Palú OA, Texidor GMC, Portuondo PC, Miranda RSC, Manet LLR

Language: Spanish
References: 21
Page: 1-16
PDF size: 1250.30 Kb.

ABSTRACT

Introduction: Cuba early started its preparations to face the emergency due to COVID-19 and it has made a deployment of several scientific-technical capacities for it, among them the management of geographical information’s systems by GEOCUBA company.
Objective: Identify posible sites of geospatial risk related with the transmission of COVID- 19 in Santiago de Cuba province.
Methods: It was developed an ecologic study. There were identified groups of COVID-19 transmission and epidemiological risks. Epidemiological, social and spatial variables were resumed. Also there were made spatial analyses and overflights of drones as teleepidemiology techniques.
Results: There were identified five spatial groups of transmission: one in Palma Soriano municipality, one in Contramaestre municipality and three in Santiago de Cuba municipality. The spatial distances among the cases and confirmed cases were related with the form of transmission of COVID-19. There were identified low incidence rates. It was noticed low mobility, accomplishment of the social distancing rules and social protection.
Conclusions: The creation of a multidisciplinary group in Santiago de Cuba province, as a proposal of the highest level of the Communist Party and the Government, secured the deployment of technologic resources for the use of telepidemiology, and this allowed the spatial identification and further comprehensive management of ecoepidemiologic risks related with the transmission of COVID-19 in Santiago de Cuba. The different governmental actions, the perception of risk of the population, and the community response impacted in the low rates of transmission and spatial spreading of the disease, which shows the importance of worthing health as a social product.

REFERENCES

1. Organización Mundial de la Salud. Brote de enfermedad por coronavirus (COVID-19).Organización Mundial de la Salud. 2020. Ginebra: OMS; 2020. [acceso 03/08/2020].Disponible en: https://www.who.int/es/emergencies/diseases/novel-coronavirus 2019?gclid=EAIaIQobChMIh-30rvbN6wIVkeeGCh2GEwSTEAAYASAAEgK3XfD_BwE

2. Padrón Arredondo LJ. Las Nuevas Tecnologías de la Información (NTIC) en la medicina:la Telemedicina en Cuba. Revista cuba inf méd. 2006 [acceso 03/06/2020];6(1). Disponibleen: http://www.rcim.sld.cu/revista_10/articulos_htm/tecnologiainf.htm

3. Rodríguez-Morales AJ. Ecoepidemiología y epidemiología satelital: nuevas herramientasen el manejo de problemas en salud pública. Rev. perú. med. exp. salud publica. 2005[acceso 03/09/2020];22(1):54-63. Disponible en:http://www.scielo.org.pe/scielo.php?script=sci_arttext&pid=S1726-46342005000100009&lng=es

4. Rivas R, Galván P. Telemedicine in Paraguay: Contributions of the Institute of Researchin Health Sciences, National University of Asunción (IICS-UNA). Mem. Inst. Investig.Cienc. Salud. 2018;16(3):66-72. DOI: 10.18004/mem.iics/1812-9528/2018.016(03)66-072

5. Brazeau S, Kotchi SO, Ludwig A, Turgeon P, Pelcat Y, Aube G, Ogden NH. Tele-Epidemiology and Public Health in the Canadian Context. En: Living Planet Symposium,Proceedings of the conference held 9-13 May 2016 in Prague, Czech Republic. Edited by L.Ouwehand; ag. 2016. vol. 740 p. 7. (ESA-Special Publication) [acceso 03/06/2020].Disponible en: https://ui.adsabs.harvard.edu/abs/2016ESASP.740E...7B/abstract

6. Valbuena-García AM, Rodríguez-Villamizar LA. Análisis espacial en epidemiología:revisión de métodos. Rev. Univ. Ind. Santander. Salud. 2018 [acceso 03/06/2020];50(4):358-65. Disponible en: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0121-08072018000400358&lng=en

7. Declet Barreto J, Union of Concerned Scientists. Exploratory Spatial Data Analysis ofCOVID-19 infection rates and population vulnerability indicators. Union of ConcernedScientists. 2020 [acceso 03/06/2020];50(4):358-65. Disponible en:https://www.ucsusa.org/sites/default/files/2020-04/UCS-COVID-19-Population-Vulnerability-Analysis-Methods.pdf

