ANÁLISIS TEMPORAL DEL COVID-19 EN COLOMBIA: INDICADORES ASOCIADOS Y MODELIZACIÓN

TEMPORAL ANALYSIS OF COVID-19 IN COLOMBIA: ASSOCIATED INDICATORS AND MODELLING

Publicado 2022-09-09

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Vol. 20 Núm. 38 (2022): Enero - Junio
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Rondón-Quintana, H. A., & Zafra-Mejía, C. A. (2022). ANÁLISIS TEMPORAL DEL COVID-19 EN COLOMBIA: INDICADORES ASOCIADOS Y MODELIZACIÓN. REVISTA NOVA , 20(38). https://doi.org/10.22490/24629448.6187
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https://doi.org/10.22490/24629448.6187
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NOVA por http://www.unicolmayor.edu.co/publicaciones/index.php/nova se distribuye bajo una Licencia Creative Commons Atribución-NoComercial-SinDerivar 4.0 Internacional.

Así mismo,  los autores mantienen sus derechos de propiedad intelectual sobre los artículos.  

Hugo Alexander Rondón-Quintana
Universidad Distrital Francisco José de Caldas
    https://orcid.org/0000-0003-2946-9411

    Carlos Alfonso Zafra-Mejía
    Universidad Distrital Francisco José de Caldas
      https://orcid.org/0000-0002-4061-4897

      Mostrar biografía de los autores

      Este estudio muestra información estadística sobre el COVID-19 en Colombia a la fecha (1 de marzo de 2022). Específicamente, la evolución diaria, mensual y acumulada de contagios y defunciones, correlacionada con la distribución de la población según edad y sexo. Esta información puede ayudar a planificar y diseñar, en futuras pandemias, estrategias de política de salud pública. La información diaria desde la declaratoria oficial de pandemia en Colombia (16 de marzo de 2020) fue obtenida del Instituto Nacional de Salud (INS) y fue organizada en una base de datos para realizar los análisis respectivos. Esta información se comparó con estudios similares obtenidos a partir de revisión bibliográfica. Los resultados y conclusiones son similares a los encontrados en la literatura de referencia: la mayor parte de los fallecidos por COVID-19 son de edad avanzada y sexo masculino. En cuanto a la tasa de letalidad (CFR), ésta aumenta notoriamente con la edad. La población más vulnerable presenta una edad promedio ≥ 52.8 años. La población menos vulnerable son los jóvenes menores de 30 años, pero específicamente, los que se encuentran en el rango de edad de 10 y 20 años. Los modelos Gompertz y Logistic pueden simular matemáticamente la evolución de las muertes y la evolución de la CFR según la edad.

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      1. Guan W, Ni Z, Yu H, Liang W, Ou C., He J, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020;382:1708-1720. 10.1056/NEJMoa2002032. https://doi.org/10.1056/NEJMoa2002032
      3. Xie M, Chen Q. Insight into 2019 novel coronavirus - An updated interim review and lessons from SARS-CoV and MERS-CoV. Int J Infect Dis. 2000;94:119-124. 10.1016/j.ijid.2020.03.071 1201-9712. https://doi.org/10.1016/j.ijid.2020.03.071
