COVID-19: Adaptation of a model to predict healthcare resource needs in Valle del Cauca, Colombia

Nicolas Iragorri; Carlos Gómez-Restrepo; Kali Barrett; Socrates Herrera; Isabel Hurtado; Yasin Khan; Stephen Mac; David Naimark; Petros Pechlivanoglou; Diego Rosselli; Dilian Toro; Pedro Villamizar; Raphael Ximenes; Helmer Zapata; Beate Sander

doi:10.25100/cm.v51i3.4534

COVID-19: Adaptation of a model to predict healthcare resource needs in Valle del Cauca, Colombia

Título traducido de la contribución: COVID-19: Adaptación de un modelo para predecir las necesidades de recursos de salud en el Valle del Cauca, Colombia

Nicolas Iragorri, Carlos Gómez-Restrepo, Kali Barrett, Socrates Herrera, Isabel Hurtado, Yasin Khan, Stephen Mac, David Naimark, Petros Pechlivanoglou, Diego Rosselli, Dilian Toro, Pedro Villamizar, Raphael Ximenes, Helmer Zapata, Beate Sander

Producción: Contribución a una revista › Artículo › revisión exhaustiva

4 Citas (Scopus)

Resumen

Antecedentes: el Valle del Cauca es la región con el cuarto mayor número de casos de COVID-19 en Colombia (> 50.000 al 7 de septiembre de 2020). Debido a la falta de terapias anti-COVID-19, los tomadores de decisiones requieren datos precisos y oportunos para estimar la incidencia de la enfermedad y la disponibilidad de recursos hospitalarios para contener la pandemia.
Métodos: Adaptamos un modelo existente al contexto local para pronosticar la incidencia de COVID-19 y el uso de recursos hospitalarios asumiendo diferentes escenarios: (1) la implementación de cuarentena del 1 de septiembre al 15 de octubre (tasa de crecimiento promedio diario de 2%); (2-3) restricciones parciales (a tasas de crecimiento del 4% y 8%); y (4) sin restricciones, asumiendo una tasa de crecimiento del 10%. También se presentaron escenarios previos con predicciones de junio a agosto. Estimamos el número de casos nuevos, las pruebas diagnósticas necesarias y el número de camas hospitalarias y de unidades de cuidados intensivos (UCI) disponibles (con y sin ventiladores) para cada escenario.
Resultados: Estimamos 67,700 casos al 15 de octubre al asumir la implementación de una cuarentena, 80,400 y 101,500 casos al asumir restricciones parciales a tasas de infección del 4% y 8%, respectivamente, y 208,500 sin restricciones. Según diferentes escenarios, la demanda estimada de pruebas de reacción en cadena de la polimerasa con transcripción inversa osciló entre 202.000 y 1.610.600 entre el 1 de septiembre y el 15 de octubre. El modelo predijo el agotamiento de las camas de hospitales y UCI para el 20 de septiembre si se levantaran todas las restricciones y la tasa de crecimiento de la infección aumentara al 10%.
Conclusión: No se espera que el levantamiento lento de las restricciones de distanciamiento social y la reapertura de la economía provoquen un agotamiento total de los recursos en octubre si la tasa de crecimiento diario se mantiene por debajo del 8%. Aumentar el número de camas disponibles proporciona una protección contra tasas de infección ligeramente más altas. Los modelos predictivos se pueden utilizar de forma iterativa para obtener predicciones matizadas que ayuden a la toma de decisiones.

Título traducido de la contribución	COVID-19: Adaptación de un modelo para predecir las necesidades de recursos de salud en el Valle del Cauca, Colombia
Idioma original	Inglés
Número de artículo	e204534
Páginas (desde-hasta)	1-17
Número de páginas	17
Publicación	Colombia Medica
Volumen	51
N.º	3
DOI	https://doi.org/10.25100/cm.v51i3.4534
Estado	Publicada - 2020

Palabras clave

COVID-19
Colombia
Global health
SARS-CoV-2
Valle del Cauca
Healthcare resources
Resource constraints

