Socioeconomic determinants of stay-at-home policies during the first COVID-19 wave

Abstract

Introduction: The COVID-19 pandemic has had a significant impact on public health and social systems worldwide. This study aims to evaluate the efficacy of various policies and restrictions implemented by different countries to control the spread of the virus.

Methods: To achieve this objective, a compartmental model is used to quantify the "social permeability" of a population, which reflects the inability of individuals to remain in confinement and continue social mixing allowing the spread of the virus. The model is calibrated to fit and recreate the dynamics of the epidemic spreading of 42 countries, mainly taking into account reported deaths and mobility across the populations.

Results: The results indicate that low-income countries have a harder time slowing the advance of the pandemic, even if the virus did not initially propagate as fast as in wealthier countries, showing the disparities between countries in their ability to mitigate the spread of the disease and its impact on vulnerable populations.

Discussion: This research contributes to a better understanding of the socioeconomic and environmental factors that affect the spread of the virus and the need for equitable policy measures to address the disparities in the global response to the pandemic.

Keywords: Bayesian inference; COVID-19; compartmental models; epidemic modeling; non-pharmaceutical containment policies.

Figure 1

Scheme of the compartmental model here proposed. The model comprises six compartments: Susceptible S, Exposed E, Infectious I, Recovered R, Pre-deceased P_d and Deceased D. Note that, as a result of the non-pharmaceutical interventions, the Susceptible compartment is divided into three sub-compartments: S_active, S_inactive, and S_confined representing a fraction p(t), (1−p(t))ϕ and (1−p(t))(1−ϕ) of the total number of Susceptible individuals, respectively. A detailed explanation of the flows connecting these compartments can be found in the Section 2.

Figure 2

Daily evolution of the number of deaths in Spain (A), Colombia (B), and Ukraine (C). In all the panels, dots represent real reported data whereas the blue shadowed region corresponds to the 95% prediction interval of the accepted trajectories after calibrating the model. The blue solid line represents the median trajectory whereas the orange line corresponds to the time variation of mobility compared with a baseline pre-pandemic scenario spanning from January 3 to February 6, 2020.

Figure 3

Posterior distribution for each of the free parameters of our model (ϕ, β, ξ, T, δ) obtained after calibrating the model in each of the countries here analyzed. For each parameter, dots denote the median value of the distribution whereas the solid line represents the IQR of each distribution.

Figure 4

Posterior distribution obtained for the permeability parameter ϕ as a function of the GDP per capita of the country in which the model is calibrated. The shadowed region of the fit shows the 95% prediction interval of the trajectories obtained via non-linear regression ϕ(x) = ax^−b, where x stands for GDP per capita and the parameters result in a = 16 ± 4.12, b = 0.39 ± 0.03. The solid line represents the average value of the fitted trajectories for each x. The Spearman correlation coefficient ρ_S between both variables is ρ_S = −0.590 with p < 10⁻⁴.