      5. Chen Y, Klein SL, Garibaldi BT, Li H, Wu C, Osevala N, et al. Aging in COVID-19: Vulnerability, immunity and intervention. Ageing Res Rev. 2021;65:101205. 10.1016/j.arr.2020.101205 https://doi.org/10.1016/j.arr.2020.101205
      7. Bauch CT, Oraby T. Assessing the pandemic potential of MERS-CoV. Lancet, 2013;382:662-664. 10.1016/S0140-6736(13)615044. https://doi.org/10.1016/S0140-6736(13)61504-4
      9. Liu Y, Gayl AA, Wilder-Smith A, Rocklov J. The reproductive number of COVID-19 is higher compared to SARS coronavirus. J. Travel Med. 2020;27(2):1-4. 10.1093/jtm/taaa021. https://doi.org/10.1093/jtm/taaa021
      11. Berber E, Sumbria D, Çanakoğlu N. Meta-analysis and comprehensive study of coronavirus outbreaks: SARS, MERS and COVID-19. J Infect Public Health. 2021;14(8):1051-1064. 10.1016/j.jiph.2021.06.007. https://doi.org/10.1016/j.jiph.2021.06.007
      13. WHO, Department of Communicable Disease Surveillance and Response World Health Organization. Consensus document on the epidemiology of severe acute respiratory syndrome (SARS) p. 10. 2003. Retrieved from https://www.who.int/csr/sars/en/WHOconsensus.pdf. [Accessed 20 September 2021]
      15. World Health Organization. Middle East respiratory syndrome coronavirus (MERS-CoV). Retrieved from https://applications.emro.who.int/docs/EMRPUB-CSR-241-2019-EN.pdf?ua=1%26ua=1%26ua=1%26ua=1%26ua=1%26ua=1 [Accessed 20 September 2021]
      17. Guzman NA, De la Hoz-Restrepo F, Serrano-Coll H, Gastelbondo B, Mattar S. Using serological studies to assess COVID-19 infection fatality rate in developing countries: A case study from one Colombian department. Int J Infect Dis. 2021;110:4-5. 10.1016/j.ijid.2021.06.018. https://doi.org/10.1016/j.ijid.2021.06.018
      19. Kucirka LM, Lauer SA, Laeyendecker O, Boon D, Lessler J. Variation in false-negative rate of reverse transcriptase polymerase chain reaction-based SARS-CoV-2 tests by time since exposure. Ann Intern Med. 2020;173(4):262-267. 10.7326/M20-1495. https://doi.org/10.7326/M20-1495
      21. Fernández-Barat L, López-Aladid R, Torres A. The value of serology testing to manage SARS-CoV-2 infections. Eur Respir J. 2020;56(2):2002411. 10.1183/13993003.02411-2020. https://doi.org/10.1183/13993003.02411-2020
      23. Xiao AT, Tong YX, Zhang, S. False negative of RT-PCR and prolonged nucleic acid conversion in COVID-19: Rather than recurrence. J Med Virol. 2020;92(10):1755-1756. 10.1002/jmv.25855.Epub 2020 Jul 11. https://doi.org/10.1002/jmv.25855
      25. Meyerowitz-Katz G, Merone L. A systematic review and meta-analysis of published research data on COVID-19 infection-fatality rates. Int J Infect Dis. 2020;101:138-148. https://doi.org/10.1016/j.ijid.2020.09.1464
      27. Perlroth D, Glass RJ, Davey VJ, Cannon D, Garber AM, Owens DK. Health outcomes and costs of community mitigation strategies for an influenza pandemic in the United States. Clin Infect Dis. 2010;50(2):165-74. 10.1086/649867.
