How effective has the Spanish lockdown been to battle COVID‐19? A spatial analysis of the coronavirus propagation across provinces

Orea, Luis; Álvarez, Inmaculada

doi:10.1002/hec.4437

Cited by 53 publications

(35 citation statements)

References 28 publications

(54 reference statements)

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“…We find that conditional efficiency improvements using the “adjusted” data are about 70-percent that of the unadjusted data, suggesting that slightly over a quarter of inefficiency is related to noise from uncounted COVID-19 cases. However, the similarity in results using the unadjusted or adjusted COVID-19 case data is analogous to the results from Orea and Alvarez ( 2022 ) and Orea et al ( 2021 ), further providing proof for our analysis.…”

Section: Introductionsupporting

confidence: 84%

The efficiency of COVID cases to COVID policies: a robust conditional approach

2022

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This paper examines the efficiency of four major COVID-19 social distancing policies: (i) shelter-in-place orders (SIPO), (ii) non-essential business closures, (iii) mandatory quarantine for travelers, and (iv) bans on large gatherings. Results suggest that the average US state is highly inefficient in producing the fraction of the population that does not have COVID-19 without social distancing policies put in place. We find that having any of the four major social distancing policies increases conditional efficiency by 9.7 (9.5) percentage points in the first 100 days (full sample). This corresponds to 57 (172) fewer total COVID-19 cases per 100,000 population in the first 100 days (full sample). We also find that population density accounts for a majority of unconditional state inefficiency. Evidence suggests considerable heterogeneity in conditional efficiency improvement, indicating that no uniform national social distancing policy would have been more effective; more effective strategies would have been to target more densely populated areas. Conditional efficiency regressions suggest that bans on large gatherings were the most effective policies, with SIPOs and non-essential business closures having smaller impacts. States that implemented social distancing policies except mandatory quarantine for traveler policies were highly effective for the first 100 days, but had less effectiveness over the full sample. There is also preliminary evidence that premature revocations of social distancing policies reduced conditional efficiency, leading to COVID-19 case spikes.

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Section: Introductionsupporting

confidence: 84%

The efficiency of COVID cases to COVID policies: a robust conditional approach

2022

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“…It is likely that better representation of mobility patterns could be achieved through separate weekday and weekend models, however the aim of this study was to examine general mobility trends over time, rather than to isolate variation with respect to weekdays or weekends. Additionally, this study explores changes in patterns of movement within and between communities over time, but does not correlate this with social, demographic and economic features of these communities, however this has been explored in other studies examining mobility changes during the pandemic [13,35,36].…”

Section: Discussionmentioning

confidence: 99%

Multiscale mobility patterns and the restriction of human movement

Schindler¹,

Clarke²,

Barahona³

2022

Preprint

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Strict lockdown measures have been put in place in many countries around the world to constrain human mobility in response to the unparalleled challenges posed by the COVID-19 pandemic. Here we apply networktheoretic tools to analyse a geolocalised dataset of human mobility of 16 million UK Facebook users from March to July 2020. A special emphasis lies on dynamical perspectives of network analysis and multi-scale community detection with Markov Stability analysis is performed to identify signatures for the mobility contraction in the UK. Thereby, a new quantitative criterion for the scale selection in Markov Stability analysis is proposed, which reveals different scales of mobility in a semi-automated manner. The analysis of the UK mobility network reveals a pronounced decline of human mobility under COVID-19 and suggests that local community structure has been strengthened under lockdown. In particular, human mobility does not follow along purely geographic and administrative lines but the flow-based approach allows for the identification of intrinsic mobility patterns that may inform future interventions to prevent COVID-19 transmission.

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“…By analyzing spatio-temporal patterns in China, Gross et al (2020) identified that the virus propagation adopted the flight of Levy, a tendency of fractal movement found in human mobility. Furthermore, spatial analysis was also used from a spatio-temporal perspective to assess the impact of lax containment efforts (Desjardins et al , 2020) or mandatory confinements (Orea and Alvarez, 2020; Giuliani et al , 2020). The latter concluded that those provinces that were closest to epicenters of COVID-19 in Spain were benefited from the confinements since the contagion rates were reduced.…”

Section: Related Workmentioning

confidence: 99%

A methodology for managing public spaces to increase access to essential goods and services by vulnerable populations during the COVID-19 pandemic

Ludowieg

Ortega

Bronfman

et al. 2021

JHLSCM

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PurposeThe purpose of this paper is to present a spatial decision support system (SDSS) to be used by the local authorities of a city in the planning and response phase of a disaster. The SDSS focuses on the management of public spaces as a resource to increase a vulnerable population’s accessibility to essential goods and services. Using a web-based platform, the SDSS would support data-driven decisions, especially for cases such as the COVID-19 pandemic which requires special care in quarantine situations (which imply walking access instead of by other means of transport).Design/methodology/approachThis paper proposes a methodology to create a web-SDSS to manage public spaces in the planning and response phase of a disaster to increase the access to essential goods and services. Using a regular polygon grid, a city is partitioned into spatial units that aggregate spatial data from open and proprietary sources. The polygon grid is then used to compute accessibility, vulnerability and population density indicators using spatial analysis. Finally, a facility location problem is formulated and solved to provide decision-makers with an adaptive selection of public spaces given their indicators of choice.FindingsThe design and implementation of the methodology resulted in a granular representation of the city of Lima, Peru, in terms of population density, accessibility and vulnerability. Using these indicators, the SDSS was deployed as a web application that allowed decision-makers to explore different solutions to a facility location model within their districts, as well as visualizing the indicators computed for the hexagons that covered the district’s area. By performing tests with different local authorities, improvements were suggested to support a more general set of decisions and the key indicators to use in the SDSS were determined.Originality/valueThis paper, following the literature gap, is the first of its kind that presents an SDSS focused on increasing access to essential goods and services using public spaces and has had a successful response from local authorities with different backgrounds regarding the integration into their decision-making process.

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How effective has the Spanish lockdown been to battle COVID‐19? A spatial analysis of the coronavirus propagation across provinces

Cited by 53 publications

References 28 publications

The efficiency of COVID cases to COVID policies: a robust conditional approach

The efficiency of COVID cases to COVID policies: a robust conditional approach

Multiscale mobility patterns and the restriction of human movement

A methodology for managing public spaces to increase access to essential goods and services by vulnerable populations during the COVID-19 pandemic

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