medigraphic.com
ISSN 1561-3003 (Electronic)

2021, Number 1

<< Back Next >>

Rev Cubana Hig Epidemiol 2021; 58 (1)

Prospective spatiotemporal detection of COVID-19 clusters in Cuba

Montano VDN, Abreu JY, Germán AÁM, Iñiguez RLB, Percedo AMI, Borroto GSM, Alfonso ZP

Language: Spanish
References: 32
Page: 1-18
PDF size: 1241.72 Kb.


ABSTRACT

Introduction: During the occurrence of ongoing emerging infectious diseases such as COVID-19, spatiotemporal surveillance is crucial to identify priority areas for specific interventions, differentiate diagnostic intensity and assign resources.
Objective: To model the evolution of the relative risk of presentation of COVID-19 cases and to identify clusters in municipalities where the disease remains at the stage following the descent of the epidemic curve in Cuba.
Methods: The period mentioned was from 26/05/2020 to 4/09/2020. Cyclic runs of Poisson's prospective spatiotemporal model were performed, with progressive 14-day increases, using the software SaTScan™ 9.6.
Results: A total 15 significant clusters were identified (p ≤ 0.0001) extending over one to thirteen municipalities and distributed in six provinces (Pinar del Río, Artemisa, Havana, Mayabeque, Matanzas, Villa Clara and Ciego de Ávila). In the clusters, all municipalities showed a high relative risk among them, La Palma in Pinar del Rio province and Ciego de Avila in the province of the same name, with the highest values, 119.95 and 121.04, respectively.
Conclusion: The model was able to identify territories with a significant likelihood of COVID-19 occurrence, as well as periods in the evolution of relative risk. It also showed that surveillance and early warning strategies may facilitate prioritization of health control and containment interventions aimed at preventing the reemergence of the disease with greater spatial coverage.


REFERENCES

  1. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A Novel Coronavirus fromPatients with Pneumonia in China, 2019. N Engl J Med. 2020 [acceso09/08/2020];382(8):727-33.Disponible en:https://www.pubmed.ncbi.nlm.nih.gov/31978945/

  2. World Health Organization. WHO Director-General’s opening remarks at the mediabriefing on COVID-19-11. Ginebra: WHO; 2020 [acceso 28/11/2020]. Disponible en:https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarksat-the-media-briefing-on-covid-19---11-march-2020

  3. World Health Organization. Coronavirus Disease (COVID-19) Dashboard. Ginebra:WHO; 2021 [acceso 5/04/2021]. Disponible en: https://covid19.who.int/update---5-april-2021

  4. Qiu J. Covert coronavirus infections could be seeding new outbreaks. Nature. 2020[acceso 27/08/2020]:32203376. Disponible en:https://www.pubmed.ncbi.nlm.nih.gov/32203376/

  5. Li R, Pei S, Chen B, Song Y, Zhang T, Yang W, et al. Substantial undocumentedinfection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science.2020 [acceso 09/09/2020];368(6490):489-93. Disponible en:https://www.pubmed.ncbi.nlm.nih.gov/32179701/

  6. Bonaccorsi G, Pierri F, Cinelli M, Flori A, Galeazzi A, Porcelli F, et al. Economic andsocial consequences of human mobility restrictions under COVID-19. Proceedings of theNational Academy of Sciences. 2020 [acceso 15/07/2020];117(27):15530-5. Disponibleen: https://www.pnas.org/content/117/27/15530

  7. Borroto Gutiérrez S. Vigilancia epidemiológica frente a la COVID-19 en Cuba. Boletínde la OPS/OMS en Cuba. 2020 [acceso 15/07/2020];24(2):10-4. Disponible en:https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiZ9fS8oebrAhWspFkKHaCLDPcQFjAAegQIBhAB&url=https%3A%2F%2Firis.paho.org%2Fbitstream%2Fhandle%2F10665.2%2F52514%2Fv24n2.pdf.pdf%3Fsequence%3D1%26isAllowed%3Dy&usg=AOvVaw2Vqulyi4k7G8tnoYYO1H5e

