Estimating the effect of social inequalities in the mitigation of COVID-19 across communities in Santiago de Chile

Abstract

We study the spatio-temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data from 1.4 million users, 22% of the whole population in the area, characterizing the effects of non-pharmaceutical interventions (NPIs) on the epidemic dynamics. We integrate these data into a mechanistic epidemic model calibrated on surveillance data. As of August 1, 2020, we estimate a detection rate of 102 cases per 1,000 infections (90% CI: [95 - 112 per 1,000]). We show that the introduction of a full lockdown on May 15, 2020, while causing a modest additional decrease in mobility and contacts with respect to previous NPIs, was decisive in bringing the epidemic under control, highlighting the importance of a timely governmental response to COVID-19 outbreaks. We find that the impact of NPIs on individuals' mobility correlates with the Human Development Index of comunas in the city. Indeed, more developed and wealthier areas became more isolated after government interventions and experienced a significantly lower burden of the pandemic. The heterogeneity of COVID-19 impact raises important issues in the implementation of NPIs and highlights the challenges that communities affected by systemic health and social inequalities face adapting their behaviors during an epidemic.

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

“…A subsets of papers use a metapopulation approach accounting also for the age-stratification and contact matrices within each subpopulation. In this group we find papers that evaluate the risks of importation of cases from China in the early phases of the pandemic [22] , model the first wave [119] , [121] , [122] , [123] , [147] and investigate reopening scenarios [106] , [120] .…”

“…The metapopulation approach and the real data used to fed it allowed the authors to provide a picture of the spatio-temporal diffusion and diffusion potential of the virus which at that time was still highly debated. A similar model, without long-range mobility, has been proposed to describe the first wave and the impact of NPIs in the metropolitan area of Santiago de Chile [119] . The model considers as subpopulation the municipalities of the capital of Chile and adopts data from mobile phones to estimate the mobility flows as well as their changes induced by NPIs.…”

COVID-19 policy analysis: labour structure dictates lockdown mobility behaviour

“…We use mobile phone data (call records) to characterize changes in city residents' urban mobility in an 11 day period beginning with the introduction of local lockdown measures. Urban mobility metrics from mobile phones have frequently been used to quantify mobility reductions in the wake of COVID-19 as well as other infectious diseases, both to characterize lockdown measures [2,18] and to predict epidemic spread [19][20][21][22].…”

The importance of non-pharmaceutical interventions during the COVID-19 vaccine rollout

“…The importance of socio-demographic and socio-economic characteristics in the adoption of NPIs has been also reported in other studies conducted by monitoring variations to aggregate mobility patterns before and during NPIs. Unanimously they indicate that disadvantaged groups, though less mobile before the pandemic, were not able to reduce their mobility (i.e., stay home) during the implementation of strict NPIs [30][31][32][33][34][35][36][37]. The literature aimed at estimating the epidemiological and societal impact of COVID-19 vaccines has been focused mainly on two very important points.…”