Management of Healthcare Resources in the Gauteng Province, South Africa, During the COVID-19 Pandemic

32 Pages Posted: 15 Mar 2022

See all articles by Mahnaz Alavinejad

Mahnaz Alavinejad

York University - Department of Mathematics and Statistics

Bruce Mellado

University of the Witwatersrand

Ali Asgary

Africa-Canada Artificial Intelligence and Data Innovation Consortium

Mduduzi Mbada

Gauteng Health Department

Thuso Mathaha

University of the Witwatersrand

Benjamin Lieberman

University of the Witwatersrand

Finn Stevenson

University of the Witwatersrand

Nidhi Tripathi

University of the Witwatersrand

Abhaya Kumar Swain

University of Witwatersrand

James Orbinski

York University - Dahdaleh Institute for Global Health Research; York University - Faculty of Health; University of Toronto - Dalla Lana School of Public Health

Jianhong Wu

York University - Laboratory for Industrial and Applied Mathematics; Africa-Canada Artificial Intelligence and Data Innovation Consortium

Jude Dzevela Kong

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University

Date Written: March 3, 2022

Abstract

We conducted an observational retrospective study on patients hospitalized with COVID-19, during March 05, 2020, to October 28, 2021, and developed an agent-based model to evaluate effectiveness of recommended healthcare resource management strategies during the COVID-19 pandemic in Gauteng, South Africa. We measured the effectiveness of these strategies by calculating the number of deaths prevented by implementing them. We observed differences between the epidemic waves. The length of hospital stay (LOS) during the third wave was lower than the first two waves. The median of the LOS, was 6.73 days for the first wave, 6.63 days for the second wave and 6.78 days for the third wave. A combination of public and private sector provided hospital care to COVID-19 patients requiring ward and Intensive Care Units (ICU) beds. The private sector provided 88.4% of High care (HC)/ICU beds and 49.4% of ward beds during the first wave, 73.9% and 51.4% during the second wave, 71.8% and 58.3% during the third wave. Our simulation results showed that with a high maximum capacity, i.e., 10,000 general and isolation ward beds, 4,000 high care and ICU beds and 1,200 ventilators, increasing the resource capacity allocated to COVID-19 patients by 25% was enough to maintain bed availability throughout the epidemic waves. With a medium resource capacity (8,500 general and isolation ward beds, 3,000 high care and ICU beds and 1,000 ventilators) a combination of resource management strategies and their timing and criteria were very effective in maintaining bed availability and therefore preventing excess deaths. With a low number of maximum available resources (7,000 general and isolation ward beds, 2,000 high care and ICU beds and 800 ventilators) and a severe epidemic wave, these strategies were effective in maintaining the bed availability and minimizing the number of excess deaths for the entire province and throughout the epidemic wave.

Note:
Funding Information: This research is funded by Canada’s International Development Research Centre (IDRC) and the Swedish International Development Cooperation Agency (SIDA) (Grant No. 109559-001).

Conflict of Interests: None declared.

Keywords: COVID-19, healthcare resources management, Gauteng Province

JEL Classification: C63

Suggested Citation

Alavinejad, Mahnaz and Mellado, Bruce and Asgary, Ali and Mbada, Mduduzi and Mathaha, Thuso and Lieberman, Benjamin and Stevenson, Finn and Tripathi, Nidhi and Swain, Abhaya Kumar and Orbinski, James and Wu, Jianhong and Kong, Jude Dzevela, Management of Healthcare Resources in the Gauteng Province, South Africa, During the COVID-19 Pandemic (March 3, 2022). Available at SSRN: https://ssrn.com/abstract=4049177 or http://dx.doi.org/10.2139/ssrn.4049177

Mahnaz Alavinejad

York University - Department of Mathematics and Statistics ( email )

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Bruce Mellado

University of the Witwatersrand ( email )

1 Jan Smuts Avenue
Johannesburg, GA Gauteng 2000
South Africa

Ali Asgary

Africa-Canada Artificial Intelligence and Data Innovation Consortium ( email )

Mduduzi Mbada

Gauteng Health Department ( email )

GDOH, 78 Fox Street, Marshalltown
Johannesburg, Gauteng
South Africa

Thuso Mathaha

University of the Witwatersrand ( email )

1 Jan Smuts Avenue
Johannesburg, GA Gauteng 2000
South Africa

Benjamin Lieberman

University of the Witwatersrand ( email )

1 Jan Smuts Avenue
Johannesburg, GA Gauteng 2000
South Africa

Finn Stevenson

University of the Witwatersrand ( email )

1 Jan Smuts Avenue
Johannesburg, GA Gauteng 2000
South Africa

Nidhi Tripathi

University of the Witwatersrand ( email )

South Africa
South Africa

Abhaya Kumar Swain

University of Witwatersrand ( email )

jan smuts avenue
johannesburg
South Africa

James Orbinski

York University - Dahdaleh Institute for Global Health Research

88 The Pond Rd suite 5021
Toronto, ON M3J 2S5
Canada

York University - Faculty of Health

4700 Keele St.
Toronto, Ontario M3J 1P3
Canada

University of Toronto - Dalla Lana School of Public Health

Toronto, Ontario
Canada

Jianhong Wu

York University - Laboratory for Industrial and Applied Mathematics ( email )

Canada

Africa-Canada Artificial Intelligence and Data Innovation Consortium ( email )

Jude Dzevela Kong (Contact Author)

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University ( email )

4700 Keele St
Toronto, ON M3J 1P3
Canada

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