Territorial impact of the COVID-19 pandemic in Galicia (Spain): a geographical approach

Main Article Content

Ángel Miramontes Carballada
Jose Balsa-Barreiro

Abstract

The coronavirus pandemic is causing a huge impact around the world. Its real magnitude presents very important regional differences, which are appreciable in the number of infected and victims in the different countries. The outbreak of the pandemic and the ignorance of the virus mean that, even today, there are many unknowns about essential aspects related to it. In this sense, geographic knowledge can help answer many questions from the territorial analysis of the data. The objective of this article will be to analyze the behavior of the coronavirus pandemic within the Spanish region of Galicia. The authors of this study propose a multiscale analysis that allows deciphering the most common propagation patterns. For this, we have high spatial resolution data that has been provided by the competent authority under confidentiality. The results of this work allow us to represent and interpret the territorial impact of the pandemic, understanding its behavior as far as possible, allowing future dynamics to be predicted.



Downloads

Download data is not yet available.

Article Details

How to Cite
Miramontes Carballada, Ángel, & Balsa-Barreiro, J. (2021). Territorial impact of the COVID-19 pandemic in Galicia (Spain): a geographical approach. Boletín De La Asociación Española De Geografía, (91). https://doi.org/10.21138/bage.3157

References

Arab-Mazar, Z, Sah, R., Rabaan, A.A., Dhama, K., & Rodriguez-Morales, A.J. (2020). Mapping the incidence of the COVID-19 hotspot in Iran – implications for travellers Travel Medicine Infectious Disease, 34, 101630. https://doi.org/10.1016/j.tmaid.2020.101630

Balsa-Barreiro, J., & Landsperger, S. (2015). A Costa da Morte (Galicia, España): un modelo demográfico antagónico al español. Análisis de su evolución demográfica en el siglo XXI. Journal of Iberian and Latin American Research, 21(1), 63-86. https://doi.org/10.1080/13260219.2015.1041198

Balsa-Barreiro, J. (2013). Insostenibilidad de modelos territoriales desde un punto de vista demográfico: El caso de Costa da Morte (Galicia, España). Papeles de población, 19(78), 167-206. https://www.redalyc.org/articulo.oa?id=11229719007

Balsa-Barreiro, J., Ambühl, L., Menendez, M., &Pentland A.S. (2019). Mapping time-varying accessibility and territorial cohesion with time-distorted maps. IEEE Access, 7, 41702-41714. https://dspace.mit.edu/bitstream/handle/1721.1/134793/08675273.pdf?sequence=2&isAllowed=y

Balsa-Barreiro, J., Morales, A., & Lois-González, RC (2021). Mapping population dynamics at local scales using spatial networks. Complexity, 2021, ID 8632086. https://doi.org/10.1155/2021/8632086

BBC News (2020, March 30). Coronavirus: A visual Guide to the Pandemic. BBC news. https://www.bbc.co.uk/news/world-51235105

Boulos, K., & Geraghty E.M (2020). Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics. International Journal Health Geographics, 19, 8. https://doi.org/10.1186/s12942-020-00202-8

Buzai, G.D. (2020). De Wuhan a Luján. Evolución espacial del COVID-19. Posición, 3, 2683-8915. https://ri.unlu.edu.ar/xmlui/bitstream/handle/rediunlu/683/Buzai_Gustavo_COVID-19.pdf?sequence=1&isAllowed=y

Bynum, P., Raja R.A.I., & Olbina, S. (2013). Building information modelling in support of sustainable design and construction. Journal of Construction Engineering and Management, 139, 24-34. http:://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000560

Buckee, C.O., Balsari, S., Chan, J., Crosas, M., Dominici, F., Gasser, U., & Lipsitch, M. (2020). Aggregated mobility data could help fight COVID-19. Science, 368(6487), 145-146. https://doi.org/10.1126/science.abb8021

Carballada, A.M., & Balsa-Barreiro, J. (2021) Geospatial Analysis and Mapping Strategies for Fine-Grained and Detailed COVID-19 Data with GIS. ISPRS International Journay Geo-Information, 10(9), 602. https://doi.org/10.3390/ijgi10090602

