While many articles have analyzed the effectiveness of the policies that aimed to limit the spread of COVID-19, very little research work has examined the determinants that drove these policies. Therefore, we proposed to study the determinants that led government authorities to implement more or less restrictive policies to limit the spread of the pandemic. Using the COVID-19 stringency index, we highlighted a positive effect of the incidence rate on the stringency level. Patient capacity in intensive care units was also a key variable. This is indicative of the capacity of countries to have a sufficient and appropriate health system to absorb such pandemic crises. On the other hand, we show that epidemiological data regarding the risk of excess mortality (diabetes, cancer, and cardiovascular pathologies) had a negative effect. We conclude by recalling the importance of policy coordination between countries when it comes to lowering the stringency levels of measures, in order to avoid a resurgence of the epidemic.
In this paper we obtain time uniform propagation estimates for systems of interacting diffusion processes. Using a well defined metric function h , our result guarantees a time-uniform estimates for the convergence of a class of interacting stochastic differential equations towards their mean field equation, and this for a general model, satisfying various conditions ensuring that the decay associated to the internal dynamics term dominates the interaction and noise terms. Our result should have diverse applications, particularly in neuroscience, and allows for models more elaborate than the one of Wilson and Cowan, not requiring the internal dynamics to be of linear decay. An example is given at the end of this work as an illustration of the interest of this result. 1
In this paper we prove the propagation of chaos property for an ensemble of interacting neurons subject to independent Brownian noise. The propagation of chaos property means that in the large network size limit, the neurons behave as if they are probabilistically independent. The model for the internal dynamics of the neurons is taken to be that of Wilson and Cowan, and we consider there to be multiple different populations. The synaptic connections are modelled with a nonlinear 'electrical' model. The nonlinearity of the synaptic connections means that our model lies outside the scope of classical propagation of chaos results. We obtain the propagation of chaos result by taking advantage of the fact that the mean-field equations are Gaussian, which allows us to use Borell's Inequality to prove that its tails decay exponentially.
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