[COVID-19 in the city of Madrid and vulnerability. Analysis of the first two waves.]
Related Experiment Video
Updated: Oct 15, 2025

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples
Published on: June 30, 2023
María Mercedes Esteban Y Peña1, José Luis Gil Bermejo2, Elena Fernández Velasco3
1Departamento de Estudios y Análisis. Participación Ciudadana. Ayuntamiento de Madrid. Madrid. España.
Objective:
The advance of the pandemic in the large cities of the world with great virulence and the apparent heterogeneous distribution by factors of vulnerability, led us to propose this work. The objective of this study was to relate COVID-19 infection rates to the social vulnerability of the city of Madrid by district, in two different episodes, spring 2020 and summer 2020.
Methods:
Analytical cross-sectional study. Taking sociodemographic data of the entire population of the city of Madrid between the months of April and October 2020, together with the cumulative incidence rates of COVID-19, a linear regression analysis, correlation and factor analysis was carried out, relating the cumulative incidence rate of COVID-19 and the vulnerability indicator of the districts of the city of Madrid.
Results:
The results showed important differences between the two episodes of the pandemic: on one hand, the first had more relationship with health factors, while in the second, a relationship appeared with the groups of greater social vulnerability, territorially located in the South-East of the City and related in this case to social factors rather than health. Thus we see that the TIA x 100,000 in the first episode in Chamberí and Usera -two extreme districts in vulnerability- were 896 and 843 cases respectively, while in the second they were 3,708 and 6,258 cases.
Conclusions:
The territorial differences in the city become evident with the arrival of a generalised, universal event such as COVID-19, where vulnerability increases for everyone, with greater repercussions in those territories where it already had an impact.
Related Concept Videos
Steps in Outbreak Investigation
Statistical Methods for Analyzing Epidemiological Data
Causality in Epidemiology
Principles of Disease Surveillance
Pareto Chart
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
Factors Affecting the Risk of Infection
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...