Cattarino, L., Rodriguez-Barraquer, I., Imai, N., Cummings, D.A.T., & Ferguson, N.M. (2020). Mapping global variation in dengue 690 transmission intensity. Science Translational Medicine, 12(528). https://doi.org/10.1126/scitranslmed.aax4144

Centro Nacional de Epidemiología, Instituto de Salud Carlos III (2020). https://www.isciii.es/Paginas/Inicio.aspx

Chang, S.L., Harding, N., & Zachreson, C. (2020). Modelling transmission and control of the COVID-19 pandemic in Australia. Nature Communications, 11, 5710. https://doi.org/10.1038/s41467-020-19393-6

Chen, S., Li, Q., Gao, S., Kang, Y., & Shi, X. (2020a). Mitigating COVID-19 outbreak via high testing capacity and strong transmission-intervention in the United States. medRxiv https://www.medrxiv.org/content/10.1101/2020.04.03.20052720v1

Chen, S., Zhang, Q., Lu, Y., Guo, Z.M., Zhang, X., Zhang, W.J., & Lu, J.H. (2020b). Distribution of the COVID-19 epidemic and correlation with population emigration from Wuhan, China. Chinese Medical Journal, 133(9), 1044-1050. https://dx.doi.org/10.1097%2FCM9.0000000000000782

Cicalò, E., & Valentino, M. (2019). Mapping and visualisation of health data. The contribution of the graphic sciences to medical re-626 search from New York yellow fever to China coronavirus. Disegnarecon, 12(23), 12-21. https://doi.org/10.20365/disegnarecon.23.2019.12

Dagnino, R., Weber, E.J., & Panitz, L.M. (2020). Monitoramento do Coronavírus (Covid-19) nos municípios do Rio Grande do Sul, Brasil. SocArXiv. https://doi.org/10.31235/osf.io/3uqn5

Deka, M.A., & Morshed N. (2018). Mapping disease transmission risk of Nipah virus in South and Southeast Asia. Tropical Medicine and Infectious Disease, 3(2), 57. https://doi.org/10.3390/tropicalmed3020057

De Kadt J., Gotz G., Hamann C., Maree G., Parker A., & Gauteng City-Region Observatory; (2020). Mapping Vulnerability to COVID-19 in Gauteng. GCRO Map of the Month https://gcro.ac.za/outputs/map-of-the-month/detail/mapping-vulnerability-to-covid-19/

Desjardins, M.R., Hohl, A., & Delmelle, E.M. (2020). Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: detecting and evaluating emerging clusters. Applied Geography, 118(102202), 102-202. https://doi.org/10.1016/j.apgeog.2020.102202

Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time. Lancet Infectious Diseases, 20, 533-534. https://doi.org/10.1016/S1473-3099(20)30120-1

Dudley, J. (2008). Public Health and Epidemiological Considerations for Avian Influenza Risk Mapping and Risk Assessment. Ecology and Society, 13(2). http://www.ecologyandsociety.org/vol13/iss2/art21/

Franch-Pardo, I., Napoletano, B.M., Rosete-Verges F., & Billa L. (2020). Spatial analysis and GIS in the study of COVID-19. A review. The Science of the Total Environment. http://doi.org/10.1016/j.scitotenv.2020.140033

Gesler, W. & Kearns, R. (2002). Culture/Place/Health. https://doi.org/10.4324/9780203996317

Gibson, L., & Rush, D. (2020). Novel coronavirus in Cape Town informal settlements: feasibility of using informal dwelling outlines to identify high risk areas for COVID-19 transmission from a social distancing perspective. JMIR Public Health Surveillance, 6(2), e18844. https://doi.org/10.2196/18844

Giuliani, D., Dickson, M.M., Espa, G., & Santi, F. (2020). Modelling and predicting the spatio-temporal spread of COVID-19 in Italy. BMC Infectious Disease, 20(700). https://doi.org/10.1186/s12879-020-05415-7

Graham, H. (2000). Understanding health inequalities. Open University Press.

