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2021, Number 1

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Rev cubana med 2021; 60 (1)

Identification of clusters in COVID-19 cases in the Santiago de Cuba province

Palu OA, Rafael OE, Valdés GLE, Bergues CLE, Zamora ML, Bandera JD, Rodríguez VA, Fernández CCA, Rubio RM, Castro CD

Language: Spanish
References: 19
Page: 1-9
PDF size: 297.22 Kb.


ABSTRACT

Introduction: From the onset of COVID-19 epidemic, a multidisciplinary team is formed in Santiago de Cuba with the participation of several institutions and activated by the Provincial Defense Council. Integrated epidemiological analysis, government management and social response would be decisive in controlling the disease.
Objectives: To identify possible groups of COVID-19 cases in the Santiago de Cuba province and to describe the transmission according to epidemiological variables.
Methods: An ecological study was carried out, comparing COVID-19 transmission clusters. Variables of interest were summarized and analysis of social contact networks was carried out from the point of view of the relationships between cases and contacts, as well as spatial analysis.
Results: Five spatial transmission groups were identified in the municipalities, one in Palma Soriano, one in Contramaestre and three in Santiago de Cuba. The personal pathological antecedents (hypertension and respiratory processes), female sex, symptomatic cases and the average of 22 to 27 contacts for each confirmed were the most relevant variables. A source of introduced infection was identified in 51% (25/49). In addition, complex social networks were identified in the transmission of the disease.
Conclusions: The transmission of COVID-19 in Santiago de Cuba province showed groups of cases and contacts with characteristic epidemiological social networks for each municipality, as well as the mode of transmission according to the source of infection, relationships of familiarity or social closeness and the relationship of spatial distance between contacts, which influenced on the low incidence rates of the disease, with predominance of symptomatic form, young ages and in women.


REFERENCES

  1. Ortega Lenis D, Arango Londoño D, Muñoz E, Cuartas Daniel E, Caicedo D, Mena J, et al.Predictions of a SEIR model for COVID-19 cases in Cali-Colombia. Rev Salud Pública. 2020[17/06/2020]22(2):e286432. Disponible en:http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0124-00642020000202101&lng=en

  2. Serrano Barrera OR. Predicción de la inmunogenicidad de la proteína del SARS-CoV-2responsable de la infección COVID-19 en humanos. Rev. electron. Zoilo. 2020[17/06/2020];45(3).Disponible en: http://www.revzoilomarinello.sld.cu/index.php/zmv/article/view/2270

  3. Vilar Scavuzzi de Carvalho AR, Cezarotti Filho ML, Pires de Azevedo PC, Silveira Filho RN,Barbosa FT, Matos Rocha TJ, et al. Epidemiology, diagnosis, treatment, and future perspectivesconcerning SARS-COV-2: a review article. Rev. Assoc. Med. Bras. 2020[17/06/2020];66(3).Disponible en: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0104-42302020000300370&lng=en

  4. Valbuena-Garcia AM, Rodriguez-Villamizar LA. Análisis espacial en epidemiología: revisión demétodos. Rev. Univ. Ind. Santander. Salud. 2018[16/05/2020];50(4). Disponible en:http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0121-08072018000400358&lng=en

  5. Declet-Barreto J. Exploratory Spatial Data Analysis of COVID-19 infection rates and populationvulnerability indicators. Union of Concerned Scientists. 2020[16/05/2020]. Disponible en:https://www.ucsusa.org/sites/default/files/2020-04/UCS-COVID-19-Population-Vulnerability-Analysis-Methods.pdf

  6. The Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. 2020. 2019Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE.https://github.com/CSSEGISandData/COVID-19

  7. Peng Y, Hiro Nagata M. Statistical analysis of the Chinese COVID-19 data with Benford's Law andclustering. Laboratório de Aprendizado de Máquina em Finanças e Organizações (LAMFO).2020[16/05/2020]. Disponible en: https://lamfo-unb.github.io/2020/04/21/COVID-China-EN/

