Arquitectura conceptual de plataforma tecnológica de vigilancia epidemiológica para la COVID-19

  1. Pedro Atencio 1
  2. German Sánchez-Torres 2
  3. Rene Iral Palomino 3
  4. John W. Branch Bedoya 3
  5. Daniel Burgos 4
  1. 1 Instituto Tecnológico Metropolitano, Colombia
  2. 2 Universidad del Magdalena, Colombia
  3. 3 Universidad Nacional de Colombia, Sede Medellín, Colombia
  4. 4 Universidad International de La Rioja (UNIR), España
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Revista:
Campus Virtuales

ISSN: 2255-1514

Año de publicación: 2021

Volumen: 10

Número: 1

Páginas: 21-34

Tipo: Artículo

DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Campus Virtuales

Resumen

Since SARS-CoV-2 is likely to become endemic in many countries, it will require not only short-term support but also long-term support, as social distancing policies cannot be extended for long. Therefore, a technological platform for epidemiological surveillance can represent a fundamental tool. The impact of the project is essential for public health actors to design and evaluate policies aimed at the safe reactivation of social activities after social distancing policies are suspended. We also consider this software service as a basic piece in the Digital Transformation strategy, since it allows us to anticipate the behaviors and necessary resources that adapt the needs with the provision in a dynamic way, but adjusted to reality. This anticipation approach becomes a pillar in the digital strategy of any company, Administration and education center. The tool includes a mechanism based on Artificial Intelligence for data analysis in order to have a dynamic understanding of symptoms, evolution, social space-time data and the relationships between them, which will allow the relevant entities to optimize resources such as virus detection tests and positive test controls.