Gross, B., Zheng, Z., Liu, S., Chen, X., Sela, A., Li, J., & Havlin, S. (2020). Spatio-temporal propagation of COVID-19 pandemics. MedRxiv https://doi.org/10.1101/2020.03.23.20041517

Guan W.J., Ni Z.Y., Hu Y., Liang W.H., Ou C.Q., He J.X., & Du B. (2020). Clinical characteristics of coronavirus disease 2019 in China. New England Journal of Medicine, 382(18):1708–1720. https://doi.org/10.1056/NEJMoa2002032

Huang H., Wang Y., Wang Z., Liang Z., Qu S., Ma S., & Liu X. (2020). Epidemic Features and Control of 2019 Novel Coronavirus Pneumonia in Wenzhou, China (3/3/2020). http://dx.doi.org/10.2139/ssrn.3550007

National Institute of Statistics (2021). [Website] https://www.ine.es

Kearns, R., & Moon, G. (2002). From medical to health geography: novelty, place and theory after a decade of change. Progress Human Geography, 26(5), 605-625. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.137.3695&rep=rep1&type=pdf

Koch, T. (2005). Cartographies of Disease: Maps, Mapping, and Medicine. ESRI Press.

Kuupiel, D., Adu, K.M., Bawontuo, V., Adogboba, D.A., Drain, P.K., Moshabela, M., & Mashamba Thompson, T.P. (2020). Geographical accessibility to glucose-6-phosphate dioxygenase deficiency point-of-care testing for antenatal care in Ghana. Diagnostics, 10(4), 229. https://dx.doi.org/10.3390%2Fdiagnostics10040229

Lee, J.G., & Kang, M. (2015). Geospatial big data: challenges and opportunities. Big Data Research, 2(2), 74-81 https://doi.org/10.1016/j.bdr.2015.01.003

Lenzen, M., Li, M., Malik, A., Pomponi, F., Sun, Y., Wiedmann, T., Faturay, F., Fry, J., Gallego, B., Geschke, A., Gómez-Paredes, J., Kanemoto, K., Kenway, S., Nansai, K., Prokopenko, M., Wakiyama, T., Wang, Y., & Yousefzadeh, M. (2020). Global socio-economic losses and environmental gains from the Coronavirus pandemic. PLOS ONE, 15, 1.13. https://doi.org/10.1371/journal.pone.0235654

Lois-González, R. (2004). A model of Spanish-Portuguese urban growth: the Atlantic axis. Dela, (21), 281-294. https://doi.org/10.4312/dela.21.281-294

Lyseen, A. K., Nøhr, C., Sørensen, E. M., Gudes, O., Geraghty, E. M., Shaw, N. T., Bivona-Tellez, C., & IMIA Health GIS Working Group (2014). A Review and Framework for Categorizing Current Research and Development in Health Related Geographical Information Systems (GIS) Studies. Yearbook of medical informatics, 9(1), 110-124. https://doi.org/10.15265/IY-2014-0008

Meade, M.S. (2014). Medical geography. In The Wiley Blackwell Encyclopedia of Health, Illness, Behavior, and Society (pp. 1375-1381). https://doi.org/10.1002/9781118410868.wbehibs204

Messina, J., Kraemer, M.U., Brady, O.J., Pigott, D.M., Shearer, F.M., Weiss, D.J., Golding, N., Ruktanonchai, C.W., Gething, P.W., Cohn, E., Brownstein, J.S., Khan, K., Tatem, A.J., Jaenisch, T., Murray, C.J., Marinho, F., Scott, T.W., & Hay, S.I. (2016). Mapping global environmental 695 suitability for Zika virus. Elife, 19(5),e15272. https://doi.org/10.7554/eLife.15272

Messina, J., Brady, O., & Pigott, D. (2014). A global compendium of human dengue virus occurrence. Sciencie Data 1, 140004. https://doi.org/10.1038/sdata.2014.4

MoMo dashboard (n.d.). Instituto de Salud Carlos III (Spain). https://momo.isciii.es/public/momo/dashboard/momo_dash-738 board.html

National Health Commission (NHC) of the People’s Republic of China (2020). NHC daily reports. http://www.nhc.gov.cn/yjb/pzhgli/new_list.shtml

