ArticleRoyal Society open science2020
A parsimonious approach for spatial transmission and heterogeneity in the COVID-19 propagation.
Article in Royal Society open science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- A Stochastic Mobility-Driven Spatially Explicit SEIQRD covid-19 Model with VOCs, Seasonality, and Vaccines.Applied mathematical modelling · 2023Article
- Mobility and the spatial spread of sars-cov-2 in Belgium.Mathematical biosciences · 2023Article
- Epidemic modeling with heterogeneity and social diffusion.Journal of mathematical biology · 2023Article
- Population density impact on COVID-19 mortality rate: A multifractal analysis using French data.Physica A · 2022Article
- The Geographical Distribution and Influencing Factors of COVID-19 in China.Tropical medicine and infectious disease · 2022Article
- Article
- A new logistic growth model applied to COVID-19 fatality data.Epidemics · 2021Article
- Multi-population analysis of the Cuban SARS-CoV-2 epidemic transmission before and during the vaccination process.Physics of fluids (Woodbury, N.Y. : 1994) · 2021Article
- Plateaus, rebounds and the effects of individual behaviours in epidemics.Scientific reports · 2021Article
- Modelling COVID-19 contagion: risk assessment and targeted mitigation policies.Royal Society open science · 2021Article
- Heterogeneity of the COVID-19 Pandemic in the United States of America: A Geo-Epidemiological Perspective.Frontiers in public healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Raw data on the number of deaths at a country level generally indicate a spatially variable distribution of COVID-19 incidence. An important issue is whether this 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 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 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 and sanitary measures overwhelms the effect of local factors. Additionally, this 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.