      28. https://doi.org/10.1086/649867
      30. Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, et al. Estimates of the severity of coronavirus disease 2019: A model-based analysis. Lancet Infect Dis. 2020;20:669-77. 10.1016/S1473-3099(20)30243-7
      32. Salje H, Kiem C, Lefrancq N, Courtejoie N, Bosetti P, Paireau J, et al. Estimating the burden of SARS-CoV-2 in France. Science. 2020;369(6500):208-211. 10.1126/science.abc3517. https://doi.org/10.1126/science.abc3517
      34. Roques L, Klein EK, Papaix J, Sar A, Soubeyrand S. Using early data to estimate the actual infection fatality ratio from Covid-19 in France. Biology. 2020;9(5):97. 10.3390/biology9050097. https://doi.org/10.3390/biology9050097
      36. Dana S, Simas AB, Filardi BA, Rodriguez RN, Lane Valiengo L, Gallucci-Neto J. Brazilian modeling of COVID-19 (BRAM-COD): A Bayesian Monte Carlo approach for COVID-19 spread in a limited data set context. medRxiv. 2020;2020:1-41 10.1101/2020.04.29.20081174. https://doi.org/10.1101/2020.04.29.20081174
      38. Mellan TA, Hoeltgebaum HH, Mishra S, Whittaker C, Schnekenberg RP, Gandy A, et al. Report 21: Estimating COVID-19 cases and reproduction number in Brazil. medRxiv. 2020:1-24. 10.1101/2020.05.09.20096701
      40. Perez-Saez J, Lauer SA, Kaiser L, Regard S, Delaporte E, Guessous I, et al. Serology-informed estimates of Sars-COV-2 infection fatality risk in Geneva, Switzerland. Lancet Infect Dis. 2020;21(4):e69-e70. 10.1016/S1473-3099(20)30584-3
      41. https://doi.org/10.31219/osf.io/wdbpe
      43. Marra V, Quartin M. Bayesian estimate of the early COVID-19 infection fatality ratio in Brazil based on a random seroprevalence survey. Int J Infect Dis. 2021;111:190-195. 10.1016/j.ijid.2021.08.016. https://doi.org/10.1016/j.ijid.2021.08.016
      45. Luo G, Zhang X, Zheng H, He D. Infection fatality ratio and case fatality ratio of COVID-19. Int J Infect Dis. 2021;113:43-46. 10.1016/j.ijid.2021.10.004. https://doi.org/10.1016/j.ijid.2021.10.004
      47. Gao J, Zheng P, Jia Y, Chen H, Mao Y, Chen S, et al. Mental health problems and social media exposure during COVID-19 outbreak. PloS One. 2020;15(4):e0231924. 10.1371/journal.pone.0231924. https://doi.org/10.1371/journal.pone.0231924
      49. Rodriguez-Nava G, Yanez-Bello MA, Trelles-Garcia DP, Chung CW, Chaudry S, Khan AS, et al. Clinical characteristics and risk factors for mortality of hospitalized patients with COVID-19 in a community hospital: a retrospective cohort study. Mayo Clin Proc Innov Qual Outcomes. 2021;5(1):1-10. 10.1016/j.mayocpiqo.2020.10.007. https://doi.org/10.1016/j.mayocpiqo.2020.10.007
      51. Liu S, Yang L, Zhang C, Xiang Y, Liu Z, Hu S. et al. Online mental health services in China during the COVID19 outbreak. The lancet Psychiatry. 2020;7(4):e17-e18. 10.1016/S2215-0366(20)30077-8. https://doi.org/10.1016/S2215-0366(20)30077-8
      53. García-Posada M, Aruachan-Vesga S, Mestra D, Humánez K, Serrano-Coll H, Cabrales H, et al. Clinical outcomes of patients hospitalized for COVID-19 and evidence-based on the pharmacological management reduce mortality in a region of the Colombian Caribbean. J Infect Public Health. 2021;14(6):696-701. 10.1016/j.jiph.2021.02.013
      54. https://doi.org/10.1016/j.jiph.2021.02.013
      56. Sharma P, Sharma R. Impact of covid-19 on mental health and aging. Saudi J Biol Sci. 2021;28(12): 7046-7053. 10.1016/j.sjbs.2021.07.087. https://doi.org/10.1016/j.sjbs.2021.07.087