Orea, L., & Álvarez. I. C. (2020). How Effective Has the Spanish Lockdown Been to Battle COVID-19? A Spatial Analysis of the Coronavirus Propagation across Provinces (Working Paper). FEDEA, Universidad de Oviedo. https://documentos.fedea.net/pubs/dt/2020/dt2020-03.pdf

Pattison, W.D. (1964) The four traditions of geography. Journal Geography, 63, 211-216. https://doi.org/10.1080/00221346408985265

Pazos Otón, M. (2003). El estudio geográfico de la movilidad: un análisis histórico-evolutivo. In Xeográfic (pp. 101-119). Universidade de Santiago de Compostela.

Pham, Q., Nguyen, D.C., Huynh-The, T., Hwang W., & Pathirana, P.N. (2020). Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts. IEEE Access, 8, 130820-130839. https://doi.org/10.1109/ACCESS.2020.3009328

Pigott, D.M., Golding, N., Mylne, A., Huang, Z., Henry, A.J., Weiss, D.J., Brady, O.J., Kraemer, M.U., Smith, D.L., Moyes, C.L., Bhatt, S., Gething, P.W., Horby, P.W., Bogoch, I.I., Brownstein, J.S., Mekaru, S.R., Tatem, A.J., Khan, K., & Hay, S.I. (2014). Mapping the zoonotic niche of Ebola virus disease in Africa. Elife, 3, e04395. https://doi.org/10.7554/eLife.04395

Pollán, M., Pérez-Gómez, B., Pastor-Barriuso, R., Oteo, J., Hernán, M. A., Pérez-Olmeda, M., Sanmartín, J.L., Fernández-García, A., Cruz, I., Fernández de Larrea, N., Molina, M., Rodríguez-Cabrera, F., Martín, M., Merino-Amador, P., Paniagua, J.L., Muñoz-Montalvo, J.F. Blanco, F., & Yotti, R. (2020). Prevalence of SARS-CoV-2 in Spain (ENE-COVID): a nationwide, population-based seroepidemiological study. The Lancet, 396(10250), 535-544. https://doi.org/10.1016/S0140-6736(20)31483-5

Rezaei, M., Nouri, A.A., Park, G.S., & Kim, D.H. (2020). Application of geographic information system in monitoring and detecting the COVID-19 outbreak Iran Journal Public Health, 49, 114-116. https://doi.org/10.18502/ijph.v49iS1.3679

Rodriguez-Morales, A.J., Galindo-Marquez, M.L., García-Loaiza, C.J., Sabogal-Roman, J.A., Marin-Loaiza, S., Ayala, A.F., Lagos-Grisales, G.J., Lozada-Riascos, C.O., Parra-Valencia, E., Rojas-Palacios, J.H., López, E., López, P., & Grobusch, M.P. (2017). Mapping Zika virus disease incidence in Valle del Cauca. Infection, 45(1), 93-102. https://doi.org/10.1007/s15010-016-0948-1

Romero JM. (2020). Los muertos de la pandemia en España: 44.868. El País. https://elpais.com/sociedad/2020-07-25/las-44868-muertes-de-la-pandemia-en-espana.html

Rosenkrantz, L., Schuurman, N., Bell, N., & Amram, O., (2021). The need for GIScience in mapping COVID-19. Health & Place, 67, 102389. https://doi.org/10.1016/j.healthplace.2020.102389

Rossman, H., Keshet, A., Shilo, S., Gavrieli, A., Bauman, T., Cohen, O., & Segal, E. (2020). A framework for identifying regional outbreak and spread of COVID-19 from one-minute population-wide surveys. Nature Medicine, 26, 634-638. https://doi.org/10.1038/s41591-020-0857-9

Saha, A., Gupta, K., & Patil, M. (2020). Monitoring and Epidemiological Trends of Coronavirus Disease (COVID-19) around the World. Osfpreprints. https://osf.io/2mwky

Santana, P. (2005). Geografias da Saúde e do Desenvolvimento. Evolução e Tendências em Portugal, Coimbra. Ed. Almedina.