Referencias bibliográficas

  • Amazon (2020). Amazon Web Services (AWS) data lake for analysis of COVID-19 data. (https://dj2taa9i652rf.cloudfront.net/).
  • American Chemical Society (2020). CAS COVID-19 antiviral candidate compounds dataset. (https://www.cas.org/covid19).
  • Apostolopoulos, I.; Aznaouridis, S.; Tzani, M. (2020). Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases. Journal of Medical and Biological Engineering, 40, 462-469. doi:10.1007/s40846-020-00529-4.
  • Bavel, J. J. V.; Baicker, K.; Boggio, P. S.; Capraro, V.; Cichocka, A.; Cikara, M.; ...; Willer, R. (2020). Using social and behavioural science to support COVID-19 pandemic response. Nature Human Behaviour, 1-12. 4, 460-471. doi:10.1038/s41562-020-0884-z.
  • Bayer, J. B.; Triệu, P.; Ellison, N. B. (2020). Social Media Elements, Ecologies, and Effects. Annual Review of Psychology, 71(10), 471-497.
  • Brady, W. J.; Crockett, M.; Van Bavel, J. J. (2019). The MAD Model of Moral Contagion: The role of motivation, attention and design in the spread of moralized content online. Perspectives on Psychological Science, 15(4), 978-1010. doi:10.31234/osf.io/pz9g6.
  • Brooks, S. K.; Webster, R. K.; Smith, L. E.; Woodland, L.; Wessely, S.; Greenberg, N.; Rubin, G. J. (2020). The psychological impact of quarantine and how to reduce it: rapid review of the evidence. The Lancet, 395(1), 912-920.
  • Cabero-Almenara, J.; Llorente-Cejudo, C. (2020). Covid-19: transformación radical de la digitalización en las instituciones universitarias. Campus Virtuales, 9(2), 25-34.
  • CDCP - Center for Diase Control and Prevention. (2020). CDC COVID-19 Cases, Data, and Surveillance. (https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/).
  • Ceylan, Z.; Meral, R.; Cetinkaya, T. (2020). Relevance of SARS-CoV-2 in food safety and food hygiene: potential preventive measures, suggestions and nanotechnological approaches. Virusdisease, 31, 154-160. doi:10.1007/s13337-020-00611-0.
  • Chang, M. C.; Park, D. (2020). How Can Blockchain Help People in the Event of Pandemics Such as the COVID-19?. Journal of Medical Systems, 44, 1-2. doi:10.1007/s10916-020-01577-8.
  • Chen, X.; Yu, B. (2020). First two months of the 2019 Coronavirus Disease (COVID-19) epidemic in China: real-time surveillance and evaluation with a second derivative model. Global health research and policy, 5(1), 1-9.
  • Choi, B. C. (2012). The past, present, and future of public health surveillance. Scientifica, 2012(875253), 875253. doi:10.6064/2012/875253.
  • Choi, J.; Cho, Y.; Shim, E.; Woo, H. (2016). Web-based infectious disease surveillance systems and public health perspectives: a systematic review. BMC public health, 16(1), 1238.
  • Contreras, C. M.; Metzger, G. A.; Beane, J. D.; Dedhia, P. H.; Ejaz, A.; Pawlik, T. M. (2020). Telemedicine: Patient-Provider Clinical Engagement During the COVID-19 Pandemic and Beyond. Journal of Gastrointestinal Surgery, 1. doi:10.1007/s11605-020-04623-5.
  • CSELS - Center for Surveillance, Epidemiology, and Laboratory Services (2020). Surveillance Data Platform (SDP) Program Efforts,. Estados Unidos:. U.S. Department of Health & Human Services. (https://www.cdc.gov/sdp/index.html).
  • Cuticchia, A. J.; Cooley, P. C.; Hall, R. D.; Qin, Y. (2006). NIDDK data repository: a central collection of clinical trial data. BMC medical informatics and decision making, 6(1), 19.
  • Deshpande, P. S.; Sharma, S. C.; Peddoju, S. K. (2019). Predictive and Prescriptive Analytics in Big-Data Era. In Security and Data Storage Aspect in Cloud Computing (pp. 71-81). Singapore: Springer.
  • DHHS - Department of Health & Human Services (2020). El Sistema Nacional de Notificación de Vigilancia de Enfermedades (NNDSS), Epidemiology, and Laboratory Services. Estados Unidos: U.S. (hhttps://wwwn.cdc.gov/nndss/).
  • Duke-Sylvester, S. M.; Perencevich, E. N.; Furuno, J. P.; Real, L. A.; Gaff, H. (2008). Advancing epidemiological science through computational modeling: a review with novel examples. In Annales Zoologici Fennici (Vol. 45, No. 5, pp. 385-401). Finnish Zoological and Botanical Publishing Board.
  • Ferretti, L.; Wymant, C.; Kendall, M.; Zhao, L.; Nurtay, A.; Abeler-Dörner, L.; ...; Fraser, C. (2020). Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science, 368(6491), 1-9.
  • Fong, S. J.; Dey, N.; Chaki, J. (2020). AI-Enabled Technologies that fight the Coronavirus Outbreak. In S. J. Fong, N. Dey & J. Chaki (Eds.), Artificial Intelligence for Coronavirus Outbreak (pp. 23-45). Singapore: Springer. doi:10.1007/978-981-15-5936-5_2.
  • García-Peñalvo, F. J.; Corell, A. (2020). La COVID-19: ¿enzima de la transformación digital de la docencia o reflejo de una crisis metodológica y competencial en la educación superior?. Campus Virtuales, 9(2), 83-98.
  • Google (2020). Google Cloud Platform (GCP) Datasets for COVID-19 Research. (https://console.cloud.google.com/marketplace/browse?filter=category:covid19&pli=1).
  • Guibovich, G. (2009). Sistemas de Información para Vigilancia de la Salud 2009. OPS/OMS. (https://www.paho.org/venezuela/index.php?option=com_docman&view=download&category_slug=presentaciones&alias=22-sistemas-de-informacion-para-vigilancia-de-la-salud&Itemid=466).
  • Kappen, T. H.; Van Klei, W. A.; Van Wolfswinkel, L.; Kalkman, C. J.; Vergouwe, Y.; Moons, K. G. (2018). Evaluating the impact of prediction models: lessons learned, challenges, and recommendations. Diagnostic and prognostic research, 2(1), 11.