      58. Aguiar M, Stollenwerk N. Condition-specific mortality risk can explain differences in COVID-19 case fatality ratios around the globe. Public Health. 2020;188:18-20. 10.1016/j.puhe.2020.08.021. https://doi.org/10.1016/j.puhe.2020.08.021
      60. Guan W-J, Liang W-H, Zhao Y, Liang H-R, Chen Z-S, Li Y-M, et al. Comorbidity and its impact on 1590 patients with covid-19 in China: a nationwide analysis. Eur Respir J 2020;55(5):2000547. 10.1183/13993003.00547-2020. https://doi.org/10.1183/13993003.01227-2020
      62. Richardson S, Hirsch JS, Narasimhan M, Crawford JM, Mcginn T, Davidson KW, et al. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. Jama. 2020;323(20):2052e9. 10.1001/jama.2020.6775. https://doi.org/10.1001/jama.2020.6775
      64. Yang W, Kandula S, Huynh M, Greene S, Van Wye G, Li W, et al. Estimating the infection-fatality risk of SARS-CoV-2 in New York City during the spring 2020 pandemic wave: a model-based analysis. Lancet Infect Dis. 2021;21(2):203-212. 10.1016/S1473-3099(20)30769-6. https://doi.org/10.1016/S1473-3099(20)30769-6
      66. Yang J, Zheng Y, Gou X, Pu K, Chen Z, Guo Q, et al. Prevalence of comorbidities in the novel Wuhan coronavirus (COVID-19) infection: a systematic review and meta-analysis. Int J Infect Dis. 2020;94:91-95. 10.1016/j.ijid.2020.03.017
      67. https://doi.org/10.1016/j.ijid.2020.03.017
      69. Xiong D, Zhang L, Watson GL, Sundin P, Bufford T, Zoller JA, et al. Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender, race, and age in California. Epidemics. 2020;33:100418. 10.1016/j.epidem.2020.100418. https://doi.org/10.1016/j.epidem.2020.100418
      71. Alshogran OY, Altawalbeh SM, Al-Azzam SI, Karasneh R. Predictors of Covid-19 case fatality rate: An ecological study. Ann Med Surg. 2021;65:102319. 10.1016/j.amsu.2021.102319. https://doi.org/10.1016/j.amsu.2021.102319
      73. Chen JT, Krieger N. Revealing the unequal burden of COVID-19 by income, race/ethnicity, and household crowding: US county versus zip code analyses. J Public Health Manag Pract. 2021;27:S46-S56. 10.1097/PHH.0000000000001263
      74. https://doi.org/10.1097/PHH.0000000000001263
      76. Abate SM, Chekole YA, Estifanos M, Abate KH, Kabthymer RH. Prevalence and outcomes of malnutrition among hospitalized COVID-19 patients: A systematic review and meta-analysis. Clinical Nutrition ESPEN. 2021;43:174-183. 10.1016/j.clnesp.2021.03.002. https://doi.org/10.1016/j.clnesp.2021.03.002
      78. Díaz-Guio DA, Villamil-Gómez WE, Dajud L, Pérez-Díaz CE, Bonilla-Aldana K, Mondragón A, et al. Will the Colombian intensive care units collapse due to the COVID-19 pandemic? Travel Med Infect Dis. 2020;38:101746. 10.1016/j.tmaid.2020.101746. https://doi.org/10.1016/j.tmaid.2020.101746
      80. Rodriguez-Villamizar LA, Belalcázar-Ceron LC, Fernández-Niño JA, Marín-Pineda DM, Rojas OA, Acuña L, et al. Air pollution, sociodemographic and health conditions effects on COVID-19 mortality in Colombia: An ecological study. Sci Total Environ. 2021;756:144020. 10.1016/j.scitotenv.2020.144020. https://doi.org/10.1016/j.scitotenv.2020.144020
      82. Rondón-Quintana HA, Zafra-Mejía CA. Covid 19 death analysis in Colombia. Revista Cuidarte. 2021;12(3):e1528. 10.15649/cuidarte.1528. https://doi.org/10.15649/cuidarte.1528
