Multi-objective Evolutionary Discretization of Gene Expression Profiles: Application to COVID-19 Severity Prediction

Machine learning models can use information from gene expressions in patients to efficiently predict the severity of symptoms for several diseases. Medical experts, however, still need to understand the reasoning behind the predictions before trusting them. In their day-to-day practice, physicians prefer using gene expression profiles, consisting of a discretized subset of all data from gene expressions: in these profiles, genes are typically reported as either over-expressed or under-expressed, using discretization thresholds computed on data from a healthy control group. A discretized profile allows medical experts to quickly categorize patients at a glance. Building on previous works related to the automatic discretization of patient profiles, we present a novel approach that frames the problem as a multi-objective optimization task: on the one hand, after discretization, the medical expert would prefer to have as few different profiles as possible, to be able to classify patients in an intuitive way;on the other hand, the loss of information has to be minimized. Loss of information can be estimated using the performance of a classifier trained on the discretized gene expression levels. We apply one common state-of-the-art evolutionary multi-objective algorithm, NSGA-II, to the discretization of a dataset of COVID-19 patients that developed either mild or severe symptoms. The results show not only that the solutions found by the approach dominate traditional discretization based on statistical analysis and are more generally valid than those obtained through single-objective optimization, but that the candidate Pareto-optimal solutions preserve the sense-making that practitioners find necessary to trust the results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Proposed Framework for Student's Skills-Driven Personalization of Cloud-Based Course Content

Engaging students' personalized data in the aspects of education has been on focus by different researchers. This paper considers it vital for exploring the student's progress, moreover, it could predict the student's level which consequently leads to identifying the required student material to raise his current education level. Although the topic has been vital before the COVID-19 pandemic, however, the importance of the topic has increased exponentially ever since. The research supports the decision-makers in educational institutions as considering personalized data for the student's educational tasks and activities proved the positive impact of raising the student level. The paper proposes a framework that considers the students' personal data in predicting their learning skills as well as their educational level. The research included engaging five well-known clustering algorithms, one of the most successful classification algorithms, and a set of 10 features selection techniques. The research applied two main experiment phases, the first phase focused on predicting the students' learning skills, and the second focused on predicting the students' level. Two datasets are involved in the experiments and their sources are mentioned. The research revealed the success of the clustering and prediction tasks by applying the selected techniques to the datasets. The research concluded that the highest clustering algorithm accuracy is enhanced k-means (EKM) and the highest contributing features selection method is the evolutionary computation method. © 2023 World Scientific Publishing Company.

A genetic programming-based convolutional deep learning algorithm for identifying COVID-19 cases via X-ray images.

Evolutionary algorithms have been successfully employed to find the best structure for many learning algorithms including neural networks. Due to their flexibility and promising results, Convolutional Neural Networks (CNNs) have found their application in many image processing applications. The structure of CNNs greatly affects the performance of these algorithms both in terms of accuracy and computational cost, thus, finding the best architecture for these networks is a crucial task before they are employed. In this paper, we develop a genetic programming approach for the optimization of CNN structure in diagnosing COVID-19 cases via X-ray images. A graph representation for CNN architecture is proposed and evolutionary operators including crossover and mutation are specifically designed for the proposed representation. The proposed architecture of CNNs is defined by two sets of parameters, one is the skeleton which determines the arrangement of the convolutional and pooling operators and their connections and one is the numerical parameters of the operators which determine the properties of these operators like filter size and kernel size. The proposed algorithm in this paper optimizes the skeleton and the numerical parameters of the CNN architectures in a co-evolutionary scheme. The proposed algorithm is used to identify covid-19 cases via X-ray images.

The Superiority of Coronavirus Optimization Algorithm for Optimal Designing of Photovoltaic/Wind/Fuel Cell Hybrid System Considering Cost Minimization Approach to Improve Reliability

This article proposes the best design for a hybrid system that incorporates wind turbines, solar panels, and fuel cells (FC) to satisfy the load requirement. The design's goal is to reduce the system's energy production costs considering the load supply's reliability. System costs include initial investment costs, operation and maintenance, replacement and replacement costs, and load loss costs. The optimal capacity of the hybrid system's equipment has been calculated with the help of the Coronavirus Optimization Algorithm (COVIDOA). The results obtained from the optimization have been compared and analyzed with those obtained from the Differential Evolution (DE) algorithm. The results have shown that the COVIDOA optimization method, like the DE optimization method, has obtained favourable results. In the COVIDOA method, the system's production costs have increased slightly, but the reliability of the load supply has been improved. Therefore, in the suggested approach, in addition to considering the economic aspect of the design, much attention has been paid to the technical aspect of the design, in other words, the reliability level of the system. © 2023 IEEE.

