dc.contributor.author |
Olivier, Laurentz Eugene
|
|
dc.contributor.author |
Botha, Stefan
|
|
dc.contributor.author |
Craig, Ian Keith
|
|
dc.date.accessioned |
2021-05-04T08:58:12Z |
|
dc.date.available |
2021-05-04T08:58:12Z |
|
dc.date.issued |
2020-11 |
|
dc.description.abstract |
To curb the spread of COVID-19, many governments around the world have implemented tiered
lockdowns with varying degrees of stringency. Lockdown levels are typically increased when the disease
spreads and reduced when the disease abates. A predictive control approach is used to develop optimized
lockdown strategies for curbing the spread of COVID-19. The strategies are then applied to South African
data. The South African case is of interest as the South African government has defined five distinct levels of
lockdown, which serves as a discrete control input. An epidemiological model for the spread of COVID-19 in
South Africa was previously developed, and is used in conjunction with a hybrid model predictive controller
to optimize lockdown management under different policy scenarios. Scenarios considered include how to
flatten the curve to a level that the healthcare system can cope with, how to balance lives and livelihoods, and
what impact the compliance of the population to the lockdown measures has on the spread of COVID-19.
The main purpose of this article is to show what the optimal lockdown level should be given the policy that
is in place, as determined by the closed-loop feedback controller. |
en_ZA |
dc.description.department |
Electrical, Electronic and Computer Engineering |
en_ZA |
dc.description.librarian |
pm2021 |
en_ZA |
dc.description.uri |
http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 |
en_ZA |
dc.identifier.citation |
L. E. Olivier, S. Botha and I. K. Craig, "Optimized Lockdown Strategies for Curbing the Spread of COVID-19: A South African Case Study," in IEEE Access, vol. 8, pp. 205755-205765, 2020, doi: 10.1109/ACCESS.2020.3037415. |
en_ZA |
dc.identifier.issn |
2169-3536 (online) |
|
dc.identifier.other |
10.1109/ACCESS.2020.3037415 |
|
dc.identifier.uri |
http://hdl.handle.net/2263/79757 |
|
dc.language.iso |
en |
en_ZA |
dc.publisher |
Institute of Electrical and Electronics Engineers |
en_ZA |
dc.rights |
© This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. |
en_ZA |
dc.subject |
Epidemiology |
en_ZA |
dc.subject |
Genetic algorithm |
en_ZA |
dc.subject |
Hybrid systems |
en_ZA |
dc.subject |
Model predictive control |
en_ZA |
dc.subject |
SEIQRDP model |
en_ZA |
dc.subject |
COVID-19 pandemic |
en_ZA |
dc.subject |
Coronavirus disease 2019 (COVID-19) |
en_ZA |
dc.subject |
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) |
en_ZA |
dc.title |
Optimized lockdown strategies for curbing the spread of COVID-19 : a South African case study |
en_ZA |
dc.type |
Article |
en_ZA |