Prediction on transmission trajectory of COVID-19 based on particle swarm algorithm

Ding, Caichang; Chen, Yiqin; Liu, Zhiyuan; Liu, Tianyin

doi:10.1016/j.patrec.2021.09.003

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…Castillo and Melin ( 2021 ) outlined the hybrid intelligent approach based on fuzzy logic and fractal dimension to categorize the COVID-19 time-series data based on each country. Ding et al ( 2021 ) predicted the transmission trajectory of the COVID-19 using the combined method of particle swarm optimization algorithm (PSO) and susceptible exposed infected recovered (SEIR). Ceylan ( 2021 ) proposed a hybrid model based on GM(1,1) and a PSO and utilized it to forecast the cumulative case number of COVID-19 in Germany, Turkey, and USA.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Real-time forecasting of the COVID 19 using fuzzy grey Markov: a different approach in decision-making

Nagarajan¹,

Sujatha

Kuppuswami³

et al. 2022

Comp. Appl. Math.

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The ongoing epidemic SARS-CoV-2 named Corona Virus Disease (COVID-19) is highly infectious and subsequently spread all over the world affecting millions of people. Humans have never seen such a deadly disease so far, and as there is no specific drug or vaccination, the mortality rate of the disease has been increasing exponentially. This current situation exacerbated people’s restlessness and fear. Because of this pandemic, the world is travelling on a different path. This world has recovered from many disasters, but this is entirely a different situation. Today’s world is struggling in many ways to get rid of this disease. On the other hand, the number of people recovering from this disease gives us comfort. Yet, we have to take urgent precautionary measures to control this disease in all possible ways. Therefore, forecasting is one of the ways to take the necessary precautionary measures. In this paper, using fuzzy–grey–Markov model, we predict the number of affected and recovered patient count, death count using real-time data in different approaches and compared with the real data. The study concludes with important recommendations for the Indian government to manage the COVID 19 critical situation in advance.

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Section: Related Work and Motivationmentioning

confidence: 99%

Real-time forecasting of the COVID 19 using fuzzy grey Markov: a different approach in decision-making

Nagarajan¹,

Sujatha

Kuppuswami³

et al. 2022

Comp. Appl. Math.

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“…There are many applications of nature-inspired metaheuristic algorithm across disciplines. For example, PSO, being an exemplary nature-inspired algorithm, is widely used to tackle problems due to COVID-19 11 – 14 . There are many monographs on nature-inspired metaheuristic algorithms at various levels, see, for example, 15 – 18 .…”

Section: Introductionmentioning

confidence: 99%

Applications of nature-inspired metaheuristic algorithms for tackling optimization problems across disciplines

Cui,

Zhang,

Chen

et al. 2024

Sci Rep

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Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems. This paper demonstrates the usefulness of such algorithms for solving a variety of challenging optimization problems in statistics using a nature-inspired metaheuristic algorithm called competitive swarm optimizer with mutated agents (CSO-MA). This algorithm was proposed by one of the authors and its superior performance relative to many of its competitors had been demonstrated in earlier work and again in this paper. The main goal of this paper is to show a typical nature-inspired metaheuristic algorithmi, like CSO-MA, is efficient for tackling many different types of optimization problems in statistics. Our applications are new and include finding maximum likelihood estimates of parameters in a single cell generalized trend model to study pseudotime in bioinformatics, estimating parameters in the commonly used Rasch model in education research, finding M-estimates for a Cox regression in a Markov renewal model, performing matrix completion tasks to impute missing data for a two compartment model, and selecting variables optimally in an ecology problem in China. To further demonstrate the flexibility of metaheuristics, we also find an optimal design for a car refueling experiment in the auto industry using a logistic model with multiple interacting factors. In addition, we show that metaheuristics can sometimes outperform optimization algorithms commonly used in statistics.

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“…Many research studies based on compartmental/epidemic models have been conducted to evaluate the COVID-19 outbreak [14], [20]. Researchers have also enhanced epidemiological models by introducing new compartments and applying various machine learning techniques for better prediction accuracy [3], [5], [22]. Shinde et al [31] summarized various forecasting techniques for COVID-19 that include stochastic theory, mathematical models, data science, and machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Epidemic Modeling using Hybrid of Time-varying SIRD, Particle Swarm Optimization, and Deep Learning

Kumar,

Susan

2023

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Epidemiological models are best suitable to model an epidemic if the spread pattern is stationary. To deal with nonstationary patterns and multiple waves of an epidemic, we develop a hybrid model encompassing epidemic modeling, particle swarm optimization, and deep learning. The model mainly caters to three objectives for better prediction: 1. Periodic estimation of the model parameters. 2. Incorporating impact of all the aspects using data fitting and parameter optimization 3. Deep learning based prediction of the model parameters. In our model, we use a system of ordinary differential equations (ODEs) for Susceptible-Infected-Recovered-Dead (SIRD) epidemic modeling, Particle Swarm Optimization (PSO) for model parameter optimization, and stacked-LSTM for forecasting the model parameters. Initial or one time estimation of model parameters is not able to model multiple waves of an epidemic. So, we estimate the model parameters periodically (weekly). We use PSO to identify the optimum values of the model parameters. We next train the stacked-LSTM on the optimized parameters, and perform forecasting of the model parameters for upcoming four weeks. Further, we fed the LSTM forecasted parameters into the SIRD model to forecast the number of COVID-19 cases. We evaluate the model for highly affected three countries namely; the USA, India, and the UK. The proposed hybrid model is able to deal with multiple waves, and has outperformed existing methods on all the three datasets.

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Prediction on transmission trajectory of COVID-19 based on particle swarm algorithm

Cited by 10 publications

References 16 publications

Real-time forecasting of the COVID 19 using fuzzy grey Markov: a different approach in decision-making

Real-time forecasting of the COVID 19 using fuzzy grey Markov: a different approach in decision-making

Applications of nature-inspired metaheuristic algorithms for tackling optimization problems across disciplines

Epidemic Modeling using Hybrid of Time-varying SIRD, Particle Swarm Optimization, and Deep Learning

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