Agent-based mathematical model of COVID-19 spread in Novosibirsk region: Identifiability, optimization and forecasting

The problem of identification of unknown epidemiological parameters (contagiosity, the initial number of infected individuals, probability of being tested) of an agent-based model of COVID-19 spread in Novosibirsk region is solved and analyzed. The first stage of modeling involves data analysis based on the machine learning approach that allows one to determine correlated datasets of performed PCR tests and number of daily diagnoses and detect some features (seasonality, stationarity, data correlation) to be used for COVID-19 spread modeling. At the second stage, the unknown model parameters that depend on the date of introducing of containment measures are calibrated with the usage of additional measurements such as the number of daily diagnosed and tested people using PCR, their daily mortality rate and other statistical information about the disease. The calibration is based on minimization of the misfit function for daily diagnosed data. The OPTUNA optimization framework with tree-structured Parzen estimator and covariance matrix adaptation evolution strategy is used to minimize the misfit function. Due to ill-posedness of identification problem, the identifiability analysis is carried out to construct the regularization algorithm. At the third stage, the identified parameters of COVID-19 for Novosibirsk region and different scenarios of COVID-19 spread are analyzed in relation to introduced quarantine measures. This kind of modeling can be used to select effective anti-pandemic programs. © 2023 Walter de Gruyter GmbH, Berlin/Boston 2023.

Cluster analysis and forecasting of viruses incidence growth curves: Application to SARS-CoV-2.

The sanitary emergency caused by COVID-19 has compromised countries and generated a worldwide health and economic crisis. To provide support to the countries' responses, numerous lines of research have been developed. The spotlight was put on effectively and rapidly diagnosing and predicting the evolution of the pandemic, one of the most challenging problems of the past months. This work contributes to the existing literature by developing a two-step methodology to analyze the transmission rate, designing models applied to territories with similar pandemic behavior characteristics. Virus transmission is considered as bacterial growth curves to understand the spread of the virus and to make predictions about its future evolution. Hence, an analytical clustering procedure is first applied to create groups of locations where the virus transmission rate behaved similarly in the different outbreaks. A curve decomposition process based on an iterative polynomial process is then applied, obtaining meaningful forecasting features. Information of the territories belonging to the same cluster is merged to build models capable of simultaneously predicting the 14-day incidence in several locations using Evolutionary Artificial Neural Networks. The methodology is applied to Andalusia (Spain), although it is applicable to any region across the world. Individual models trained for a specific territory are carried out for comparison purposes. The results demonstrate that this methodology achieves statistically similar, or even better, performance for most of the locations. In addition to being extremely competitive, the main advantage of the proposal lies in its complexity cost reduction. The total number of parameters to be estimated is reduced up to 93.51% for the short term and 93.31% for the mid-term forecasting, respectively. Moreover, the number of required models is reduced by 73.53% and 58.82% for the short- and mid-term forecasting horizons.

Applied Enhanced Q-NAS for COVID-19 Detection in CT Images

The Deep Neural Networks are flexible and robust models that have gained attention from the machine learning community over the last decade. During the construction of a neural network, an expert can spend significant time designing a neural architecture with trial and error sessions. Because of the manual process, there is a greater interest in Neural Architecture Search (NAS), which is an automated method of architectural search in neural networks. Quantum-inspired evolutionary algorithms present propitious results regarding faster convergence when compared to other solutions with restricted search space and high computational costs. In this work, we enhance the Q-NAS model: a quantum-inspired algorithm to search for deep networks by assembling substructures. We present a new architecture that was designed automatically by the Q-NAS and applied to a case study for COVID-19 vs. healthy classification. For this classification, the Q-NAS algorithm was able to find a network architecture with only 1.23 M parameters that reached the accuracy of 99.44%, which overcame benchmark networks like Inception (GoogleLeNet), EfficientNet and VGG that were also tested in this work. The algorithm is publicly avaiable at https://github.com/julianoce/qnas. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Integrating Two-Level Reinforcement Learning Process for Enhancing Task Scheduling Efficiency in a Complex Problem-Solving Environment

