Assessing the impact of human behavior towards preventative measures on COVID-19 dynamics for Gauteng, South Africa: a simulation and forecasting approach

CW Chukwu; S. Y. Tchoumi; Z. Chazuka; M. L. Juga; G. Obaido; CW Chukwu; S. Y. Tchoumi; Z. Chazuka; M. L. Juga; G. Obaido

doi:10.3934/math.2024514

AIMS Mathematics

2024, Volume 9, Issue 5: 10511-10535. doi: 10.3934/math.2024514

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Assessing the impact of human behavior towards preventative measures on COVID-19 dynamics for Gauteng, South Africa: a simulation and forecasting approach

1.
Department of Mathematics, Wake Forest University, Winston-Salem, NC 27109, USA
2.
Department of Mathematics and Computer Sciences ENSAI, University of NGaoundere, Cameroon
3.
Department of Mathematics and Applied Mathematics, University of Pretoria, South Africa
4.
Department of Decision Sciences, College of Economics and Management Sciences, University of South Africa
5.
Department of Mathematics and Applied Mathematics, University of Johannesburg, South Africa
6.
Center for Human Artificial Intelligence (CHAI), Berkeley Institute for Data Science (BIDS), University of California, Berkeley, CA 94720, USA

Received: 07 January 2024 Revised: 25 February 2024 Accepted: 05 March 2024 Published: 18 March 2024
MSC : 34A08, 92B05

Globally, the COVID-19 pandemic has claimed millions of lives. In this study, we develop a mathematical model to investigate the impact of human behavior on the dynamics of COVID-19 infection in South Africa. Specifically, our model examined the effects of positive versus negative human behavior. We parameterize the model using data from the COVID-19 fifth wave of Gauteng province, South Africa, from May 01, 2022, to July 23, 2022. To forecast new cases of COVID-19 infections, we compared three forecasting methods: exponential smoothing (ETS), long short-term memory (LSTM), and gated recurrent units (GRUs), using the dataset. Results from the time series analysis showed that the LSTM model has better performance and is well-suited for predicting the dynamics of COVID-19 compared to the other models. Sensitivity analysis and numerical simulations were also performed, revealing that noncompliant infected individuals contribute more to new infections than those who comply. It is envisaged that the insights from this work can better inform public health policy and enable better projections of disease spread.
- human behavior,
- COVID-19,
- compliance,
- noncompliance,
- simulation,
- time series
Citation: CW Chukwu, S. Y. Tchoumi, Z. Chazuka, M. L. Juga, G. Obaido. Assessing the impact of human behavior towards preventative measures on COVID-19 dynamics for Gauteng, South Africa: a simulation and forecasting approach[J]. AIMS Mathematics, 2024, 9(5): 10511-10535. doi: 10.3934/math.2024514

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Abstract

Globally, the COVID-19 pandemic has claimed millions of lives. In this study, we develop a mathematical model to investigate the impact of human behavior on the dynamics of COVID-19 infection in South Africa. Specifically, our model examined the effects of positive versus negative human behavior. We parameterize the model using data from the COVID-19 fifth wave of Gauteng province, South Africa, from May 01, 2022, to July 23, 2022. To forecast new cases of COVID-19 infections, we compared three forecasting methods: exponential smoothing (ETS), long short-term memory (LSTM), and gated recurrent units (GRUs), using the dataset. Results from the time series analysis showed that the LSTM model has better performance and is well-suited for predicting the dynamics of COVID-19 compared to the other models. Sensitivity analysis and numerical simulations were also performed, revealing that noncompliant infected individuals contribute more to new infections than those who comply. It is envisaged that the insights from this work can better inform public health policy and enable better projections of disease spread.

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