Acceder al documento

10.25100/cm.v51i3.4534

Otros archivos y enlaces

Ver registro en Web of Science

Citar esto

Iragorri, N., Gómez-Restrepo, C., Barrett, K., Herrera, S., Hurtado, I., Khan, Y., Mac, S., Naimark, D., Pechlivanoglou, P., Rosselli, D., Toro, D., Villamizar, P., Ximenes, R., Zapata, H., & Sander, B. (2020). COVID-19: Adaptation of a model to predict healthcare resource needs in Valle del Cauca, Colombia. Colombia Medica, 51(3), 1-17. Artículo e204534. https://doi.org/10.25100/cm.v51i3.4534

@article{301a8326899d47e29a8f865edc9487c6,

title = "COVID-19: Adaptation of a model to predict healthcare resource needs in Valle del Cauca, Colombia",

abstract = "Background: Valle del Cauca is the region with the fourth-highest number of COVID-19 cases in Colombia (>50,000 on September 7, 2020). Due to the lack of anti-COVID-19 therapies, decision-makers require timely and accurate data to estimate the incidence of disease and the availability of hospital resources to contain the pandemic.Methods: We adapted an existing model to the local context to forecast COVID-19 incidence and hospital resource use assuming different scenarios: (1) the implementation of quarantine from September 1st to October 15th (average daily growth rate of 2%); (2-3) partial restrictions (at 4% and 8% growth rates); and (4) no restrictions, assuming a 10% growth rate. Previous scenarios with predictions from June to August were also presented. We estimated the number of new cases, diagnostic tests required, and the number of available hospital and intensive care unit (ICU) beds (with and without ventilators) for each scenario.Results: We estimated 67,700 cases by October 15th when assuming the implementation of a quarantine, 80,400 and 101,500 cases when assuming partial restrictions at 4% and 8% infection rates, respectively, and 208,500 with no restrictions. According to different scenarios, the estimated demand for reverse transcription-polymerase chain reaction tests ranged from 202,000 to 1,610,600 between September 1st and October 15th. The model predicted depletion of hospital and ICU beds by September 20th if all restrictions were to be lifted and the infection growth rate increased to 10%.Conclusion: Slowly lifting social distancing restrictions and reopening the economy is not expected to result in full resource depletion by October if the daily growth rate is maintained below 8%. Increasing the number of available beds provides a safeguard against slightly higher infection rates. Predictive models can be iteratively used to obtain nuanced predictions to aid decision-making",

keywords = "COVID-19, Colombia, Global health, SARS-CoV-2, Valle del Cauca, Healthcare resources, Resource constraints",

author = "Nicolas Iragorri and Carlos G{\'o}mez-Restrepo and Kali Barrett and Socrates Herrera and Isabel Hurtado and Yasin Khan and Stephen Mac and David Naimark and Petros Pechlivanoglou and Diego Rosselli and Dilian Toro and Pedro Villamizar and Raphael Ximenes and Helmer Zapata and Beate Sander",

note = "Publisher Copyright: {\textcopyright} 2020 Universidad del Valle.",

year = "2020",

doi = "10.25100/cm.v51i3.4534",

language = "English",

volume = "51",

pages = "1--17",

journal = "Colombia Medica",

issn = "0120-8322",

publisher = "Facultad de Salud de la Universidad del Valle",

number = "3",

}

Iragorri, N, Gómez-Restrepo, C, Barrett, K, Herrera, S, Hurtado, I, Khan, Y, Mac, S, Naimark, D, Pechlivanoglou, P, Rosselli, D, Toro, D, Villamizar, P, Ximenes, R, Zapata, H & Sander, B 2020, 'COVID-19: Adaptation of a model to predict healthcare resource needs in Valle del Cauca, Colombia', Colombia Medica, vol. 51, n.º 3, e204534, pp. 1-17. https://doi.org/10.25100/cm.v51i3.4534

TY - JOUR

T1 - COVID-19

T2 - Adaptation of a model to predict healthcare resource needs in Valle del Cauca, Colombia

