Understanding How Socioeconomic Inequalities Drive Inequalities In COVID-19 Infections (preprint)

Background: Across the world, the COVID-19 pandemic has disproportionately affected economically disadvantaged groups. This differential impact has numerous possible explanations, each with significantly different policy implications. We examine, for the first time in a low- or middle-income country, which mechanisms best explain the disproportionate impact of the virus on the poor. Methods: : We use primary data from the CoVIDA project, including the results of 59,770 RT-PCR tests in Bogotá, targeted on a mostly asymptomatic adult population June 2020 to March 3rd, 2021. This is combined with administrative data that covers all reported cases in Bogotá. We estimate a number of parameters that are likely to drive inequality in COVID-19 infection rates across socioeconomic groups, then use these estimates in an individual-level branching process model of the epidemic. We use counterfactual scenarios to estimate the relative importance of different channels for explaining inequality in infection rates. Findings: Total infections and inequalities in infections are largely driven by inequalities in the ability to work remotely and in within-home secondary attack rates. Inequalities in isolation behavior are less important but non-negligible, while access to testing and contract-tracing plays practically no role. Interventions that mitigate transmission are found to be more effective when targeted on socioeconomically disadvantaged groups. Interpretation: Socioeconomically disadvantaged groups are particularly vulnerable to COVID-19 infections, and this appears to be primarily driven by the need to work out of home, higher transmission within home, and to some extent, the ability to isolate when needed. Policies that can successfully reduce these channels of transmission among the poor are likely to have large benefits.

Understanding How Socioeconomic Inequalities Drive Inequalities in COVID-19 Infections (preprint)

Background: Across the world, the COVID-19 pandemic has disproportionately affected economically disadvantaged groups. This differential impact has numerous possible explanations, each with significantly different policy implications. We examine, for the first time in a low- or middle-income country, which mechanisms best explain the disproportionate impact of the virus on the poor.Methods: We use primary data from the CoVIDA project, including the results of 59,770 RT-PCR tests in Bogota´, targeted on a mostly asymptomatic adult population June 2020 to March 3rd, 2021. This is combined with administrative data that covers all reported cases in Bogota´. We estimate a number of parameters that are likely to drive inequality in COVID-19 infection rates across socioeconomic groups, then use these estimates in an individual-level branching process model of the epidemic. We use counterfactual scenarios to estimate the relative importance of different channels for explaining inequality in infection rates.Findings: Total infections and inequalities in infections are largely driven by inequalities in the ability to work remotely and in within-home secondary attack rates. Inequalities in isolation behavior are less important but non-negligible, while access to testing and contract-tracing plays practically no role. Interventions that mitigate transmission are found to be more effective when targeted on socioeconomically disadvantaged groups.Interpretation: Socioeconomically disadvantaged groups are particularly vulnerable to COVID-19 infections, and this appears to be driven by the need to work out of home, and higher transmission within home. Policies that can successfully reduce these channels of transmission among the poor are likely to have large benefits.Funding: The authors acknowledge generous support from the Interamerican Development Bank, the Development Bank of Latin America (CAF), the University of Los Andes and the Universidad Nacional de Colombia. Duncan Webb gratefully acknowledges support from the ED 465 at University Paris 1, and the EUR project ANR-17-EURE-0001.Declaration of Interest: The authors have no competing interests to declare.Ethical Approval: Ethics approval was obtained from the ethics committee of Universidad de los Andes (Act number 1278 of 2020). The ethics committee approved that the participants would receive the information via telephone and give their verbal consent, in order to comply with physical distancing and limit the restriction for a study is part of a public health surveillance strategy implemented jointly with the Health Secretary of Bogota.

Understanding How Socioeconomic Inequalities Drive Inequalities in SARS-CoV-2 Infections (preprint)

Across the world, the SARS-CoV-2 (COVID-19) pandemic has disproportionately affected economically disadvantaged groups. This differential impact has numerous possible explanations, each with significantly different policy implications. We examine, for the first time in a low- or middle-income country, which mechanisms best explain the disproportionate impact of the virus on the poor. Combining an epidemiological model with rich data from Bogotá, Colombia, we show that total infections and inequalities in infections are largely driven by inequalities in the inability to work remotely and in within-home secondary attack rates. Inequalities in isolation behavior are less important but non-negligible, while access to testing and contract-tracing plays practically no role. Interventions that mitigate transmission are often more effective when targeted on socioeconomically disadvantaged groups.

SARS-CoV-2 Spread, Detection, and Dynamics in a Megacity in Latin America (preprint)

In many developing countries, the COVID-19 pandemic has spread much faster and wider than the number of detected cases implies. By combining data from 59,770 RT-PCR tests on mostly asymptomatic individuals with administrative data on all detected cases, we capture the spread and dynamics of the COVID-19 pandemic in Bogotá from June 2020 to early March 2021. Our data provide unusually broad and detailed information on mostly asymptomatic adults in Bogotá, allowing to describe various features of the pandemic that appear to be specific to a developing country context. We find that, by the end of March 2021, slightly more than half of the population in Bogotá has been infected, despite only a small fraction of this population being detected. In July 2020, after four months of generalized quarantine that mitigated the pandemic without curving it, the initial buildup of immunity contributed to the end of the first wave. We also show that the share of the population infected by February 2021 varies widely by occupation, socio-economic stratum, and location. This, in turn, has affected the dynamics of the spread: while the first wave of infections was driven by the lowest economic strata and highly-exposed occupations, the second peak affected the population more evenly. A better understanding of the spread and dynamics of the pandemic across different groups provides valuable guidance for efficient targeting of health policy measures and restrictions.