      84. Mikiko W, Risi R, Tuccinardi D. Obesity and SARS-CoV-2: a population to safeguard. Diabetes Metab Res Rev. 2020;36:e3325. 10.1002/dmrr.3325
      86. Sánchez-Ramirez DC, Mackey D. Underlying respiratory diseases, specifically COPD, and smoking are associated with severe COVID-19 outcomes: A systematic review and meta-analysis. Respir Med. 2020;171:106096. 10.1016/j.rmed.2020.10609639. https://doi.org/10.1016/j.rmed.2020.106096
      88. Sharma A, Garg A, Rout A, Lavie CJ. Association of obesity with more critical illness in COVID-19. Mayo Clinic Proc. 2020;95(9):2040-2042. 10.1016/j.mayocp.2020.06.046. https://doi.org/10.1016/j.mayocp.2020.06.046
      90. Sharma JR, Yadav U.C.S. COVID-19 severity in obese patients: potential mechanisms and molecular targets for clinical intervention. Obes Res Clin Pract. 2021;15(2):163-171. 10.1016/j.orcp.2021.01.004. https://doi.org/10.1016/j.orcp.2021.01.004
      92. Yadav R, Aggarwal S, Singh A. SARS-CoV-2-host dynamics: Increased risk of adverse outcomes of COVID-19 in obesity. Diabetes Metab Syndr: Clinical Res & Rev. 2020;14(5):1355-1360. 10.1016/j.dsx.2020.07.030. https://doi.org/10.1016/j.dsx.2020.07.030
      94. Landecho MF, Marin-Oto M, Recalde-Zamacona B, Bilbao I, Frühbeck G. Obesity as an adipose tissue dysfunction disease and a risk factor for infections - Covid-19 as a case study. Eur J Intern Med. 2021;91:3-9. 10.1016/j.ejim.2021.03.031
      95. https://doi.org/10.1016/j.ejim.2021.03.031
      97. Stefano M, Andrea B, Daniela C, Emanuela M, Lorena P, Daniela D, et al. Malnutrition risk as a negative prognostic factor in COVID-19 patients. Clinical Nutrition ESPEN. 2021;45:369-373. 10.1016/j.clnesp.2021.07.016
      98. https://doi.org/10.1016/j.clnesp.2021.07.016
      100. Mertz D, Kim TH, Johnstone J, Lam PP, Science M, Kuster SP, et al. Populations at risk for severe or complicated influenza illness: systematic review and meta-analysis. BMJ., 2013;347:f5061. 10.1136/bmj.f5061
      101. https://doi.org/10.1136/bmj.f5061
      103. Pearce DC, McCaw JM, McVernon J, Mathews JD. Influenza as a trigger for cardiovascular disease: An investigation of serotype, subtype and geographic location. Environ Res. 2017;156:688-696. 10.1016/j.envres.2017.04.024.
      104. https://doi.org/10.1016/j.envres.2017.04.024
      106. Goeijenbier M, van Sloten TT, Slobbe L, Mathieuf C, van Genderen P, Beyer W, Osterhaus A. Benefits of flu vaccination for persons with diabetes mellitus: A review. Vaccine. 2017;35(38):5095-5101. 10.1016/j.vaccine.2017.07.095.
      107. https://doi.org/10.1016/j.vaccine.2017.07.095
      109. Tekin S, Keske S, Alan S, Batirel A, Karakoc C, Tasdelen-Fisgin N, et al. Predictors of fatality in influenza A virus subtype infections among inpatients in the 2015-2016 season. Int J Infect Dis. 2019;81:6-9. 10.1016/j.ijid.2019.01.005
      110. https://doi.org/10.1016/j.ijid.2019.01.005
      112. Zhang ZXZ, Kyaw W, Ho HJ, Tay MZ, Huang H, Hein AA, et al. Seasonal influenza-associated intensive care unit admission and death in tropical Singapore, 2011-2015. J Clin Virol. 2019;117:73-79. 10.1016/j.jcv.2019.06.005
      113. https://doi.org/10.1016/j.jcv.2019.06.005
      115. Zou Q, Zheng S, Wang X, Liu S, Bao J, Yu F, et al. Influenza A-associated severe pneumonia in hospitalized patients: Risk factors and NAI treatments. Int J Infect Dis. 2020;92:208-213. 10.1016/j.ijid.2020.01.017.
