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

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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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]

8. 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]

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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.
https://doi.org/10.1086/649867

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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
https://doi.org/10.31219/osf.io/wdbpe

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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
https://doi.org/10.1016/j.jiph.2021.02.013

27. 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

28. 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

29. 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

30. 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

31. 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

32. 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
https://doi.org/10.1016/j.ijid.2020.03.017

33. 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

34. 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

35. 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
https://doi.org/10.1097/PHH.0000000000001263

36. 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

37. 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

38. 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

39. 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

40. 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

41. 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

42. 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

43. 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

44. 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

45. 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
https://doi.org/10.1016/j.ejim.2021.03.031

46. 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
https://doi.org/10.1016/j.clnesp.2021.07.016

47. 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
https://doi.org/10.1136/bmj.f5061

48. 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.
https://doi.org/10.1016/j.envres.2017.04.024

49. 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.
https://doi.org/10.1016/j.vaccine.2017.07.095

50. 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
https://doi.org/10.1016/j.ijid.2019.01.005

51. 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
https://doi.org/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.
https://doi.org/10.1016/j.ijid.2020.01.017

53. 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

54. 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

55. 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

56. 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

57. 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

58. 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

59. 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.
https://doi.org/10.1016/j.ijid.2020.10.038

60. 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.
https://doi.org/10.1016/j.gfs.2021.100571

61. 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

62. 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

63. 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

64. 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

65. 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

66. 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

67. 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

68. 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

69. 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

70. 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.

71. 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

72. 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

73. 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

74. 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

75. 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

76. 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

77. 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

78. 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