Temporal analysis of covid-19 in Colombia: associated indicators and modeling

Hugo Alexander  Rondón Quintana; Carlos Alfonso  Zafra Mejía

doi:10.22490/24629448.6187

Vol. 20 No. 38 (2022)

Published 2022-09-13

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Research Article (before OJS)

Análisis temporal del covid-19 en Colombia

DOI: https://doi.org/10.22490/24629448.6187

Hugo Alexander Rondón Quintana

Carlos Alfonso Zafra Mejía

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Abstract
References

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.

keywords: COVID-19, tasa de letalidad (TL), Colombia, estadística, modelización, pandemia

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

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Rondón Quintana, H. A. ., & Zafra Mejía, C. A. . (2022). Análisis temporal del covid-19 en Colombia: indicadores asociados y modelización. NOVA Biomedical Sciences Journal, 20(38), 121-140. https://doi.org/10.22490/24629448.6187

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Análisis temporal del covid-19 en Colombia

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