A Machine Learning Evaluation of the Effects of South Africa’s COVID-19 Lockdown Measures on Population Mobility

Albert Whata,Charles Chimedza

doi:10.3390/make3020025

Abstract

Following the declaration by the World Health Organisation (WHO) on 11 March 2020, that the global COVID-19 outbreak had become a pandemic, South Africa implemented a full lockdown from 27 March 2020 for 21 days. The full lockdown was implemented after the publication of the National Disaster Regulations (NDR) gazette on 18 March 2020. The regulations included lockdowns, public health measures, movement restrictions, social distancing measures, and social and economic measures. We developed a hybrid model that consists of a long-short term memory auto-encoder (LSTMAE) and the kernel quantile estimator (KQE) algorithm to detect change-points. Thereafter, we utilised the Bayesian structural times series models (BSTSMs) to estimate the causal effect of the lockdown measures. The LSTMAE and KQE, successfully detected the changepoint that resulted from the full lockdown that was imposed on 27 March 2020. Additionally, we quantified the causal effect of the full lockdown measure on population mobility in residential places, workplaces, transit stations, parks, grocery and pharmacy, and retail and recreation. In relative terms, population mobility at grocery and pharmacy places decreased significantly by −17,137.04% (p-value = 0.001 < 0.05). In relative terms, population mobility at transit stations, retail and recreation, workplaces, parks, and residential places decreased significantly by −998.59% (p-value = 0.001 < 0.05), −1277.36% (p-value = 0.001 < 0.05), −2175.86% (p-value = 0.001 < 0.05), −370.00% (p-value = 0.001< 0.05), and −22.73% (p-value = 0.001 < 0.05), respectively. Therefore, the full lockdown Level 5 imposed on March 27, 2020 had a causal effect on population mobility in these categories of places.

Highlights

On March 11, 2020, the World Health Organisation (WHO) declared that the globalCOVID-19 outbreak had become a pandemic [1]
We developed a hybrid model that consists of a long-short term memory auto-encoder (LSTMAE) and the kernel quantile estimator (KQE) algorithm to detect change-points
We develop a hybrid model that consists of a long-short term memory auto-encoder (LSTMAE) and the kernel quantile estimator (KQE) algorithm to automatically detect change points from a time series or a sequence of values, We compare the change points detected by our proposed model, the long-short term memory auto-encoder (LSTMAE) that is combined with a kernel quantile estimator

Summary

Introduction

On March 11, 2020, the World Health Organisation (WHO) declared that the globalCOVID-19 outbreak had become a pandemic [1]. A national state of disaster was declared by the government of South Africa on of 15 March 2020 [2]. The government ordered all South Africans into a full lockdown. The full lockdown was implemented after the publication of the National Disaster Regulations (NDR) gazette on. The regulations or measures contained in the gazette were applicable for the duration of the full lockdown. These drastic regulations or measures that were imposed on the public included lockdowns, public health measures, movement restrictions, social distancing, and social and economic. The lockdown was viewed as the best response from a public health perspective, the economic impact was devastating for ordinary South African households and businesses [4]

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Results

Discussion

Conclusion