A parsimonious approach for spatial transmission and heterogeneity in the COVID-19 propagation

  1. L. Roques
  2. O. Bonnefon
  3. V. Baudrot
  4. S. Soubeyrand
  5. H. Berestycki

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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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  1. Version published to 10.1098/rsos.201382
  2. Version published to 10.1101/2020.07.15.20154740v2 on medRxiv
  3. Version published to 10.1101/2020.07.15.20154740v3 on medRxiv
  4. ScreenIT

    SciScore for 10.1101/2020.07.15.20154740: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

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    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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  5. Version published to 10.1101/2020.07.15.20154740v1 on medRxiv