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1.
Computers & Industrial Engineering ; : 108603, 2022.
Article in English | ScienceDirect | ID: covidwho-2003930

ABSTRACT

With the increasing demand for hospital services amidst the COVID-19 pandemic, allocation of limited public resources and management of healthcare services are of paramount importance. In the field of patient flow scheduling, previous research primarily focused on classical-based objective functions, while ignoring environmental-based objective functions. This study presents a flexible job shop scheduling problem to optimize patient flow and, thereby, minimize the total carbon footprint, as the sustainability-based objective function. Since flexible job shop scheduling is an NP-hard problem, a metaheuristic optimization algorithm, called Chaotic Salp Swarm Algorithm Enhanced with Opposition-Based Learning and Sine Cosine (CSSAOS), was developed. The proposed algorithm integrates the Salp Swarm Algorithm (SSA) with chaotic maps to update the position of followers, the sine cosine algorithm to update the leader position, and opposition-based learning for a better exploration of the search space. generating more accurate solutions. The proposed method was successfully applied in a real-world case study and demonstrated better performance than other well-known metaheuristic algorithms, including differential evolution, genetic algorithm, grasshopper optimization algorithm, SSA based on opposition-based learning, quantum evolutionary SSA, and whale optimization algorithm. In addition, it was found that the proposed method is scalable to different sizes and complexities.

2.
Ann Am Thorac Soc ; 19(8): 1346-1354, 2022 08.
Article in English | MEDLINE | ID: covidwho-1974363

ABSTRACT

Rationale: During the first wave of the coronavirus disease (COVID-19) pandemic in New York City, the number of mechanically ventilated COVID-19 patients rapidly surpassed the capacity of traditional intensive care units (ICUs), resulting in health systems utilizing other areas as expanded ICUs to provide critical care. Objectives: To evaluate the mortality of patients admitted to expanded ICUs compared with those admitted to traditional ICUs. Methods: Multicenter, retrospective, cohort study of mechanically ventilated patients with COVID-19 admitted to the ICUs at 11 Northwell Health hospitals in the greater New York City area between March 1, 2020 and April 30, 2020. Primary outcome was in-hospital mortality up to 28 days after intubation of COVID-19 patients. Results: Among 1,966 mechanically ventilated patients with COVID-19, 1,198 (61%) died within 28 days after intubation, 46 (2%) were transferred to other hospitals outside of the Northwell Health system, 722 (37%) survived in the hospital until 28 days or were discharged after recovery. The risk of mortality of mechanically ventilated patients admitted to expanded ICUs was not different from those admitted to traditional ICUs (hazard ratio [HR], 1.07; 95% confidence interval [CI], 0.95-1.20; P = 0.28), while hospital occupancy for critically ill patients itself was associated with increased risk of mortality (HR, 1.28; 95% CI, 1.12-1.45; P < 0.001). Conclusions: Although increased hospital occupancy for critically ill patients itself was associated with increased mortality, the risk of 28-day in-hospital mortality of mechanically ventilated patients with COVID-19 who were admitted to expanded ICUs was not different from those admitted to traditional ICUs.


Subject(s)
COVID-19 , Critical Illness , COVID-19/therapy , Cohort Studies , Hospital Mortality , Humans , Intensive Care Units , New York City/epidemiology , Respiration, Artificial , Retrospective Studies
3.
Am J Med Qual ; 37(4): 327-334, 2022.
Article in English | MEDLINE | ID: covidwho-1741052

ABSTRACT

Accurate determinations of the time of intubation (TOI) are critical for retrospective electronic health record (EHR) data analyses. In a retrospective study, the authors developed and validated an improved query (Ti) to identify TOI across numerous settings in a large health system, using EHR data, during the COVID-19 pandemic. Further, they evaluated the affect of Ti on peri-intubation patient parameters compared to a previous method-ventilator parameters (Tv). Ti identified an earlier TOI for 84.8% (n = 1666) of cases with a mean (SD) of 3.5 hours (15.5), resulting in alternate values for: partial pressure of arterial oxygen (PaO 2 ) in 18.4% of patients (mean 43.95 mmHg [54.24]); PaO 2 /fractional inspired oxygen (FiO 2 ) in 17.8% of patients (mean 48.29 [69.81]), and oxygen saturation/FiO 2 in 62.7% (mean 16.75 [34.14]), using the absolute difference in mean values within the first 4 hours of intubation. Differences in PaO 2 /FiO 2 using Ti versus Tv resulted in the reclassification of 7.3% of patients into different acute respiratory distress syndrome (ARDS) severity categories.