      116. https://doi.org/10.1016/j.ijid.2020.01.017
      118. Polidori MC, Sies H, Ferrucci L, Benzing T. COVID-19 mortality as a fingerprint of biological age. Ageing Res Rev. 2021;67:101308. 10.1016/j.arr.2021.101308. https://doi.org/10.1016/j.arr.2021.101308
      120. Tian F, Liu X, Chao Q, Qian Z, Zhang S, Qi L, et al. Ambient air pollution and low temperature associated with case fatality of COVID-19: A nationwide retrospective cohort study in China. The Innovation. 2021;2(3):100139. 10.1016/j.xinn.2021.100139. https://doi.org/10.1016/j.xinn.2021.100139
      122. Henao-Cespedes V, Garcés-Gómez YA, Ruggeri S, Henao-Cespedes TM. Relationship analysis between the spread of COVID-19 and the multidimensional poverty index in the city of Manizales, Colombia. Egypt J Remote Sens Space Sci. 2021, in press. 10.1016/j.ejrs.2021.04.002. https://doi.org/10.1016/j.ejrs.2021.04.002
      124. Sepulveda ER, Brooker A. Income inequality and COVID-19 mortality: Age-stratified analysis of 22 OECD countries. SSM - Population Health. 2021;16:100904. 10.1016/j.ssmph.2021.100904. https://doi.org/10.1016/j.ssmph.2021.100904
      126. Wildman J. COVID-19 and income inequality in OECD countries. Eur. J. Health Econ. 2021;22(3):455-462. 10.1007/s10198-021-01266-4. https://doi.org/10.1007/s10198-021-01266-4
      128. Ghosh D, Bernstein JA, Mersha TB. COVID-19 pandemic: the African paradox. J Glob Health. 2020;10(2):020348. 10.7189/jogh.10.020348. https://doi.org/10.7189/jogh.10.020348
      130. Lawal Y. Africa's low COVID-19 mortality rate: a paradox? Int. J. Infect. Dis. 2020;102:118-122. 10.1016/j.ijid.2020.10.038.
      131. https://doi.org/10.1016/j.ijid.2020.10.038
      133. Birner R, Blaschke N, Bosch C, Daum T, Graf S, Guttler D et al. 'We would rather die from Covid-19 than from hunger' - Exploring lockdown stringencies in five African countries. Glob Food Sec. 2021;31:100571. 10.1016/j.gfs.2021.100571.
      134. https://doi.org/10.1016/j.gfs.2021.100571
      136. Kulohoma BW. COVID-19 risk factors: The curious case of Africa's governance and preparedness. Scientific African. 2021;13:e00948. 10.1016/j.sciaf.2021.e00948. https://doi.org/10.1016/j.sciaf.2021.e00948
      138. Ngere I, Dawa J, Hunsperger E, Otieno N, Masika M, Amoth P, et al. High seroprevalence of SARS-CoV-2 but low infection fatality ratio eight months after introduction in Nairobi, Kenya. Int J Infect Dis. 2021;112:25-34. 10.1016/j.ijid.2021.08.062. https://doi.org/10.1016/j.ijid.2021.08.062
      140. Njenga MK, Dawa J, Nanyingi M, Gachohi J, Ngere I, Letko M, et al. Why is There Low Morbidity and Mortality of COVID-19 in Africa? Am J Trop Med Hyg 2020;103:564-9. 10.4269/ajtmh.20-0474. https://doi.org/10.4269/ajtmh.20-0474
      142. Diop BZ, Ngom M, Biyong CP, Biyong JNP. The relatively young and rural population may limit the spread and severity of COVID-19 in Africa: a modelling study. BMJ Glob Health. 2020;5:e002699. 10.1136/bmjgh-2020-002699. https://doi.org/10.1136/bmjgh-2020-002699
      144. Afolabi MO, Folayan MO, Munung NS, Yakubu A, Ndow G, Jegede A, Ambe J, Kombe F. Lessons from the Ebola epidemics and their applications for COVID-19 pandemic response in sub-Saharan Africa. Dev. World Bioeth. 2021;21(1):25-30. 10.1111/dewb.12275. https://doi.org/10.1111/dewb.12275
