Optimized lockdown strategies for curbing the spread of COVID-19: A South African case study

TL;DR

This paper develops a hybrid model predictive control approach to optimize COVID-19 lockdown strategies in South Africa, balancing healthcare capacity, economic impacts, and population compliance.

Contribution

It introduces a novel control-based framework for optimizing lockdown levels using South African data and a hybrid epidemiological model.

Findings

Optimal lockdown levels vary by scenario and policy goals.

The control approach effectively balances healthcare capacity and economic activity.

Population compliance significantly influences COVID-19 spread dynamics.

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 paper 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.