  8. Desjardinsa A, Hohlb D. Rapid surveillance of COVID-19 in the United States using a prospectivespace-time scan statistic: Detecting and evaluating emerging clusters. Elsevier. AppliedGeography. 2020[16/05/2020];(118). Disponible en:https://www.sciencedirect.com/science/article/pii/S0143622820303039

  9. Shikha Garg LK, Whitaker M, O’Halloran A, Cummings C, Holstein R, Prill M, et al.Hospitalization rates and characteristics of patients hospitalized with laboratory-confirmedcoronavirus disease 2019. Centers for Disease Control and Prevention. Morbidity and MortalityWeekly Report. Weekly. 2020[16/05/2020];69(15). Disponible en:https://www.cdc.gov/mmwr/volumes/69/wr/mm6915e3.htm?s%20cid=mm6915e3%20w

  10. Zhiliang H, Song CC, Guangfu J, Yaling C, Xin X, Hongxia M, Wei C, et al. Clinicalcharacteristics of 24 asymptomatic infections with COVID-19 screened among close contacts inNanjing, China. China. Science China. Life Sciences. 2020[acceso: 22/05/2020];63(5). Disponibleen: https://doi.org/10.1007/s11427-020-1331-4

  11. Ministerio de Salud Pública. Anuario estadístico de salud. 2019. La Habana, Cuba.2020[22/05/2020]. Disponible en: http://bvscuba.sld.cu/anuario-estadistico-de-cuba/

  12. Giralt-Herrera A, Rojas-Velázquez JM, Leiva-Enríquez J. Relación entre COVID-19 eHipertensión Arterial. Revista Habanera de Ciencias Médicas. 2020[22/05/2020];19(2). Disponibleen: http://www.revhabanera.sld.cu/index.php/rhab/article/view/3246

  13. Hiroshi N, Tetsuro K, Takeshi M, Ayako S, Sungmok J, Katsuma H, et al. Estimation of theasymptomatic ratio of novel coronavirus infections (COVID-19). International Journal of InfectiousDiseases. medRxiv 2020.02.03.20020248; doi: https://doi.org/10.1101/2020.02.03.20020248

  14. Guan W, Ni Z, Yu Hu Y, Liang WH, Ou CQ, Jian-xing He JX, et al. Clinical Characteristics ofCoronavirus Disease 2019 in China. N Engl J Med. 2020;382:1708-20. Doi.https://doi.org/10.1056/NEJMoa2002032

  15. 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[22/06/2020];10(2). Disponible en:http://www.revistaccuba.cu/index.php/revacc/article/view/881

  16. Duczmal Luiz H, Almeida Leite AC, Duczmal Bulgarelli D, Alves Lindgren CR, Costa Oliveira F,Lima Max Sousa de, et al. Vertical social distancing policy is ineffective to contain the COVID-19pandemic. Cad. Saúde Pública. 2020[22/05/2020];36(5):e00084420. Disponible en:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-311X2020000506002&lng=en

  17. China CDC Weekly. The Epidemiological Characteristics of an Outbreak of 2019 NovelCoronavirus Diseases (COVID-19)-China, 2020[22/05/2020]. Disponible en:TheEpidemiologicalCharacteristicsofanOutbreakof2019NovelCoronavirusDiseases28COVID-1929E28094China2C20201.pdf

  18. Alvarez Cabrera CE, Andrade Lotero EJ, Gauthier Umaña V. Modelos epidemiológicos en redes:una presentación introductoria. Boletín de Matemáticas. 2020;22(1):21-37. Disponible en:https://revistas.unal.edu.co/index.php/bolma/article/viewFile/51844/51641

  19. Palú Orozco A, Vera Sánchez M, Orozco González MI, Brito Moreno AL. Gestión espacial deriesgos entomoecológicos en Santiago de Cuba. Medisan. 2017[26/05/2020];21(6):695-702.Disponible en: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S1029-30192017000600008&lng=es




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