Santana Juárez, M.V. (2020). COVID-19 en México: comportamiento espacio temporal y condicionantes socioespaciales, febrero y marzo de 2020. Posición, 3, 2683-8915.

Sarwar, S., Waheedab, R., Sarwar, S., & Khand, A. (2020). COVID-19 challenges to Pakistan: is GIS analysis useful to draw solutions? Science Total Environment, https://doi.org/10.1016/j.scitotenv.2020.139089

Schnaiberg, A., & Gould, K. (1994). Environment and Society: The Enduring Conflict. St. Martin’s Press.

Trias-Llimós, S., Alustiza, A., Prats, C., Tobias, A., & Riffe, T. (2020). The need for detailed COVID-19 data in Spain, The Lancet Public Health, 5(11), e576. https://doi.org/10.1016/S2468-2667(20)30234-6

Smith, D., Lapedes, A., Jong, J.D., Bestebroer, T., Rimmelzwaan, G., Osterhaus, A., & Fouchier, R. (2004). Mapping the Antigenic and Genetic Evolution of Influenza Virus. Science, 305, 371-376.

Su, L., Hong, N., Zhou, X., He, J., Ma, Y., Jiang, H., Han, L., Chang, F., Shan, G., Zhu, W., & Long, Y. (2020). Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China. Frontiers in medicine, 7, 171. https://doi.org/10.3389/fmed.2020.00171

Tang W, Liao H, Marley G, Wang Z, Cheng W, Wu D, Yu R. (2020). The Changing Patterns of Coronavirus Disease 2019 (COVID-19) in China: A Tempogeographic Analysis of the Severe Acute Respiratory Syndrome Coronavirus 2. Clinical Infectious Diseases, 71(15), 818-824. https://doi.org/10.1093/cid/ciaa423

The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team (2020). Vital Surveillances: The Epidemiological Characteristics of an Outbreak of 2019 Novel Coronavirus Diseases (COVID-19) — China, 2020. China CDC Weekly, 2(8), 113-122. https://doi.org/10.46234/ccdcw2020.032

The Lancet Public Health, COVID-19 in Spain: a predictable storm?, The Lancet Public Health, 5(11), e568. https://doi.org/10.1016/S2468-2667(20)30239-5

Tobaiqy, M., Qashqary, M., Al-Dahery, S., Mujallad, A., Hershan, A. A., Kamal, M. A., & Helmi, N. (2020). Therapeutic management of patients with COVID-19: a systematic review. Infection Prevention in Practice, 2(3), 100061. https://doi.org/10.1016/j.infpip.2020.100061

Turner, B.L. (2002). Contested identities: human-environment geography and disciplinary implications in a restructuring academy. Annals Association American Geographers, 92(1), 52-74.

Wang, C., Horby, P. W., Hayden, F. G., & Gao, G. F. (2020). A novel coronavirus outbreak of global health concern. Lancet, 395, 470-473.

World Health Organization (2020a). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf

World Health Organization (2020b). WHO Director-General’s opening remarks at the media briefing on COVID-19—11 March 2020. https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020

Wilkinson, R. (1996). Unhealthy socities: the afflictions of inequality. Routledge https://doi.org/10.1111/1467-9566.ep10938939

Xiong, Y., Wang, Y., Chen, F., & Zhu, M. (2020). Spatial statistics and influencing factors of the novel coronavirus pneumonia 2019 epidemic in Hubei Province, China. International Journal of Environmental Research and Public Health, 17(11), 3903. https://doi.org/10.3390/ijerph17113903

Zhang, X., Rao, H.X., Wu, Y., Huang, Y., & Dai, H. (2020). Comparison of the spatiotemporal characteristics of the COVID-19 and SARS outbreaks in mainland China. BMC Infectious Diseases, 20, 805. https://doi.org/10.1186/s12879-020-05537-y

Zhou, C., Su, F., Pei, T., Zhang, A., Du, Y., Luo, B., & Song, C. (2020). COVID-19: challenges to GIS with big data. Geography and Sustainability, 1(1), 77-87. https://doi.org/10.1016/j.geosus.2020.03.005