Task scheduling scenarios require the system designers to have complete information about the resources and their capabilities, along with the tasks and their application-specific requirements. An effective task-to-resource mapping strategy will maximize resource utilization under constraints, while minimizing the task waiting time, which will in-turn maximize the task execution efficiency. In this work, a two-level reinforcement learning algorithm for task scheduling is proposed. The algorithm utilizes a deep-intensive learning stage to generate a deployable strategy for task-to-resource mapping. This mapping is re-evaluated at specific execution breakpoints, and the strategy is re-evaluated based on the incremental learning from these breakpoints. In order to perform incremental learning, real-time parametric checking is done on the resources and the tasks;and a new strategy is devised during execution. The mean task waiting time is reduced by 20% when compared with standard algorithms like Dynamic and Integrated Resource Scheduling, Improved Differential Evolution, and Q-learning-based Improved Differential Evolution;while the resource utilization is improved by more than 15%. The algorithm is evaluated on datasets from different domains like Coronavirus disease (COVID-19) datasets of public domain, National Aeronautics and Space Administration (NASA) datasets and others. The proposed method performs consistently on all the datasets. © 2023 IETE.

SA-FGDEM: A Self-adaptive E-Learning Performance Prediction Model

With the rapid development of computer computing power and the severe challenges brought by the COVID-19, e-learning, as the optimal solution for most students and other learner groups, plays an extremely important role in maintaining the normal operation of educational institutions. As the user community continues to expand, it has become increasingly important to guarantee the quality of teaching and learning. One way to ensure the quality of online education is to construct e-learning behavior data to build learning performance predictors. Still, most studies have ignored the intrinsic correlation between e-learning behaviors. Therefore, this study proposes an adaptive feature fusion-based e-learning performance prediction model (SA-FGDEM) relying on the theoretical model of learning behav-ior classification. The experimental results show that the feature space mined by fine-grained differential evolution algorithm and the adaptive feature fusion combined with differential evolution algorithm can support e-learning performance prediction more effectively and is better than the benchmark method. © 2022 IEEE.

Prediction of COVID-19 Confirmed Cases Combining The LSTM Model and Evolutionary Strategy

In the global response to the COVID-19 epidemic, a reasonable prediction of the number of infections is a significant reference to reveal the trend of the outbreak and help governments take appropriate action. In this paper, we propose a new ES-LSTM model to predict the growth rate of the number of new infections per day and use a feature processor to address interventions in time series to quantify the impact of interventions to slow the spread of the outbreak. The evolutionary strategy is used to handle the problem that different interventions have different impacts on outbreak prevention and control, as well as optimize model weight to improve the accuracy of prediction results. Experimental results demonstrate that compared to the Linear model, CNN model, and the LSTM model, the MAE of the algorithm is enhanced by 72.9%, 27.6%, and 26.3%, and the RMSE is improved by 74.15%, 31.4%, and 29.5% respectively. © 2022 IEEE.

Curbing Pandemic Through Evolutionary Algorithm-Based Priority Aware Mobility Scheduling

COVID-19 is a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2. While swift vaccine development and distribution have arrested the infection spread rate, it is necessary to design public policies that inform human mobility to curb outbreaks from future strains of the virus. While existing non-pharmaceutical approaches employing network science and machine learning offer promising travel policy solutions, they are guided by epidemiological and economic considerations alone and not human itineraries. We introduce an evolutionary algorithm (EA) based mobility scheduler that incorporates the personalized itineraries of individuals to determine the ideal timing of their mobility. We mathematically analyze the computational efficiency versus the optimality trade-off of the mobility scheduler. Through extensive simulations, we demonstrate that the EA-based mobility scheduler can balance the trade-off between (1) optimality and computational cost and (2) fair and preferential human mobility while reducing contagion under lockdown and no-lockdown as well as even and uneven human mobility traffic scenarios. We show that for two human mobility models, the scheduler exhibits lower infection numbers than a baseline trip-planning approach that directs human traffic along the least congested route to minimize contagion. We discuss that the EA scheduler lends itself to intricate mobility schedules of multiple destination choices with varying priorities and socioeconomic and demographic considerations.