  8. Presidencia y Gobierno de Cuba. Nota informativa sobre el inicio de la primera etapay fase 1 de la recuperación pos-COVID-19. Cuba: Consejo de Ministros; 2020 [acceso20/07/2020]. Disponible en: https://www.presidencia.gob.cu/es/noticias/nota informativa-sobre-el-inicio-de-la-primera-etapa-y-fase-1-de-la-recuperacion-poscovid-19/

  9. COVID19 CUBADATA. Datos en tiempo real de la evolución de la epidemia de COVID-19 en Cuba. Cuba: Cubadata; 2020 [acceso 05/04/2020]. Disponible en:http://www.covid19cubadata.github.io/#cuba

  10. Desjardins MR, Hohl A, Delmelle EM. Rapid surveillance of COVID-19 in the UnitedStates using a prospective space-time scan statistic: Detecting and evaluating emergingcluster. Appl Geography. 2020 [acceso 09/06/2020];118:e102202. Disponible en:https://www.pubmed.ncbi.nlm.nih.gov/32287518/

  11. Kulldorff M. Prospective time periodic geographical disease surveillance using a scanstatistic. J Royal Statist Soc Series A. 2020 [acceso 07/08/2020];164(1):61–72.Disponible en: https://www.rss.onlinelibrary.wiley.com/doi/10.1111/1467-985X.00186

  12. Mulatti P, Mazzucato M, Montarsi F, Ciocchetta S, Capelli G, Bonfan Marangon S.Retrospective space–time analysis methods to support west nile virus surveillanceactivities. Epidemiol Infect. 2015;143(1):202–13. doi:https://doi.org/10.1017/S0950268814000442

  13. Whiteman A, Desjardins M, Eskildsen G, Loaiza J. Detecting space-time clusters ofdengue fever in panama after adjusting for vector surveillance data. PLoS Negl TropDis. 2018;13(9):e0007266. Disponible en:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6776363/

  14. Alkhamis MA, Youha Sarah Al, Khajah MM, Haider NB, Alhardan S, Nabeel A, et al.Spatiotemporal dynamics of the COVID-19 pandemic in the State of Kuwait. Int J InfectDis. 2020;98:153–60. doi: https://doi.org/10.1016/j.ijid.2020.06.078

  15. Andrade LA, Gomes DS, de Oliveira MA, Feitosa de Souza MS, Pizzi DC, Nunes CJ, etal. Surveillance of COVID-19 in Sergipe. Rev Soc Bras Med Trop. 2020;53:e20200287.doi: http://doi.org/10.1590/0037-8682-0287-2020

  16. Amin R, Hall T, Church J, Schlierf D, Kulldorff M. Geographical surveil- lance ofcovid-19: diagnosed cases and death in the United States. MedRxiv. 2020 [acceso14/10/2020]. Disponible en:https://www.medrxiv.org/content/10.1101/2020.05.22.20110155v1

  17. Durán N, Botello E. Detección de conglomerados “activos” emergentes de altastasas de incidencia, para la vigilancia rápida de la COVID-19. Medicentro Electrón. 2020[acceso 12/12/2020];24(3):642-55. Disponible en:http://www.scielo.sld.cu/scielo.php?pid=S1029-30432020000300642&script=sci_arttext&tlng=en

  18. Jones RC, Liberatore M, Fernandez JR, Gerber SI. Uso de una estadística deexploración prospectiva de espacio-tiempo para priorizar las investigaciones de casos de shigelosis en una jurisdicción urbana. Rev Salud Pública. 2006; 121(2):133-139. doi:http//doi.org/10.1177 / 003335490612100206

  19. Hohl A, Delmelle E, Desjardins M, Lan Y. Daily surveillance of COVID-19 using theprospective space-time scan statistic in the United States. Spat SpatiotemporalEpidemiol. 2020 [acceso 15/12/2020];34:e100354. Disponible en:https://www.sciencedirect.com/science/article/pii/S1877584520300320

