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.

Details

Title
Socio-economic pandemic modelling: case of Spain
Author
Snellman, Jan E. 1 ; Barreiro, Nadia L. 2 ; Barrio, Rafael A. 3 ; Ventura, Cecilia I. 4 ; Govezensky, Tzipe 5 ; Kaski, Kimmo K. 6 ; Korpi-Lagg, Maarit J. 7 

 Aalto University School of Science, Department of Computer Science, Aalto, Finland (GRID:grid.5373.2) (ISNI:0000 0001 0838 9418) 
 Instituto de Investigaciones Científicas y Técnicas para la Defensa (CITEDEF), Buenos Aires, Argentina (GRID:grid.472580.c) (ISNI:0000 0004 0438 8903) 
 Universidad Nacional Autónoma de México, Instituto de Física, CDMX, Mexico (GRID:grid.9486.3) (ISNI:0000 0001 2159 0001) 
 (CONICET) Centro Atómico Bariloche-CNEA, Bariloche, Argentina (GRID:grid.9486.3); Universidad Nacional de Río Negro, Bariloche, Argentina (GRID:grid.440499.4) (ISNI:0000 0004 0429 9257) 
 Universidad Nacional Autónoma de México, Instituto de Investigaciones Biomédicas, CDMX, Mexico (GRID:grid.9486.3) (ISNI:0000 0001 2159 0001) 
 Aalto University School of Science, Department of Computer Science, Aalto, Finland (GRID:grid.5373.2) (ISNI:0000 0001 0838 9418); The Alan Turing Institute, London, UK (GRID:grid.499548.d) (ISNI:0000 0004 5903 3632) 
 Aalto University School of Science, Department of Computer Science, Aalto, Finland (GRID:grid.5373.2) (ISNI:0000 0001 0838 9418); Max-Planck-Institut für Sonnensystemforschung, Göttingen, Germany (GRID:grid.435826.e) (ISNI:0000 0001 2284 9011); Stockholm University, Nordita, KTH Royal Institute of Technology, Stockholm, Sweden (GRID:grid.10548.38) (ISNI:0000 0004 1936 9377) 
Pages
817
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2911668002
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.