Retrospective study of the first wave of COVID-19 in Spain: analysis of counterfactual scenarios

Steinegger, Benjamin; Granell, Clara; Rapisardi, Giacomo; Gómez, Sergio; Matamalas, Joan T.; Soriano‐Paños, David; Gómez‐Gardeñes, Jesús; Arenas, Àlex

doi:10.1101/2021.02.16.21251832

Cited by 2 publications

(2 citation statements)

References 29 publications

(34 reference statements)

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“…When we analysed these same indicators jointly throughout 2020, the Basque Country, Madrid, La Rioja and Navarra were the units of analysis with the “best” results. This coincides with the communities previously identified that first pushed Rt below 1, for example the Community of Madrid [ 36 ].…”

Section: Discussionsupporting

confidence: 90%

“…It has been pointed out in other studies that in Spain there has been a substantial delay between exposure and notification which could have impacted of the data assigned to each period. [ 36 ] The workers registered in the ERTE should be added to the unemployed to compare countries that do not have this social protection system. It must be noted that the primary data regarding the number of registered companies does not imply that they are operational since they can remain in the registry even if there is no economic activity.…”

Section: Discussionmentioning

confidence: 99%

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Measuring the Impact of Future Outbreaks? A Secondary Analysis of Routinely Available Data in Spain

Martín-Delgado

Mula²,

Manzanera³

et al. 2022

IJERPH

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Background: As of 7 January 2022, it is estimated that 5.5 million people worldwide have died from COVID-19. Although the full impact of SARS-CoV-2 (COVID-19) on healthcare systems worldwide is still unknown, we must consider the socio-economic impact. For instance, it has resulted in an 11% decrease in the GDP (Gross Domestic Product) in the European Union. We aim to provide valuable information for policymakers by analysing widely available epidemiological and socioeconomic indicators using Spanish data. Methods: Secondary analysis of routinely available data from various official data sources covering the period from 1 March 2020 to 31 March 2021. To measure the impact of COVID-19 in the population, a set of epidemiological and socioeconomic indicators were used. The interrelationships between these socioeconomic and epidemiological indicators were analysed using Pearson’s correlation. Their behaviour was grouped according to their greater capacity to measure the impact of the pandemic and was compared to identify those that are more appropriate to monitor future health crises (primary outcome) using multivariate analysis of canonical correlation for estimating the correlation between indicators using different units of analysis. Results: Data from different time points were analysed. The excess of mortality was negatively correlated with the number of new companies created during the pandemic. The increase in COVID-19 cases was associated with the rise of unemployed workers. Neither GDP nor per capita debt was related to any epidemiological indicators considered in the annual analysis. The canonical models of socioeconomic and epidemiological indicators of each of the time periods analysed were statistically significant (0.80–0.91 p < 0.05). Conclusions: In conclusion, during the COVID-19 pandemic in Spain, excess mortality, incidence, lethality, and unemployment constituted the best group of indicators to measure the impact of the pandemic. These indicators, widely available, could provide valuable information to policymakers and higher management in future outbreaks.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Measuring the Impact of Future Outbreaks? A Secondary Analysis of Routinely Available Data in Spain

Martín-Delgado

Mula²,

Manzanera³

et al. 2022

IJERPH

View full text Add to dashboard Cite

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On the use of aggregated human mobility data to estimate the reproduction number

Vanni

Lambert

Palatella³

et al. 2021

Sci Rep

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The reproduction number of an infectious disease, such as CoViD-19, can be described through a modified version of the susceptible-infected-recovered (SIR) model with time-dependent contact rate, where mobility data are used as proxy of average movement trends and interpersonal distances. We introduce a theoretical framework to explain and predict changes in the reproduction number of SARS-CoV-2 in terms of aggregated individual mobility and interpersonal proximity (alongside other epidemiological and environmental variables) during and after the lockdown period. We use an infection-age structured model described by a renewal equation. The model predicts the evolution of the reproduction number up to a week ahead of well-established estimates used in the literature. We show how lockdown policies, via reduction of proximity and mobility, reduce the impact of CoViD-19 and mitigate the risk of disease resurgence. We validate our theoretical framework using data from Google, Voxel51, Unacast, The CoViD-19 Mobility Data Network, and Analisi Distribuzione Aiuti.

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Retrospective study of the first wave of COVID-19 in Spain: analysis of counterfactual scenarios

Cited by 2 publications

References 29 publications

Measuring the Impact of Future Outbreaks? A Secondary Analysis of Routinely Available Data in Spain

Measuring the Impact of Future Outbreaks? A Secondary Analysis of Routinely Available Data in Spain

On the use of aggregated human mobility data to estimate the reproduction number

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