8. Díaz Canel-Bermúdez M, Núñez Jover J. Gestión gubernamental y ciencia cubana en elenfrentamiento a la COVID-19. Anales de la Academia de Ciencias de Cuba. 2020 [acceso03/06/2020];10(2). Disponible en:http://www.revistaccuba.cu/index.php/revacc/article/view/881

9. Palú Orozco A, Vera Sánchez M, Orozco González MI, Brito Moreno AL. Gestiónespacial de riesgos entomoecológicos en Santiago de Cuba. MEDISAN. 2017 [acceso 03/09/2020];21(6):695-702. Disponible en:http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S1029-30192017000600008&lng=es

10. Ramirez Torres EE, Selva Castañeda AR, Rodríguez Aldana Y, Suárez-León AA,Sánchez Domínguez S, Valdés García LE, et al. Mathematical modeling and forecasting ofCOVID-19: experience in Santiago de Cuba province. Rev. Applied MathematicalModelling. “en prensa”. 2020.

11. The Center for Systems Science and Engineering (CSSE) at Johns Hopkins University.2020. 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns HopkinsCSSE. 2020 [acceso 03/07/2020] Disponible en:https://github.com/CSSEGISandData/COVID-19

12. Yaohao P, Mateus Hiro N. Statistical analysis of the Chinese COVID-19 data withBenford's Law and clustering. Laboratório de Aprendizado de Máquina em Finanças eOrganizações (LAMFO). 2020 [acceso 03/07/2020] Disponible en: https://lamfounb.github.io/2020/04/21/COVID-China-EN/

13. Desjardinsa MR, Hohlb A, Delmelle EM. Rapid surveillance of COVID-19 in the UnitedStates using a prospective space-time scan statistic: Detecting and evaluating emergingclusters. Elsevier. Applied Geography. may. 2020;118:102202. DOI:10.1016/j.apgeog.2020.102202

14. Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, Pastore A, et al.The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19)outbreak. Science. 2020;268(6489):395-400. DOI: 10.1126/science.aba9757

15. Micheli D, Muratore G, Vannelli A, Sola G. Un modellodinamico su un approcciobigdataallamobilità per lo studiodelladiffusione del covid-19 nel norditalia. Italia: Grupo TIM;2020 [acceso 15/07/2020]. Disponible en:https://www.gruppotim.it/content/tiportal/it/notiziariotecnico/edizioni-2020/n-1-2020/Modello-dinamico-approccio-Big-Data.html

16. Gatto M, Bertuzzo E, Mari L, Miccoli S, Carraro L, Casagrandi R, Rinaldo AProceedings of the National Academy of Sciences. 2020 [acceso15/07/2020];117(19):10484-91. Disponible en: https://www.pnas.org/content/117/19/10484

17. Epidemiology Working Group for NCIP Epidemic Response, Chinese Center for DiseaseControl and Prevention. [The Epidemiological Characteristics of an Outbreak of 2019 NovelCoronavirus Diseases (COVID-19) in China]. Zhonghua Liu Xing Bing Xue Za Zhi.2020;41(2):145-151. DOI: 10.3760/cma.j.issn.0254-6450.2020.02.003

18. Xu B, Gutierrez B, Mekaru S, Sewalk K, Goodwin L, Loskill A, et al. Epidemiologicaldata from the COVID-19 outbreak, real-time case information. Sci Data7. 2020;7(1):106.DOI: 10.1038/s41597-020-0448-0

19. Mollalo A, Vahedi B, Rivera KM. GIS-based spatial modeling of COVID-19 incidencerate in the continental United States. Science of The Total Environment. 2020;728:138884.DOI: 10.1016/j.scitotenv.2020.138884

20. Duczmal LH, Almeida Alexandre CL, Duczmal Denise B, Alves Claudia RL, MagalhãesFlávia CO, Lima Max S, et al. Vertical social distancing policy is ineffective to contain theCOVID-19 pandemic. Cad. Saúde Pública. 2020. [acceso 19/08/2020] Disponible en:https://pubmed.ncbi.nlm.nih.gov/32428075/

21. Alvarez Cabrera CE, Andrade Lotero EJ, Gauthier Umaña V. Modelos epidemiológicosen redes: una presentación introductoria. Colombia: Universidad del Rosario, Bogotá,Colombia, Departamento de Matemáticas, Facultad de Ciencias Naturales y Matemáticas.Boletín de Matemáticas. 2015 [acceso 19/06/2020];22(1):21-37. Disponible en:https://revistas.unal.edu.co/index.php/bolma/article/view/51844