Análisis temporal del covid-19 en Colombia
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. Objetivo. Mostrar información estadística sobre COVID-19 que permita ayudar a planificar y diseñar, en futuras Pandemias, estrategias de política de salud pública en Colombia. Metodología. 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. Resultados y conclusiones. 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.
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
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
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
Bauch CT, Oraby T. Assessing the pandemic potential of MERS-CoV. Lancet, 2013;382:662–664. 10.1016/ S0140-6736(13)615044.
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
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
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]
World Health Organization. Middle East respiratory syndrome coronavirus (MERSCoV). 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]
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
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
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
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
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.
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 theUnited States. Clin Infect Dis. 2010;50(2):165-74.10.1086/649867.
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
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
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
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 limiteddata set context. medRxiv. 2020;2020:1-4110.1101/2020.04.29.20081174
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
Perez-Saez J, Lauer SA, Kaiser L, Regard S, Delaporte E, Guessous I, et al. Serology-informed estimates of SarsCOV-2 infection fatality risk in Geneva, Switzerland. Lancet Infect Dis. 2020;21(4):e69-e70. 10.1016/S1473-3099(20)30584-3
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.
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
Gao J, Zheng P, Jia Y, Chen H, Mao Y, Chen S, et al. Mental health problems and social mediaexposure during COVID-19 outbreak. PloS One.2020;15(4):e0231924. 10.1371/journal.pone.0231924
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
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
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
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
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
Guan W-J, Liang W-H, Zhao Y, Liang H-R, Chen Z-S, Li Y-M, et al. Comorbidity and its impact on1590 patients with covid-19 in China: a nationwideanalysis. Eur Respir J 2020;55(5):2000547.10.1183/13993003.00547-2020.
Richardson S, Hirsch JS, Narasimhan M, Crawford JM, Mcginn T, Davidson KW, et al. Presenting characteristics, comorbidities, and outcomes among5700 patients hospitalized with COVID-19 in the NewYork City area. Jama. 2020;323(20):2052e9. 10.1001/jama.2020.6775.
Yang W, Kandula S, Huynh M, Greene S, Van WyeG, Li W, et al. Estimating the infection-fatality risk of SARS-CoV-2 in New York City during the spring2020 pandemic wave: a model-based analysis. Lancet Infect Dis. 2021;21(2):203-212. 10.1016/S1473-3099(20)30769-6
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
Xiong D, Zhang L, Watson GL, Sundin P, BuffordT, 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
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
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
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
Díaz-Guio DA, Villamil-Gómez WE, Dajud L, PérezDíaz 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
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
Rondón-Quintana HA, Zafra-Mejía CA. Covid 19 death analysis in Colombia. Revista Cuidarte. 2021;12(3):e1528. 10.15649/cuidarte.1528
Mikiko W, Risi R, Tuccinardi D. Obesity and SARSCoV-2: a population to safeguard. Diabetes Metab Res Rev. 2020;36:e3325. 10.1002/dmrr.3325
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.
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
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
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
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
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
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 metaanalysis. BMJ., 2013;347:f5061. 10.1136/bmj.f5061
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.
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.
Tekin S, Keske S, Alan S, Batirel A, Karakoc C, TasdelenFisgin 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
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 52. 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.
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
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
Henao-Cespedes V, Garcés-Gómez YA, Ruggeri S, HenaoCespedes 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
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
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
Ghosh D, Bernstein JA, Mersha TB. COVID-19 pandemic: the African paradox. J Glob Health. 2020;10(2):020348.10.7189/jogh.10.020348
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.
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 infive African countries. Glob Food Sec. 2021;31:100571.10.1016/j.gfs.2021.100571.
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
Ngere I, Dawa J, Hunsperger E, Otieno N, Masika M, Amoth P, et al. High seroprevalence of SARSCoV-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
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 Hyg2020;103:564–9. 10.4269/ajtmh.20-0474
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.
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-19pandemic response in sub-Saharan Africa. Dev. WorldBioeth. 2021;21(1):25-30. 10.1111/dewb.12275.
Tso FY, Lidenge SJ, Peña PB, Clegg AA, Ngowi JR, Mwaiselage J, et al. High prevalence of pre-existingserological 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
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
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
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
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.
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
Torrealba-Rodriguez O, Conde-Gutiérrez RA, HernándezJavier AL. Modeling and prediction of COVID-19 in Mexico applying mathematical and computationalmodels. Chaos, Solitons & Fractals. 2020;138:109946.10.1016/j.chaos.2020.109946
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
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
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
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
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
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
Ministerio de Salud (MINSALUD). Lineamientos para la prevención, diagnóstico, manejo y control de casos de Influenza. 2018 [Consulted on 1 of May of2021]. https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/VS/PP/lineamientos-prevenciondiagnostico-manejo-control-casos-influenza.pdf
Copyright (c) 2022 NOVA Biomedical Sciences Journal
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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,
Declaración de privacidad.
Los nombres y las direcciones de correo electrónico introducidos en esta revista se usarán exclusivamente para los fines establecidos en ella y no se proporcionarán a terceros o para su uso con otros fines.