Subject(s)
COVID-19 , Respiration, Artificial , Data Analysis , Electronic Health Records , Humans , Intubation, Intratracheal , Oxygen , Pandemics , Respiration, Artificial/methods , Retrospective Studies
4.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-319616

ABSTRACT

The novel Coronavirus disease, known as COVID-19, is an outbreak that started in Wuhan, one of the Central Chinese cities. In this report, a short analysis focusing on Australia, Italy, and the United Kingdom has been conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia as compared with Italy and the United Kingdom, and the outbreak in different Australian cities. Mathematical approaches based on the susceptible, infected, and recovered case (SIR) and susceptible, exposed, infected, and recovered (SEIR) models were proposed to predict the epidemiology in the countries. Since the performance of the classic form of SIR and SEIR depends on parameter settings, some optimization algorithms, namely, the Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), L-BFGS-B, and Nelder-Mead are proposed to optimize the parameters of SIR and SEIR models and improve its predictive capabilities. The results of optimized SIR and SEIR models are compared with the Prophet algorithm and logistic function as two known ML algorithms. The results show that different algorithms display different behaviours in different countries. However, the improved version of the SIR and SEIR models have a better performance compared with other mentioned algorithms described in this study. Moreover, the Prophet algorithm works better for Italy and the United Kingdom cases than for Australian cases and Logistic function compared with Prophet algorithm has a better performance in these cases. It seems that Prophet algorithm is suitable for data with increasing trend in pandemic situations. Optimization of the SIR and SEIR models parameters has yielded a significant improvement in the prediction accuracy of the models. Although there are several algorithms for prediction of this Pandemic, there is no certain algorithm that would be the best one for all cases.

5.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-319615

ABSTRACT

The Novel coronavirus (COVID-19) has distributed to more than 200 territory worldwide leading to about 24 million confirmed cases as of August 25, 2020. Several models have been released that forecast the outbreak globally. This work presents a review of the most important forecasting models against COVID-19 and shows a short analysis of each one. The work presented in this study possesses two parts. A detailed scientometric analysis was done in the first section that provides an influential tool for describing bibliometric analyses. The analysis was performed on data corresponding to COVID-19 using the Scopus and Web of Science databases. For analysis, keywords and subject areas were addressed while classification of forecasting models, criteria evaluation and comparison of solution approaches were done in the second section of the work. Conclusion and discussion are provided as the final sections of this study.

6.
BMC Pulm Med ; 22(1): 51, 2022 Feb 04.
Article in English | MEDLINE | ID: covidwho-1666648

ABSTRACT

BACKGROUND: Understanding heterogeneity seen in patients with COVIDARDS and comparing to non-COVIDARDS may inform tailored treatments. METHODS: A multidisciplinary team of frontline clinicians and data scientists worked to create the Northwell COVIDARDS dataset (NorthCARDS) leveraging over 11,542 COVID-19 hospital admissions. The data was then summarized to examine descriptive differences based on clinically meaningful categories of lung compliance, and to examine trends in oxygenation. FINDINGS: Of the 1536 COVIDARDS patients in the NorthCARDS dataset, there were 531 (34.6%) who had very low lung compliance (< 20 ml/cmH2O), 970 (63.2%) with low-normal compliance (20-50 ml/cmH2O), and 35 (2.2%) with high lung compliance (> 50 ml/cmH2O). The very low compliance group had double the median time to intubation compared to the low-normal group (107.3 h (IQR 25.8, 239.2) vs. 39.5 h (IQR 5.4, 91.6)). Overall, 68.8% (n = 1057) of the patients died during hospitalization. In comparison to non-COVIDARDS reports, there were less patients in the high compliance category (2.2% vs. 12%, compliance ≥ 50 mL/cmH20), and more patients with P/F ≤ 150 (59.8% vs. 45.6%). There is a statistically significant correlation between compliance and P/F ratio. The Oxygenation Index is the highest in the very low compliance group (12.51, SD(6.15)), and lowest in high compliance group (8.78, SD(4.93)). CONCLUSIONS: The respiratory system compliance distribution of COVIDARDS is similar to non-COVIDARDS. In some patients, there may be a relation between time to intubation and duration of high levels of supplemental oxygen treatment on trajectory of lung compliance.