  20. Greene SK, Peterson ER, Kapell D, Fine AD, Kulldorff M. Daily re-portable diseasespatiotemporal cluster detection, New York City, New York, USA, 2014–2015. EmergInfect Dis. 2016 [acceso 13/10/2020];22(10):1808-12. Disponible en:https://www.pubmed.ncbi.nlm.nih.gov/27648777/

  21. Kulldorff M, Athas WF, Feurer EJ, Miller BA, Key CR. Evaluating cluster alarms: Aspace-time scan statistic and brain cancer in Los Alamos, New Mexico. Am J PublicHealth. 1998 [acceso 18/10/2020];88(9):1377–80. Disponible en:https://www.pubmed.ncbi.nlm.nih.gov/9736881/

  22. Kulldorff M. SaTScanTM user guide for version 9.6. EE. UU.: SaTScan; 2018 [acceso05/05/2020]. Disponible en: https://www.satscan.org/

  23. Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, et al. The incubationperiod of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases:estimation and application. Ann Intern Med. 2020 [acceso 12/10/2020];172(9):577-82.Disponible en: https://www.pubmed.ncbi.nlm.nih.gov/32150748/

  24. Hohl A, Delmelle E, Desjardins M. Rapid detection of covid-19 clusters in the UnitedStates using a prospective space-time scan statistic: an update. SIGSPATIAL Special.2020 [acceso 12/12/2020];12(1):2733. Disponible en:https://www.pages.uncc.edu/eric-delmelle/wpcontent/uploads/sites/150/2020/11/Rapid-detection-of-COVID-19-clusters-in-the-United-States-an-Update-B.pdf

  25. Kraemer MU, Yang CH, Gutierrez B, Wu CH, Klein B, Pigott DM, et al. The effect ofhuman mobility and control measures on the COVID-19 epidemic in China. Science. 2020[acceso 19/12/2020];368(6490):493-7.Disponible en:https://www.science.sciencemag.org/content/368/6490/493

  26. Kwok KO, Lai F, Wei WI, Wong SYS, Tang JW. Herd immunity estimating the levelrequired to halt the COVID-19 epidemics in affected countries. J Infect. 2020 [acceso12/12/2020];80(6):e32-e33. Disponible en:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7151357/

  27. Fontanet A, Cauchemez S. COVID-19 herd immunity: where are we? Nat RevImmunol. 2020 [acceso 12/01/2021];20:583–4. Disponible en:https://www.nature.com/articles/s41577-020-00451-5

  28. Rothe C, Schunk M, Sothmann P, Bretzel G, Froeschl G, Wallrauch C, et al.Transmission of 2019-nCoV infection from an asymptomatic contact in Germany. N Engl J Med. 2020 [acceso 12/01/2021];382:970–1. Disponible en:https://www.pubmed.ncbi.nlm.nih.gov/32003551/

  29. Phan LT, Nguyen TV, Luong QC, Nguyen TV, Nguyen HT, Le HQ. Importation andhuman-to-human transmission of a novel coronavirus in Vietnam. N Engl J Med. 2020[acceso 13/01/2021];382:872–4. Disponible en:https://pubmed.ncbi.nlm.nih.gov/31991079/

  30. Swelum AA, Shafi ME, Albaqami NM, El-Saadony MT, Elsify A, Abdo M, et al. COVID-19 in Human, Animal, and Environment: A Review. Front Vet. Sci. 2020 [acceso15/01/2021];7:578. Disponible en: https://pubmed.ncbi.nlm.nih.gov/33102545/

  31. Ballesteros P, Salazar E, Sánchez D, Bolaños C. Aglomeración espacial yespaciotemporal de la pandemia por COVID-19 en Ecuador. Rev Fac Med. 2021; 69(1).doi: http://dx.doi.org/10.15446/revfacmed.v69n1.86476.

  32. Ferreira RV, Martines MR, Toppa RH, Assunção LM, Desjardins MR, Delmelle EM.Applying a Prospective Space-Time Scan Statistic to Examine the Evolution of COVID-19Clusters in the State of São Paulo, Brazil. MedRxiv. 2020 [acceso14/08/2020].Disponible en:https://www.medrxiv.org/content/10.1101/2020.06.04.20122770v1




2020     |     www.medigraphic.com