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.
Highlights
In France, the first cases of COVID-19 epidemic were reported on 24 January 2020 [1], it appeared later that some cases were already present in December 2019 [2]
We want to know if the epidemic dynamics reflects this initial heterogeneity and can be modelled without taking into account any spatial heterogeneity in the local conditions
We show here that a parsimonious model can reproduce the local dynamics of the COVID-19 epidemic in France with a relatively high goodness of fit
Summary
In France, the first cases of COVID-19 epidemic were reported on 24 January 2020 [1], it appeared later that some cases were already present in December 2019 [2]. A few months later, at the beginning of June, the spatial pattern of the disease 2 spread seems to have kept track with these first introductions [3] As this spatial pattern may be correlated with covariates such as climate [4] (see electronic supplementary material, S1), a fundamental question is to assess whether this pattern is the consequence of a heterogeneous distribution of some covariates or if it can be explained by the heterogeneity of the initial introduction points. In the latter case, we want to know if the epidemic dynamics reflects this initial heterogeneity and can be modelled without taking into account any spatial heterogeneity in the local conditions
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