Feedstock planning and optimization of a sustainable distributed configuration biorefinery for biojet fuel production via ATJ process

In recent years, the production and consumption of fossil jet fuel have increased as a consequence of a rise in the number of passengers and goods transported by air. Despite the low demand caused by the coronavirus 2019 pandemic, an increase in the services offered by the sector is expected again. In an economic context still dependent on scarce oil, this represents a problem. There is also a problem arising from the fuel's environmental impact throughout its life cycle. Given this, a promising solution is the use of biojet fuel as renewable aviation fuel. In a circular economy framework, the use of lignocellulosic biomass in the form of sugar-rich crop residues allows the production of alcohols necessary to obtain biojet fuel. The tools provided by process intensification also make it possible to design a sustainable process with low environmental impact and capable of achieving energy savings. The goal of this work was to design an intensified process to produce biojet fuel from Mexican lignocellulosic biomass, with alcohols as intermediates. The process was modeled following a sequence of pretreatment/hydrolysis/fermentation/purification for the biomass-ethanol process, and dehydration/oligomerization/hydrogenation/distillation for ethanol-biojet process under the concept of distributed configuration. To obtain a cleaner, greener, and cheaper process, the purification zone of ethanol was intensified by employing a vapor side stream distillation column and a dividing wall column. Once designed, the entire process was optimized by employing the stochastic method of differential evolution with a tabu list to minimize the total annual cost and with the Eco-indicator-99 to evaluate the sustainability of the process. The results show that savings of 5.56% and a reduction of 1.72% in Eco-indicator-99 were achieved with a vapor side stream column in comparison with conventional distillation. On the other hand, with a dividing wall column, savings of 5.02% and reductions of 2.92% in Eco-indicator-99 were achieved. This process is capable of meeting a demand greater than 266 million liters of biojet fuel per year. However, the calculated sale price indicates that this biojet fuel still does not compete with conventional jet fuel produced in Mexico. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd.

Quantum Inspired Differential Evolution with Explainable Artificial Intelligence-Based COVID-19 Detection

Recent advancements in the Internet of Things (Io), 5G networks, and cloud computing (CC) have led to the development of Human-centric IoT (HIoT) applications that transform human physical monitoring based on machine monitoring. The HIoT systems find use in several applications such as smart cities, healthcare, transportation, etc. Besides, the HIoT system and explainable artificial intelligence (XAI) tools can be deployed in the healthcare sector for effective decision-making. The COVID-19 pandemic has become a global health issue that necessitates automated and effective diagnostic tools to detect the disease at the initial stage. This article presents a new quantum-inspired differential evolution with explainable artificial intelligence based COVID-19 Detection and Classification (QIDEXAI-CDC) model for HIoT systems. The QIDEXAI-CDC model aims to identify the occurrence of COVID-19 using the XAI tools on HIoT systems. The QIDEXAI-CDC model primarily uses bilateral filtering (BF) as a preprocessing tool to eradicate the noise. In addition, RetinaNet is applied for the generation of useful feature vectors from radiological images. For COVID-19 detection and classification, quantum-inspired differential evolution (QIDE) with kernel extreme learning machine (KELM) model is utilized. The utilization of the QIDE algorithm helps to appropriately choose the weight and bias values of the KELM model. In order to report the enhanced COVID-19 detection outcomes of the QIDEXAI-CDC model, a wide range of simulations was carried out. Extensive comparative studies reported the supremacy of the QIDEXAI-CDC model over the recent approaches. © 2023 Authors. All rights reserved.

COVID-19 diagnosis with hyperparameter optimization using mixture density network(MDN) and EMADE