AU - Iragorri, Nicolas

AU - Gómez-Restrepo, Carlos

AU - Barrett, Kali

AU - Herrera, Socrates

AU - Hurtado, Isabel

AU - Khan, Yasin

AU - Mac, Stephen

AU - Naimark, David

AU - Pechlivanoglou, Petros

AU - Rosselli, Diego

AU - Toro, Dilian

AU - Villamizar, Pedro

AU - Ximenes, Raphael

AU - Zapata, Helmer

AU - Sander, Beate

PY - 2020

Y1 - 2020

N2 - Background: Valle del Cauca is the region with the fourth-highest number of COVID-19 cases in Colombia (>50,000 on September 7, 2020). Due to the lack of anti-COVID-19 therapies, decision-makers require timely and accurate data to estimate the incidence of disease and the availability of hospital resources to contain the pandemic.Methods: We adapted an existing model to the local context to forecast COVID-19 incidence and hospital resource use assuming different scenarios: (1) the implementation of quarantine from September 1st to October 15th (average daily growth rate of 2%); (2-3) partial restrictions (at 4% and 8% growth rates); and (4) no restrictions, assuming a 10% growth rate. Previous scenarios with predictions from June to August were also presented. We estimated the number of new cases, diagnostic tests required, and the number of available hospital and intensive care unit (ICU) beds (with and without ventilators) for each scenario.Results: We estimated 67,700 cases by October 15th when assuming the implementation of a quarantine, 80,400 and 101,500 cases when assuming partial restrictions at 4% and 8% infection rates, respectively, and 208,500 with no restrictions. According to different scenarios, the estimated demand for reverse transcription-polymerase chain reaction tests ranged from 202,000 to 1,610,600 between September 1st and October 15th. The model predicted depletion of hospital and ICU beds by September 20th if all restrictions were to be lifted and the infection growth rate increased to 10%.Conclusion: Slowly lifting social distancing restrictions and reopening the economy is not expected to result in full resource depletion by October if the daily growth rate is maintained below 8%. Increasing the number of available beds provides a safeguard against slightly higher infection rates. Predictive models can be iteratively used to obtain nuanced predictions to aid decision-making

AB - Background: Valle del Cauca is the region with the fourth-highest number of COVID-19 cases in Colombia (>50,000 on September 7, 2020). Due to the lack of anti-COVID-19 therapies, decision-makers require timely and accurate data to estimate the incidence of disease and the availability of hospital resources to contain the pandemic.Methods: We adapted an existing model to the local context to forecast COVID-19 incidence and hospital resource use assuming different scenarios: (1) the implementation of quarantine from September 1st to October 15th (average daily growth rate of 2%); (2-3) partial restrictions (at 4% and 8% growth rates); and (4) no restrictions, assuming a 10% growth rate. Previous scenarios with predictions from June to August were also presented. We estimated the number of new cases, diagnostic tests required, and the number of available hospital and intensive care unit (ICU) beds (with and without ventilators) for each scenario.Results: We estimated 67,700 cases by October 15th when assuming the implementation of a quarantine, 80,400 and 101,500 cases when assuming partial restrictions at 4% and 8% infection rates, respectively, and 208,500 with no restrictions. According to different scenarios, the estimated demand for reverse transcription-polymerase chain reaction tests ranged from 202,000 to 1,610,600 between September 1st and October 15th. The model predicted depletion of hospital and ICU beds by September 20th if all restrictions were to be lifted and the infection growth rate increased to 10%.Conclusion: Slowly lifting social distancing restrictions and reopening the economy is not expected to result in full resource depletion by October if the daily growth rate is maintained below 8%. Increasing the number of available beds provides a safeguard against slightly higher infection rates. Predictive models can be iteratively used to obtain nuanced predictions to aid decision-making

KW - COVID-19

KW - Colombia

KW - Global health

KW - SARS-CoV-2

KW - Valle del Cauca

KW - Healthcare resources

KW - Resource constraints

UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=pure_puj3&SrcAuth=WosAPI&KeyUT=WOS:000596733900004&DestLinkType=FullRecord&DestApp=WOS_CPL

U2 - 10.25100/cm.v51i3.4534

DO - 10.25100/cm.v51i3.4534

M3 - Article

C2 - 33402754

AN - SCOPUS:85095567236

SN - 0120-8322

VL - 51

SP - 1

EP - 17

JO - Colombia Medica

JF - Colombia Medica

IS - 3

M1 - e204534

ER -

COVID-19: Adaptation of a model to predict healthcare resource needs in Valle del Cauca, Colombia

Resumen

Palabras clave

Acceder al documento

Otros archivos y enlaces

Huella

Citar esto