Subject(s)
COVID-19/physiopathology , Hypoxia/virology , Lung/physiopathology , Respiratory Distress Syndrome/virology , Adult , Aged , Aged, 80 and over , Biomechanical Phenomena , COVID-19/therapy , Case-Control Studies , Disease Progression , Female , Humans , Hypoxia/physiopathology , Hypoxia/therapy , Male , Middle Aged , Respiration, Artificial , Respiratory Distress Syndrome/physiopathology , Respiratory Distress Syndrome/therapy , Respiratory Function Tests , Retrospective Studies , Treatment Outcome
7.
J Cell Mol Med ; 26(5): 1445-1455, 2022 03.
Article in English | MEDLINE | ID: covidwho-1642687

ABSTRACT

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype.


Subject(s)
COVID-19/genetics , COVID-19/mortality , Neural Networks, Computer , COVID-19/epidemiology , Complement Activation/genetics , Complement Factor H/genetics , Complement System Proteins/genetics , Female , Greece/epidemiology , Hospitalization/statistics & numerical data , Humans , Intensive Care Units/statistics & numerical data , Male , Middle Aged , Models, Genetic , Morbidity , Polymorphism, Single Nucleotide , Thrombomodulin/genetics
8.
Applied Sciences ; 11(23):11423, 2021.
Article in English | MDPI | ID: covidwho-1554906

ABSTRACT

The COVID-19 pandemic has claimed the lives of millions of people and put a significant strain on healthcare facilities. To combat this disease, it is necessary to monitor affected patients in a timely and cost-effective manner. In this work, CXR images were used to identify COVID-19 patients. We compiled a CXR dataset with equal number of 2313 COVID positive, pneumonia and normal CXR images and utilized various transfer learning models as base classifiers, including VGG16, GoogleNet, and Xception. The proposed methodology combines fuzzy ensemble techniques, such as Majority Voting, Sugeno Integral, and Choquet Fuzzy, and adaptively combines the decision scores of the transfer learning models to identify coronavirus infection from CXR images. The proposed fuzzy ensemble methods outperformed each individual transfer learning technique and several state-of-the-art ensemble techniques in terms of accuracy and prediction. Specifically, VGG16 + Choquet Fuzzy, GoogleNet + Choquet Fuzzy, and Xception + Choquet Fuzzy achieved accuracies of 97.04%, 98.48%, and 99.57%, respectively. The results of this work are intended to help medical practitioners achieve an earlier detection of coronavirus compared to other detection strategies, which can further save millions of lives and advantageously influence society.

9.
Comput Biol Med ; 141: 105024, 2022 02.
Article in English | MEDLINE | ID: covidwho-1509702

ABSTRACT

BACKGROUND AND OBJECTIVE: The world is currently facing a global emergency due to COVID-19, which requires immediate strategies to strengthen healthcare facilities and prevent further deaths. To achieve effective remedies and solutions, research on different aspects, including the genomic and proteomic level characterizations of SARS-CoV-2, are critical. In this work, the spatial representation/composition and distribution frequency of 20 amino acids across the primary protein sequences of SARS-CoV-2 were examined according to different parameters. METHOD: To identify the spatial distribution of amino acids over the primary protein sequences of SARS-CoV-2, the Hurst exponent and Shannon entropy were applied as parameters to fetch the autocorrelation and amount of information over the spatial representations. The frequency distribution of each amino acid over the protein sequences was also evaluated. In the case of a one-dimensional sequence, the Hurst exponent (HE) was utilized due to its linear relationship with the fractal dimension (D), i.e. D+HE=2, to characterize fractality. Moreover, binary Shannon entropy was considered to measure the uncertainty in a binary sequence then further applied to calculate amino acid conservation in the primary protein sequences. RESULTS AND CONCLUSION: Fourteen (14) SARS-CoV protein sequences were evaluated and compared with 105 SARS-CoV-2 proteins. The simulation results demonstrate the differences in the collected information about the amino acid spatial distribution in the SARS-CoV-2 and SARS-CoV proteins, enabling researchers to distinguish between the two types of CoV. The spatial arrangement of amino acids also reveals similarities and dissimilarities among the important structural proteins, E, M, N and S, which is pivotal to establish an evolutionary tree with other CoV strains.