COVID-19 diagnosis has become a crucial task in today's world due to the rapid spread of the infectious Corona Virus disease caused by the SARS-CoV-2 virus. Analysis of COVID using CT scan images is shown to give better results but it requires expert radiologists and it consumes time. Hence there is a need for a diagnosis system to classify whether it's COVID positive or not for quick and early diagnosis. Deep Learning models are effective in handling classification problems but some models might lead to vanishing gradient problem. A Mixture Density Network (i.e.) Bidirectional Long Short-Term Memory((Bi-LSTM) with Mixture Network is used as the classifier to handle the vanishing gradient problem and to classify based on the probability distribution. Parameter tuning plays a major role in improving the overall efficiency of the classifier. An Enhanced Memetic Adaptive Differential Evolution (EMADE) algorithm is proposed for tuning the parameters of the classifier. Enhanced MADE is a memetic algorithm with proposed Elite chaotic local search (ECLS) which helps in addressing the issue of getting stuck at a local optimal solution and premature convergence. The use of Elitism in the chaotic local search directs the algorithm toward the optimal solution and increases the exploitation ability. Due to high false negatives in RT-PCR, CT scan images have been taken as the input. The dataset is labeled and it consists of 1252 CT scans that are positive for COVID-19, and 1230 CT scans that are negative for COVID-19. The dataset collected from patients in Sao Paulo, Brazil that is available on Kaggle is used [21]. A sample of the dataset is taken for experimentation and an accuracy of 75.83% is achieved. The precision is 80.32% indicating that there are fewer False positive than the existing methods. © 2022 IEEE.

An evolutionary ensemble learning for diagnosing COVID-19 via cough signals.

Objective The spread of the COVID-19 disease has caused great concern around the world and detecting the positive cases is crucial in curbing the pandemic. One of the symptoms of the disease is the dry cough it causes. It has previously been shown that cough signals can be used to identify a variety of diseases including tuberculosis, asthma, etc. In this paper, we proposed an algorithm to diagnose via cough signals the COVID-19 disease. Methods The proposed algorithm is an ensemble scheme that consists of a number of base learners, where each base learner uses a different feature extractor method, including statistical approaches and convolutional neural networks (CNN) for automatic feature extraction. Features are extracted from the raw signal and some transforms performed it, including Fourier, wavelet, Hilbert-Huang, and short-term Fourier transforms. The outputs of these base-learners are aggregated via a weighted voting scheme, with the weights optimised via an evolutionary paradigm. This paper also proposes a memetic algorithm for training the CNNs in the base-learners, which combines the speed of gradient descent (GD) algorithms and global search space coverage of the evolutionary algorithms. Results Experiments were performed on the proposed algorithm and different rival algorithms which included a number of CNN architectures in the literature and generic machine learning algorithms. The results suggested that the proposed algorithm achieves better performance compared to the existing algorithms in diagnosing COVID-19 via cough signals. Conclusion This research showed that COVID-19 could be diagnosed via cough signals and CNNs could be employed to process these signals and it may be further improved by the optimization of CNN architecture.

AutoDock Koto: A Gradient Boosting Differential Evolution for Molecular Docking

Molecular docking plays a vital role in modern drug discovery, by supporting predictions of the binding modes and affinities of ligands at the binding site of target proteins. Several docking programs have been developed for both commercial and academic applications. Typically, a docking program’s performance depends on the sampling algorithm used to generate the ligand’s potential conformations and the scoring function applied to evaluate and rank these conformations. Evolutionary algorithms are widely used as sampling algorithms in docking programs. However, both the linkage problem and the dimensionality degenerate the search ability of evolutionary algorithms in the docking process. Therefore, a newly designed docking program named AutoDock Koto was developed in this study, which adopts a novel gradient boosting differential evolution algorithm to effectively address these issues. Experimental results show that compared with commonly used docking programs, AutoDock Koto yields dramatic improvements in docking performance based on an extensive dataset of 285 protein-ligand complexes. In addition, due to its strong docking ability, AutoDock Koto was used to identify potential drugs for COVID-19 based on a virtual screening of all approved drugs in our experiments. Sixteen drugs are found to possess low binding energy to the main target protease of SARS-CoV-2, and thus have the potential to treat COVID-19 as antiviral drugs. The source code of AutoDock Koto can be downloaded for free from. https://github.com/codezhouj/Molecular_Docking. IEEE

Comparing Parallel Surrogate-Based and Surrogate-Free Multi-objective Optimization of COVID-19 Vaccines Allocation

