About: A parsimonious model for spatial transmission and heterogeneity in the COVID-19 propagation

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About: A parsimonious model for spatial transmission and heterogeneity in the COVID-19 propagation 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
has title	A parsimonious model for spatial transmission and heterogeneity in the COVID-19 propagation
Creator	Soubeyrand, Samuel Roques, Lionel Berestycki, Henri Baudrot, Virgile Bonnefon, Olivier
Source	MedRxiv
abstract	Raw data on the cumulative number of deaths at a country level generally indicate a spatially variable distribution of the incidence of COVID-19 disease. An important issue is to determine whether this spatial pattern is a consequence of environmental heterogeneities, such as the climatic conditions, during the course of the outbreak. Another fundamental issue is to understand the spatial spreading of COVID-19. To address these questions, we consider four candidate epidemiological models with varying complexity in terms of initial conditions, contact rates and non-local transmissions, and we fit them to French mortality data with a mixed probabilistic-ODE approach. Using standard statistical criteria, we select the model with non-local transmission corresponding to a diffusion on the graph of counties that depends on the geographic proximity, with time-dependent contact rate and spatially constant parameters. This original spatially parsimonious model suggests that in a geographically middle size centralized country such as France, once the epidemic is established, the effect of global processes such as restriction policies, sanitary measures and social distancing overwhelms the effect of local factors. Additionally, this modeling approach reveals the latent epidemiological dynamics including the local level of immunity, and allows us to evaluate the role of non-local interactions on the future spread of the disease. In view of its theoretical and numerical simplicity and its ability to accurately track the COVID-19 epidemic curves, the framework we develop here, in particular the non-local model and the associated estimation procedure, is of general interest in studying spatial dynamics of epidemics.
has issue date	2020-07-16(xsd:dateTime)
bibo:doi	10.1101/2020.07.15.20154740
has license	medrxiv
sha1sum (hex)	834b23686622a13f958934dcaed37f46176f92dd
schema:url	https://doi.org/10.1101/2020.07.15.20154740
resource representing a document's title	A parsimonious model for spatial transmission and heterogeneity in the COVID-19 propagation
resource representing a document's body	covid:834b23686622a13f958934dcaed37f46176f92dd#body_text
is schema:about of	named entity 'model' named entity 'non-local' named entity 'spatially' named entity 'epidemics' named entity 'COVID-19' named entity 'fundamental' named entity 'simplicity' named entity 'spatial pattern' named entity 'factors' named entity 'ALLOWS' named entity 'EPIDEMICS' named entity 'APPROACH' named entity 'WITH TIME' named entity 'STATISTICAL' named entity 'SPATIAL' named entity 'PROXIMITY' named entity 'INCIDENCE' named entity 'CRITERIA' named entity 'PROCEDURE' named entity 'ASSOCIATED' named entity 'IMPORTANT' named entity 'ENVIRONMENTAL' named entity 'EVALUATE' named entity 'DISEASE' named entity 'spatially' named entity 'non-local' named entity 'global' named entity 'spatial' named entity 'Wuhan' named entity 'model selection' named entity 'viral shedding' named entity 'model selection' named entity 'probability' named entity 'Spain' named entity 'epidemiological models' named entity 'France' named entity 'reproduction number' named entity 'branching process' named entity 'peer review' named entity 'Italy' named entity 'maximum likelihood estimator' named entity 'goodness of fit' named entity 'branching process' named entity 'relative probability' named entity 'Akaike information criterion' named entity 'statistical inference' named entity 'France' named entity 'France' named entity 'peer review' named entity 'rate parameter' named entity 'inference' named entity 'data' named entity 'middle' named entity 'mortality' named entity 'spatial' named entity 'COVID' named entity 'local transmission' named entity 'heterogeneities' named entity 'epidemic' named entity 'epidemics' named entity 'social distancing' named entity 'epidemiological' named entity 'France' named entity 'AIC' named entity 'France' named entity 'COVID-19' named entity 'symptom' named entity 'France' named entity 'epidemiological' named entity 'France' named entity 'Ile-de-France' named entity 'Inference procedure' named entity 'AIC' named entity 'epidemic' named entity 'epidemic' named entity 'age distribution' named entity 'likelihood function' named entity 'France' named entity 'epidemic' named entity 'Corsica island' named entity 'France'

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