Modeling COVID-19 cases to estimate the supply of PCR and hospital beds in health services
DOI:
https://doi.org/10.18041/2665-427X/ijeph.1.6604Keywords:
COVID-19, modeling, services supply, Gompertz mathematical modelAbstract
Objective: Determine the installed capacity of services to attend the COVID-19 pandemic based on statistical and mathematical techniques.
Methods: Describes four phases for modeling: 1. number of COVID cases, 2. use of historical information on the phenomenon, 3. non-linear statistical modeling, 4. mathematical modeling. For the latter, Gompertz was used as a model with which the projections of confirmed cases were made. The main data source was the data provided by the National Institute of Health of Colombia. The parameters to estimate the supply of services were based on the projection of cases and the parameters established according to the INS guidelines.
Results: The best fit models for the different stages of the pandemic were: in the exponential, the cubic nonlinear regression model with a coefficient of determination of 99.7%; for the exponential growth and stabilization stages, the Gompertz mathematical model. The combination of models made it possible to predict for August 26 a cumulative of 548,956 confirmed cases for Colombia. These predictions made it possible to establish the supply of services for a target population of six million (6,000,000) inhabitants in 26,226 Polymerase Chain Reaction tests - PCR for the month of August, 225 beds in Intensive Care Units - ICU and 660 hospital beds to that date.
Conclusions: The combination of simple and robust statistical and mathematical techniques allowed reliable estimates to project the needs of services related to CRP, hospital beds and ICU beds.
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References
CSSEGISandData / COVID-19. COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. [citado 4 de julio de 2020]. Disponible en: https://github.com/CSSEGISandData/COVID-19/
González-Jaramillo V, González-Jaramillo N, Gómez-Restrepo C, Palacio-Acosta CA, Gómez-López A, Franco OH. Impact of the COVID-19 pandemic on the Colombian population according to mitigation measures. Preliminary data from epidemiological models for the period March 18 to April 18, 2020. Rev Salud Publica. 2020; 22(1): e185789. Doi: 10.15446/rsap.v22.85789
MRC Centre for Global Infectious Disease Analysis. COVID-19 reports. Faculty of Medicine, Imperial College: London; 2020. Available from: https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/news--wuhan-coronavirus/
Plasencia TA. COVID-19: por qué los países deben informar sobre el número de pruebas realizadas. The Conversation; 2020; Available from: https://theconversation.com/covid-19-por-que-los-paises-deben-informar-sobre-el-numero-de-pruebas-realizadas-133307
Villalobos-Arias M. Estimation of population infected by Covid-19 using regression Generalized logistics and optimization heuristics. Cornell University; 2020; Available from: http://arxiv.org/abs/2004.01207
Damian VM. Simulaciones con el modelo SEIR del comportamiento epidémico del COVID-19 en el departamento de Guairá-Paraguay. Universidad Católica Nuestra Señora de la Asunción. Paraguay; 2020. Available from: https://www.researchgate.net/publication/340136048_Simulaciones_con_el_modelo_SEIR_del_comportamiento_epidemico_del_COVID-19_en_el_departamento_de_Guaira-Paraguay
Gómez EA, Rosendo MJA, Gonzalez CMA. Modelado y análisis de la evolución de una epidemia vírica mediante filtros de Kalman: el caso del COVID-19 en España. Universidad de Sevilla: España; 2020. Disponible en: https://idus.us.es/handle/11441/94508
Sanz I. Modelos epidemiológicos basados en ecuaciones diferenciales (Trabajo de Grado). Univ Rioja. 2016; 67. Available from: https://biblioteca.unirioja.es/tfe_e/TFE002211.pdf
Rodríguez J, Ulloa J. Alternativa didáctica para el estudio del modelo Gompertz. Investig Innov Matemática Educ. 2017; 2: 98-114.
Ridenhour B, Kowalik JM, Shay DK. El número reproductivo básico (R0): consideraciones para su aplicación en la salud pública. Am J Public Health. 2018; 108(Suppl 6): S455-65.
Navarro-Robles E, Martínez-Matsushita L, López-Molina R, Fritz-Hernández J, Flores-Aldana BA, Mendoza-Pérez JC. Modelo para estimación del comportamiento epidémico de la influenza A (H1N1) en México. Rev Panam Salud Publica. 2012; 31(4): 269-74
González-Jaramillo V, González-Jaramillo N, Gómez-Restrepo C, Franco OH, Palacio-Acosta CA, Gómez-López A. Proyecciones de impacto de la pandemia COVID-19 en la población colombiana, según medidas de mitigación. Datos preliminares de modelos epidemiológicos para el periodo del 18 de marzo al 18 de abril de 2020. Rev Salud Pública. 2020; 22(2): 1-6.
Mínguez FS. Procesos de difusión Logístico y Gompertz. Métodos numéricos clásicos en la estimación paramétrica. Master en Estadística Aplicada Departamento de Estadística e Investigación Operativa, Universidad de Granada: Granada, España. 2016. Disponible en: https://masteres.ugr.es/moea/pages/curso201516/tfm1516/simon_minguez_tfm/!
Martínez CA, Rodríguez AP, Jiménez A, Manrique C. Descripción Matemática De La Función Gompertz Aplicada Al Crecimiento De Animales. Rev la Fac Med Vet y Zootec. 2010; 57(I): 76–80.
Osejo C. Pronóstico de utilización total de la capacidad instalada combinada en los hospitales de la ciudad de Pasto, Colombia, con la epidemia del COVID-19, a marzo 17 de 2020. Rev Sigma. 2020; 14:14–24.
Presidencia de la República. Decreto 417. Por el cual se declara un Estado de Emergencia Económica, Social y Ecológica en todo el Territorio Nacional. Bogota, Colombia; 2020. Disponible en: https://dapre.presidencia.gov.co/normativa/normativa/DECRETO%20417%20DEL%2017%20DE%20MARZO%20DE%202020.pdf
Instituto Nacional de Salud. Número reproductivo efectivo Rt Nacional. Modelos de Estimación. Bogotá Colombia; 2020. Disponible en: https://www.ins.gov.co/Direcciones/ONS/modelos-de-estimacion.
Porter ME. What Is Strategy? Harvard Business Review. 1996; 74(6): 61–78
Disponible en: https://www.hbs.edu/faculty/Pages/item.aspx?num=10698
Park M, Cook AR, Lim JT, Sun Y, Dickens BL. A Systematic Review of COVID-19 Epidemiology Based on Current Evidence. J Clin Med. 2020; 9(4): 967. Doi: 10.3390/jcm9040967.
Yang CY, Wang J. A mathematical model for the novel coronavirus epidemic in Wuhan, China. Math Biosci Eng MBE. 2020; 17(3): 2708-24.
Ministerio de Salud y Protección Social. Lineamientos, orientaciones y protocolos para enfrentar la COVID-19 en Colombia. Ministerio de Salud y Protección Social: Bogotá; 2020. Disponible en: https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/VS/lineamientos-orientaciones-protocolos-covid19-compressed.pdf
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