Subject(s)
COVID-19 , SARS-CoV-2 , Amino Acid Sequence , Amino Acids , Humans , Proteomics
10.
Processes ; 9(7):1155, 2021.
Article in English | MDPI | ID: covidwho-1295906

ABSTRACT

One of the most crucial aspects of image segmentation is multilevel thresholding. However, multilevel thresholding becomes increasingly more computationally complex as the number of thresholds grows. In order to address this defect, this paper proposes a new multilevel thresholding approach based on the Evolutionary Arithmetic Optimization Algorithm (AOA). The arithmetic operators in science were the inspiration for AOA. DAOA is the proposed approach, which employs the Differential Evolution technique to enhance the AOA local research. The proposed algorithm is applied to the multilevel thresholding problem, using Kapur’s measure between class variance functions. The suggested DAOA is used to evaluate images, using eight standard test images from two different groups: nature and CT COVID-19 images. Peak signal-to-noise ratio (PSNR) and structural similarity index test (SSIM) are standard evaluation measures used to determine the accuracy of segmented images. The proposed DAOA method’s efficiency is evaluated and compared to other multilevel thresholding methods. The findings are presented with a number of different threshold values (i.e., 2, 3, 4, 5, and 6). According to the experimental results, the proposed DAOA process is better and produces higher-quality solutions than other comparative approaches. Moreover, it achieved better-segmented images, PSNR, and SSIM values. In addition, the proposed DAOA is ranked the first method in all test cases.

11.
Data Science for COVID-19 ; : 399-411, 2021.
Article in English | PMC | ID: covidwho-1244674
12.
Information ; 12(3):109, 2021.
Article in English | MDPI | ID: covidwho-1125736

ABSTRACT

The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and the United Kingdom is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and the United Kingdom, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS ( L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases.

13.
Neural Comput Appl ; : 1-11, 2021 Feb 04.
Article in English | MEDLINE | ID: covidwho-1074422

ABSTRACT

The novel coronavirus (COVID-19) has spread to more than 200 countries worldwide, leading to more than 36 million confirmed cases as of October 10, 2020. As such, several machine learning models that can forecast the outbreak globally have been released. This work presents a review and brief analysis of the most important machine learning forecasting models against COVID-19. The work presented in this study possesses two parts. In the first section, a detailed scientometric analysis presents an influential tool for bibliometric analyses, which were performed on COVID-19 data from the Scopus and Web of Science databases. For the above-mentioned analysis, keywords and subject areas are addressed, while the classification of machine learning forecasting models, criteria evaluation, and comparison of solution approaches are discussed in the second section of the work. The conclusion and discussion are provided as the final sections of this study.

14.
Heart Rhythm ; 18(4): 501-507, 2021 04.
Article in English | MEDLINE | ID: covidwho-1046413

ABSTRACT

BACKGROUND: Atrial fibrillation (AF) is the most encountered arrhythmia and has been associated with worse in-hospital outcomes. OBJECTIVE: This study was to determine the incidence of AF in patients hospitalized with coronavirus disease 2019 (COVID-19) as well as its impact on in-hospital mortality. METHODS: Patients hospitalized with a positive COVID-19 polymerase chain reaction test between March 1 and April 27, 2020, were identified from the common medical record system of 13 Northwell Health hospitals. Natural language processing search algorithms were used to identify and classify AF. Patients were classified as having AF or not. AF was further classified as new-onset AF vs history of AF. RESULTS: AF occurred in 1687 of 9564 patients (17.6%). Of those, 1109 patients (65.7%) had new-onset AF. Propensity score matching of 1238 pairs of patients with AF and without AF showed higher in-hospital mortality in the AF group (54.3% vs 37.2%; P < .0001). Within the AF group, propensity score matching of 500 pairs showed higher in-hospital mortality in patients with new-onset AF as compared with those with a history of AF (55.2% vs 46.8%; P = .009). The risk ratio of in-hospital mortality for new-onset AF in patients with sinus rhythm was 1.56 (95% confidence interval 1.42-1.71; P < .0001). The presence of cardiac disease was not associated with a higher risk of in-hospital mortality in patients with AF (P = .1). CONCLUSION: In patients hospitalized with COVID-19, 17.6% experienced AF. AF, particularly new-onset, was an independent predictor of in-hospital mortality.