  • Kritikos, M. (2020). Ten technologies to fight coronavirus, Scientific Foresight Unit (STOA) of European Parliamentary Research Service, Brussels, PE 641.543 – April 2020.
  • Lopes, L. F.; Silva, F. A. B.; Couto, F.; Zamite, J.; Ferreira, H.; Sousa, C.; Silva, M. J. (2010). Epidemic Marketplace: An Information Management System for Epidemiological Data. In S. Khuri, L. Lhotská & N. Pisanti (Eds.), Information Technology in Bio- and Medical Informatics, ITBAM 2010 (pp. 31-44). Springer Berlin Heidelberg.
  • Martínez, F. (2000). De la información a la acción: la vigilancia de la salud pública. Centro Nacional de Epidemiología. Instituto de Salud Carlos III. Ministerio de Sanidad y Consumo. Revista Española de Salud Pública.
  • McCall, B. (2020). COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread. The Lancet Digital Health, 2(4), e166-e167.
  • Mergel, I.; Edelmann, N.; Haug, N. (2019). Defining digital transformation: Results from expert interviews. Government Information Quarterly, 36(4), 101385.
  • Moore, J. H.; Barnett, I.; Boland, M. R.; Chen, Y.; Demiris, G.; Gonzalez-Hernandez, G.; ...; Holmes, J. H. (2020). Ideas for how informaticians can get involved with COVID-19 research. BioData Mining, 13, 3.
  • National Institutes of Health (2020). Open-Access Data and Computational Resources to Address COVID-19. (https://www.nih.gov/coronavirus).
  • Nsubuga, P.; White, M. E.; Thacker, S. B.; Anderson, M. A.; Blount, S. B.; Broome, C. V.; ...; Trostle, M. (2006). Public Health surveillance: a tool for targeting and monitoring interventions. Disease control priorities in developing countries, 2, 997-1018.
  • Organización Panamericana de Salud (1974). Sistemas de vigilancia epidemiológica. Boletín de la Oficina Sanitaria Panamericana (OSP); 76 (6), jun. 1974.
  • Pappas, I. O.; Mikalef, P.; Giannakos, M. N.; Krogstie, J.; Lekakos, G. (2018). Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies. IseB, 16 (3), 479-491.
  • Ramírez-Montoya, M. S. (2020). Transformación digital e innovación educativa en Latinoamérica en el marco del COVID-19. Campus Virtuales, 9(2), 123-139.
  • Rashid, M. T.; Wang, D. (2020). CovidSens: a vision on reliable social sensing for COVID-19. Artificial Intelligence Review. doi:10.1007/s10462-020-09852-3.
  • Resiere, D.; Resiere, D.; Kallel, h. (2020). Implementation of Medical and Scientific Cooperation in the Caribbean Using Blockchain Technology in Coronavirus (Covid-19) Pandemics. Journal of Medical Systems, 44, 123. doi:10.1007/s10916-020-01589-4.
  • Revere, D.; Bugni, P.; Fuller, S. (2007). A Public Health Knowledge Management Repository that Includes Grey Literature. Publishing Research Quarterly, 23(1), 65-70. doi:10.1007/s12109-007-9002-6.
  • Safiullin, M. R.; Akhmetshin, E. M. (2019). Digital transformation of a university as a factor of ensuring its competitiveness. International Journal of Engineering and Advanced Technology, 9(1), 7387-7390.
  • Semantic Scholar (2020). CORD-19: COVID-19 Open Research Dataset and AI Challenge. (https://www.semanticscholar.org/cord19).
  • Sun, K. S.; Chen, J.; Viboud, C. (2020). Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study The Lancet Digital Health, 2(4), 201-208. doi:10.1016/S2589-7500(20)30026-1.
  • Tino, R.; Moore, R.; Antoline, S.; Ravi, P.; Wake, N.; Ionita, C. N.; ...; Chepelev, L. L. (2020). COVID-19 and the role of 3D printing in medicine. 3D Printing in Medicine, 6(11). doi:10.1186/s41205-020-00064-7.
  • Valenzuela, M. T. (2015). Vigilancia Epidemiológica. Universidad de los Andes. (https://www.sabin.org/sites/sabin.org/files/oct21_1000valenzuela.pdf).
  • Wang, J.; Chun, Y.; Brook, R. (2020). Response to COVID-19 in Taiwan: big data analytics, new technology, and proactive testing. JAMA, 323(14), 1341-1342.
  • Whitelaw, S.; Mamas, M. A.; Topol, E.; Van Spall, H. G. (2020). Applications of digital technology in COVID-19 pandemic planning and response. The Lancet Digital Health. doi:10.1016/S2589-7500(20)30142-4.
  • World Health Organization (2020). Global surveillance for COVID-19 caused by human infection with COVID-19 virus. WHO. Retrieved 2 April 2020.
  • Wu, J. T.; Leung, K.; Leung, G. M. (2020). Nowcasting and forecasting the potential domestic and international spread of the 2019- nCoV outbreak originating in Wuhan, China: a modelling study. Lancet, 2020; 395(10225), 689-697.
  • Xafis, V.; Schaefer, G. O.; Labude, M. K.; Zhu, Y.; Hsu, L. Y. (2020). The perfect moral storm: Diverse ethical considerations in the COVID-19 pandemic. Asian Bioethics Review.
  • Zamite, J.; Silva, F. A. B.; Couto, F.; Silva, M. J. (2010). MEDCollector: Multisource Epidemic Data Collector. In S. Khuri, L. Lhotská & N. Pisanti (Eds.), Information Technology in Bio- and Medical Informatics, ITBAM 2010 (pp. 16-30). Springer Berlin Heidelberg.
  • Zixin, H.; Qiyang, G.; Shudi, L.; Jin, L.; Xiong, M. (2020). Artificial intelligence, forecasting of Covid-19 in China. (https://arxiv.org/abs/2002.07112).
  • Zwitter, A.; Gstrein, O. J. (2020). Big data, privacy and COVID-19 – learning from humanitarian expertise in data protection. Journal of International Humanitarian Action. doi:10.1186/s41018-020-00072-6.
Fuente de los datos: Dialnet