      146. Tso FY, Lidenge SJ, Peña PB, Clegg AA, Ngowi JR, Mwaiselage J, et al. High prevalence of pre-existing serological cross-reactivity against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in sub-Saharan Africa. Int J Infect Dis. 2021;102:577-583. 10.1016/j.ijid.2020.10.104. https://doi.org/10.1016/j.ijid.2020.10.104
      148. Benítez MA, Velasco C, Sequeira AR, Henríquez J, Menezes F, Paolucci F. Responses to COVID-19 in five Latin American countries. Health Policy Technol. 2020;9(4):525-559. 10.1016/j.hlpt.2020.08.014. https://doi.org/10.1016/j.hlpt.2020.08.014
      150. Han J, Shi L-X, Xie Y, Zhang Y-J, Huang S-P, Li J-G, et al. Analysis of factors affecting the prognosis of COVID-19 patients and viral shedding duration. Epidemiol Infect. 2020;148:e125. 10.1017/S0950268820001399. https://doi.org/10.1017/S0950268820001399
      152. Rosas F, Vargas JP. 2015. Capacidad de respuesta hospitalaria distrital en Bogotá ante un evento con múltiples víctimas. Especialización en Medicina de Emergencias, Universidad del Rosario. https://repository.urosario.edu.co/handle/10336/10154
      154. Guerrero N, Yépez-Ch M C. Factores asociados a la vulnerabilidad del adulto mayor con alteraciones de salud [Factors associated with the vulnerability of the elderly with health disorders]. Universidad y Salud. 2015;17(1):121-31.
      156. Sánchez-Villegas P, Daponte A. Modelos predictivos de la epidemia de COVID-19 en España con curvas de Gompertz. Gaceta Sanitaria. 2021;35(6):585-589. 10.1016/j.gaceta.2020.05.005. https://doi.org/10.1016/j.gaceta.2020.05.005
      158. Torrealba-Rodriguez O, Conde-Gutiérrez RA, Hernández-Javier AL. Modeling and prediction of COVID-19 in Mexico applying mathematical and computational models. Chaos, Solitons & Fractals. 2020;138:109946. 10.1016/j.chaos.2020.109946. https://doi.org/10.1016/j.chaos.2020.109946
      160. Shen CY. Logistic growth modelling of COVID-19 proliferation in China and its international implications. Int J Infect Dis. 2020;96:582-589. 10.1016/j.ijid.2020.04.085. https://doi.org/10.1016/j.ijid.2020.04.085
      162. Aviv-Sharon E, Aharoni A. Generalized logistic growth modeling of the COVID-19 pandemic in Asia. Infect Dis Model. 2020;5:502-509. 10.1016/j.idm.2020.07.003. https://doi.org/10.1016/j.idm.2020.07.003
      164. Wang P, Zheng X, Li J, Zhu B. Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics. Chaos, Solitons & Fractals. 2020;139:110058. 10.1016/j.chaos.2020.110058. https://doi.org/10.1016/j.chaos.2020.110058
      166. Mohammadi F, Pourzamani H, Karimi H, Mohammadi M, Mohammadi M, Ardalan N, et al. Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran. Biomedical J. 2021;44(3):304-316. 10.1016/j.bj.2021.02.006. https://doi.org/10.1016/j.bj.2021.02.006
      168. Wu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, et al. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern. Med. 2020;180(7):934-943. 10.1001/jamainternmed.2020.0994. https://doi.org/10.1001/jamainternmed.2020.0994
      170. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A novel coronavirus from patients with pneumonia in China, 2019. N. Engl. J. Med. 2020;382:727-733. 10.1056/NEJMoa2001017. https://doi.org/10.1056/NEJMoa2001017
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