Evidence map›Paper›PMID 38191603›Full record

ArticleScientific reports2024

Socio-economic pandemic modelling: case of Spain.

Jan E Snellman, Nadia L Barreiro, Rafael A Barrio, Cecilia I Ventura, Tzipe Govezensky, Kimmo K Kaski, Maarit J Korpi-Lagg

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jan E SnellmanDepartment of Computer Science, Aalto University School of Science, 00076, Aalto, Finland.
Nadia L BarreiroInstituto de Investigaciones Científicas y Técnicas para la Defensa (CITEDEF), 1603, Buenos Aires, Argentina.
Rafael A BarrioInstituto de Física, Universidad Nacional Autónoma de México, 04510, CDMX, Mexico.
Cecilia I Ventura(CONICET) Centro Atómico Bariloche-CNEA, 8400, Bariloche, Argentina.
Tzipe GovezenskyInstituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, 04510, CDMX, Mexico.
Kimmo K KaskiDepartment of Computer Science, Aalto University School of Science, 00076, Aalto, Finland.
Maarit J Korpi-LaggDepartment of Computer Science, Aalto University School of Science, 00076, Aalto, Finland. kapyla@mps.mpg.de.

Funding

Horizon 2020 654024Horizon 2020 Framework Programme 818665Horizon 2020 Framework Programme 871042
6 · The paper itself

Abstract

A global disaster, such as the recent Covid-19 pandemic, affects every aspect of our lives and there is a need to investigate these highly complex phenomena if one aims to diminish their impact in the health of the population, as well as their socio-economic stability. In this paper we present an attempt to understand the role of the governmental authorities and the response of the rest of the population facing such emergencies. We present a mathematical model that takes into account the epidemiological features of the pandemic and also the actions of people responding to it, focusing only on three aspects of the system, namely, the fear of catching this serious disease, the impact on the economic activities and the compliance of the people to the mitigating measures adopted by the authorities. We apply the model to the specific case of Spain, since there are accurate data available about these three features. We focused on tourism as an example of the economic activity, since this sector of economy is one of the most likely to be affected by the restrictions imposed by the authorities, and because it represents an important part of Spanish economy. The results of numerical calculations agree with the empirical data in such a way that we can acquire a better insight of the different processes at play in such a complex situation, and also in other different circumstances.

Indexed as

COVID-19DisastersHumansPandemicsSocioeconomic FactorsSpain

Identifiers

PMID38191603
PMCPMC10774333

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.