Abstract
Almost all sectors of our life have been influenced by COVID-19 pandemic. Informed decision-making regarding pandemic is essential and can be based on credible spatial data. Available COVID-19 spatial data reflect administration areas and in most of the cases used for comparison between countries and regions. These comparisons are tricky and ask for careful consideration of a number of country aspects, especially when the variable in question is dynamic and changes happen very often. Comparison between countries should consider relative numbers (e.g., incidents per capita) and should include information regarding spending for health services. Also, medical provision and climate-related aspects of each country are also important when comparing between countries. Finally, age structure of population is also crucial and needs to be examined. This work illustrates the difficulties when comparing country-data related to COVID-19 pandemic and presents the CRISTINA project. We argue that country-level COVID-19 data ask for standardization in terms of population and geography as well as that correlation of data with country-related characteristics does necessarily not imply direct causation. Finally, this work presents a number of relevant logical fallacies that should be considered when analyzing spatial data of COVID-19 pandemic.
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References
Ali AH (2019) A survey on vertical and horizontal scaling platforms for big data analytics. Int J Integr Eng 11(6):138–150. Retrieved from https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/2892
Bansal S, Chowell G, Simonsen L, Vespignani A, Viboud C (2016) Big data for infectious disease surveillance and modeling. J Infect Dis 214:S375–S379. https://doi.org/10.1093/infdis/jiw400
CSSEGIS and Data (2022) COVID-19 data repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University.
ECDPC (2022) European Centre for Disease Prevention and Control COVID-19 [WWW Document]. European Centre for Disease Prevention and Control. https://www.ecdc.europa.eu/en/covid-19. Accessed 29 Nov 2022
EODY (2022) Greek National Public Health Organization Covid-19 [WWW Document]. https://eody.gov.gr/neos-koronaios-covid-19/. Accessed 29 Nov 2022
Hamet P, Tremblay J (2017) Artificial intelligence in medicine. Metabolism 69:S36–S40. https://doi.org/10.1016/j.metabol.2017.01.011
Holmes KV (2003) SARS coronavirus: a new challenge for prevention and therapy. J Clin Investig 111(11):1605–1609. https://doi.org/10.1172/JCI18819
Johns Hopkins University (2022) Web map of COVID-19 [WWW Document]. https://gisanddata.maps.arcgis.com/apps/dashboards/bda7594740fd40299423467b48e9ecf6. Accessed 29 Nov 2022.
Kavroudakis D (2022) CRISTINA project [WWW Document]. https://www.dimitrisk.gr/covid19.html. Accessed 29 Nov 2022
Locke S, Bashall A, Al-Adely S, Moore J, Wilson A, Kitchen GB (2021) Natural language processing in medicine: a review. Trends Anaesthesia Critical Care 38:4–9. https://doi.org/10.1016/j.tacc.2021.02.007
Mathieu E, Ritchie H, Rodés-Guirao L, Appel C, Giattino C, Hasell J, Macdonald B, Dattani S, Beltekian D, Ortiz-Ospina E, Roser M (2020) Coronavirus pandemic (COVID-19). Our world in data. Retrieve from: https://ourworldindata.org/coronavirus#citation
Park H-A, Jung H, On J, Park SK, Kang H (2018) Digital epidemiology: use of digital data collected for non-epidemiological purposes in epidemiological studies. Healthc Inform Res 24(4):253–262. https://doi.org/10.4258/hir.2018.24.4.253
Rajkomar A, Dean J, Kohane I (2019) Machine learning in medicine. New Engl J Med 380(14):1347–1358. https://doi.org/10.1056/NEJMra1814259
Saecker M, Markl V (2012) Big data analytics on modern hardware architectures: a technology survey. In: Aufaure MA, Zimányi E (eds) Business intelligence. eBISS 2012. Lecture Notes in Business Information Processing, 138. Springer, Berlin, Heidelberg, pp 125–149. https://doi.org/10.1007/978-3-642-36318-4_6
Salathé M (2018) Digital epidemiology: what is it, and where is it going? Life Sci Soc policy 14:1–5. https://doi.org/10.1186/s40504-017-0065-7
Salathe M, Bengtsson L, Bodnar TJ, Brewer DD, Brownstein JS, Buckee C, Campbell EM, Cattuto C, Khandelwal S, Mabry PL (2012) Digital epidemiology. PLoS Comput Biol 8(7):e1002616. https://doi.org/10.1371/journal.pcbi.1002616
Taubenberger JK, Morens DM (2006) 1918 influenza: the mother of all pandemics. Emerg Infect Dis 12(1):15–22. https://doi.org/10.3201/eid1201.050979
WHO (2022) Coronavirus disease (COVID-19)—World Health Organization [WWW Document]. https://www.who.int/emergencies/diseases/novel-coronavirus-2019. Accessed 29 Nov 2022
Zimmer SM, Burke DS (2009) Historical perspective—emergence of influenza A (H1N1) viruses. New Engl J Med 361(3):279–285. https://doi.org/10.1056/NEJMra0904322
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Kavroudakis, D., Zafeirelli, S., Agourogiannis, P., Batsaris, M. (2023). The Art of Geographical Analysis of COVID-19-Related Data. In: Klonari, A., De Lázaro y Torres, M.L., Kizos, A. (eds) Re-visioning Geography. Key Challenges in Geography. Springer, Cham. https://doi.org/10.1007/978-3-031-40747-5_11
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