Subject(s)
Atrial Fibrillation/epidemiology , COVID-19/complications , COVID-19/mortality , Aged , Aged, 80 and over , Atrial Fibrillation/diagnosis , Atrial Fibrillation/virology , COVID-19/diagnosis , Female , Hospital Mortality , Hospitalization , Humans , Incidence , Male , Middle Aged , Propensity Score , Retrospective Studies
15.
Ieee Computational Intelligence Magazine ; 15(4):34-50, 2020.
Article in English | Web of Science | ID: covidwho-900843

ABSTRACT

The number of confirmed cases of COVID-19 has been ever increasing worldwide since its outbreak in Wuhan, China. As such, many researchers have sought to predict the dynamics of the virus spread in different parts of the globe. In this paper, a novel systematic platform for prediction of the future number of confirmed cases of COVID-19 is proposed, based on several factors such as transmission rate, temperature, and humidity. The proposed strategy derives systematically a set of appropriate features for training Recurrent Neural Networks (RNN). To that end, the number of confirmed cases (CC) of COVID-19 in three states of India (Maharashtra, Tamil Nadu and Gujarat) is taken as a case study. It has been noted that stationary and nonstationary parts of the features improved the prediction of the stationary and non-stationary trends of the number of confirmed cases, respectively. The new platform has general application and can be used for pandemic time series forecasting.

16.
Chaos Solitons Fractals ; 140: 110118, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-652086

ABSTRACT

COVID-19 or SARS-Cov-2, affecting 6 million people and more than 300,000 deaths, the global pandemic has engulfed more than 90% countries of the world. The virus started from a single organism and is escalating at a rate of 3% to 5% daily and seems to be a never ending process. Understanding the basic dynamics and presenting new predictions models for evaluating the potential effect of the virus is highly crucial. In present work, an evolutionary data analytics method called as Genetic programming (GP) is used to mathematically model the potential effect of coronavirus in 15 most affected countries of the world. Two datasets namely confirmed cases (CC) and death cases (DC) were taken into consideration to estimate, how transmission varied in these countries between January 2020 and May 2020. Further, a percentage rise in the number of daily cases is also shown till 8 June 2020 and it is expected that Brazil will have the maximum rise in CC and USA have the most DC. Also, prediction of number of new CC and DC cases for every one million people in each of these countries is presented. The proposed model predicted that the transmission of COVID-19 in China is declining since late March 2020; in Singapore, France, Italy, Germany and Spain the curve has stagnated; in case of Canada, South Africa, Iran and Turkey the number of cases are rising slowly; whereas for USA, UK, Brazil, Russia and Mexico the rate of increase is very high and control measures need to be taken to stop the chains of transmission. Apart from that, the proposed prediction models are simple mathematical equations and future predictions can be drawn from these general equations. From the experimental results and statistical validation, it can be said that the proposed models use simple linkage functions and provide highly reliable results for time series prediction of COVID-19 in these countries.

17.
Chaos Solitons Fractals ; 139: 110056, 2020 Oct.
Article in English | MEDLINE | ID: covidwho-614270

ABSTRACT

The recent worldwide outbreak of the novel coronavirus disease 2019 (COVID-19) opened new challenges for the research community. Machine learning (ML)-guided methods can be useful for feature prediction, involved risk, and the causes of an analogous epidemic. Such predictions can be useful for managing and intercepting the outbreak of such diseases. The foremost advantages of applying ML methods are handling a wide variety of data and easy identification of trends and patterns of an undetermined nature.In this study, we propose a partial derivative regression and nonlinear machine learning (PDR-NML) method for global pandemic prediction of COVID-19. We used a Progressive Partial Derivative Linear Regression model to search for the best parameters in the dataset in a computationally efficient manner. Next, a Nonlinear Global Pandemic Machine Learning model was applied to the normalized features for making accurate predictions. The results show that the proposed ML method outperformed state-of-the-art methods in the Indian population and can also be a convenient tool for making predictions for other countries.

18.
Chaos Solitons Fractals ; 138: 109945, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-436317

ABSTRACT

COVID-19 declared as a global pandemic by WHO, has emerged as the most aggressive disease, impacting more than 90% countries of the world. The virus started from a single human being in China, is now increasing globally at a rate of 3% to 5% daily and has become a never ending process. Some studies even predict that the virus will stay with us forever. India being the second most populous country of the world, is also not saved, and the virus is spreading as a community level transmitter. Therefore, it become really important to analyse the possible impact of COVID-19 in India and forecast how it will behave in the days to come. In present work, prediction models based on genetic programming (GP) have been developed for confirmed cases (CC) and death cases (DC) across three most affected states namely Maharashtra, Gujarat and Delhi as well as whole India. The proposed prediction models are presented using explicit formula, and impotence of prediction variables are studied. Here, statistical parameters and metrics have been used for evaluated and validate the evolved models. From the results, it has been found that the proposed GEP-based models use simple linkage functions and are highly reliable for time series prediction of COVID-19 cases in India.

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