The simulation-based and computationally expensive problem tackled in this paper addresses COVID-19 vaccines allocation in Malaysia. The multi-objective formulation considers simultaneously the total number of deaths, peak hospital occupancy and relaxation of mobility restrictions. Evolutionary algorithms have proven their capability to handle multi-to-many objectives but require a high number of computationally expensive simulations. The available techniques to raise the challenge rely on the joint use of surrogate-assisted optimization and parallel computing to deal with computational expensiveness. On the one hand, the simulation software is imitated by a cheap-to-evaluate surrogate model. On the other hand, multiple candidates are simultaneously assessed via multiple processing cores. In this study, we compare the performance of recently proposed surrogate-free and surrogate-based parallel multi-objective algorithms through the application to the COVID-19 vaccine distribution problem. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Feedstock planning and optimization of a sustainable distributed configuration biorefinery for biojet fuel production via ATJ process

In recent years, the production and consumption of fossil jet fuel have increased as a consequence of a rise in the number of passengers and goods transported by air. Despite the low demand caused by the coronavirus 2019 pandemic, an increase in the services offered by the sector is expected again. In an economic context still dependent on scarce oil, this represents a problem. There is also a problem arising from the fuel's environmental impact throughout its life cycle. Given this, a promising solution is the use of biojet fuel as renewable aviation fuel. In a circular economy framework, the use of lignocellulosic biomass in the form of sugar-rich crop residues allows the production of alcohols necessary to obtain biojet fuel. The tools provided by process intensification also make it possible to design a sustainable process with low environmental impact and capable of achieving energy savings. The goal of this work was to design an intensified process to produce biojet fuel from Mexican lignocellulosic biomass, with alcohols as intermediates. The process was modeled following a sequence of pretreatment/hydrolysis/fermentation/purification for the biomass-ethanol process, and dehydration/oligomerization/hydrogenation/distillation for ethanol-biojet process under the concept of distributed configuration. To obtain a cleaner, greener, and cheaper process, the purification zone of ethanol was intensified by employing a vapor side stream distillation column and a dividing wall column. Once designed, the entire process was optimized by employing the stochastic method of differential evolution with a tabu list to minimize the total annual cost and with the Eco-indicator-99 to evaluate the sustainability of the process. The results show that savings of 5.56% and a reduction of 1.72% in Eco-indicator-99 were achieved with a vapor side stream column in comparison with conventional distillation. On the other hand, with a dividing wall column, savings of 5.02% and reductions of 2.92% in Eco-indicator-99 were achieved. This process is capable of meeting a demand greater than 266 million liters of biojet fuel per year. However, the calculated sale price indicates that this biojet fuel still does not compete with conventional jet fuel produced in Mexico. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd.

An evolutionary optimization method for selecting features for speech emotion recognition

Human-computer interactions benefit greatly from emotion recognition from speech. To promote a contact-free environment in this coronavirus disease 2019 (COVID'19) pandemic situation, most digitally based systems used speech-based devices. Consequently, this emotion detection from speech has many beneficial applications for pathology. The vast majority of speech emotion recognition (SER) systems are designed based on machine learning or deep learning models. Therefore, need greater computing power and requirements. This issue was addressed by developing traditional algorithms for feature selection. Recent research has shown that nature-inspired or evolutionary algorithms such as equilibrium optimization (EO) and cuckoo search (CS) based meta-heuristic approaches are superior to the traditional feature selection (FS) models in terms of recognition performance. The purpose of this study is to investigate the impact of feature selection meta-heuristic approaches on emotion recognition from speech. To achieve this, we selected the rayerson audio-visual database of emotional speech and song (RAVDESS) database and obtained maximum recognition accuracy of 89.64% using the EO algorithm and 92.71% using the CS algorithm. For this final step, we plotted the associated precision and F1 score for each of the emotional classes. [ FROM AUTHOR]

Differential Evolution to Optimize A Long-term Multi-strain Model of COVID-19 in South Africa

A limited number of studies have been conducted to investigate the dynamics of COVID-19 disease spread in South Africa and these existing studies have mostly focussed on mathematical analysis of a relatively short time period near the initial outbreak of COVID-19 in South Africa. The current study therefore attempted to extend on previous studies by applying a Susceptible- Exposed - Infected - Removed (SEIR) disease model to analyse the long-term dynamics of COVID-19 in South Africa, taking into account multiple waves of infection potentially caused by different virus strains. A Differential Evolution (DE) algorithm was used to fit the proposed model to real-world data, and this was done on both a geographically local and global scale to investigate the differences between these two approaches. Results revealed that a local approach provided a more accurate model fit to data than a global approach and that the method proposed in this work could give valuable insights into disease dynamics. © 2022 IEEE.