2fef8addb841a434af1bfbe60eb8c4568a1286e
schema:url	https://doi.org/10.1101/2020.08.18.20171074
resource representing a document's title	Bayesian Spatio-Temporal Modeling of COVID-19: Inequalities on Case-Fatality Risk
resource representing a document's body	covid:a2fef8addb841a434af1bfbe60eb8c4568a1286e#body_text
is schema:about of	named entity 'COVID-19' named entity 'mitigating' named entity 'risk' named entity 'dying' named entity 'higher' named entity 'simulations' named entity 'strategies' named entity 'Modeling' named entity 'outbreak' named entity 'Colombia' named entity 'basic reproduction number' named entity 'Risk of death' named entity 'health insurance' named entity 'medRxiv' named entity 'Santander' named entity 'COVID-19' named entity 'MPI' named entity 'autoregressive process' named entity 'health outcomes' named entity 'Colombia' named entity 'column vector' named entity 'MPI' named entity 'risk of death' named entity 'risk factors' named entity 'Americas' named entity 'La Guajira' named entity 'case-fatality rate' named entity 'progressively' named entity 'based' named entity 'COVID-19' named entity 'dimension' named entity 'COVID-19' named entity 'regions' named entity 'safe water' named entity 'identity matrix' named entity 'multidimensional poverty index' named entity 'MPI' named entity 'MPI' named entity 'random effects' named entity 'future pandemics' named entity 'population projection' named entity 'higher risk' named entity 'Cauca' named entity 'COVID-19' named entity 'null hypothesis' named entity 'child labor' named entity 'policy-makers' named entity 'safe water' named entity 'hygiene measures' named entity 'measures' named entity 'China' named entity 'health inequality' named entity 'temporal' named entity 'housing' named entity 'impact' named entity 'COVID-19' named entity 'risk factors' named entity 'health insurance' named entity 'poor housing' named entity 'Italy' named entity 'safe water' named entity 'global health' named entity 'epidemiological' named entity 'medRxiv' named entity 'COVID-19' named entity 'spatial autocorrelation' named entity 'COVID-19' named entity 'spatial autocorrelation' named entity 'Caribbean' named entity 'COVID' named entity 'autocorrelation'

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