scholarly journals Modeling the Dynamics and Forecasting the fourth Peak of COVID-19 in Iran Using PSO Algorithm

2021 ◽  
Vol 8 (4) ◽  
pp. 140-145
Author(s):  
Ebrahim Sahafizadeh ◽  
Mohammad Ali Khajeian
Keyword(s):  
Pso Algorithm ◽  
Future Trend ◽  
Short Term ◽  
Estimated Parameters ◽  
Term Prediction ◽  
Proposed Model ◽  
Spread Model ◽  
Short Term Prediction ◽  
Reported Data ◽  
The Given

Background and aims: Iran had passed the third peak of COVID-19 pandemic, and was probably witnessing the fourth peak at the time of this study. This study aimed to model the spread of COVID-19 in Iran in order to predict the short-term future trend of COVID-19 from April 23, 2021 to May 7, 2021. Methods: In this study, a modified SEIR epidemic spread model was proposed and the data on the number of cases reported by Iranian government from February 20, 2020 to April 23, 2021 were used to fit the proposed model to the reported data using particle swarm optimization (PSO) algorithm. Then the short-term future trend of COVID-19 cases were predicted by using the estimated parameters. Results: The results indicated that the effective reproduction number increased in Nowruz (i.e., Persian New Year, 1400) and it was estimated to be 1.28 in the given period. According to the results from the short-term prediction of COVID-19 cases, the number of active confirmed cases in the fourth peak was estimated to be 516411 cases on May 2, 2021. Conclusion: Following the results from our short-term prediction, implementing strict social distancing policies was found absolutely necessary for relieving the Iran’s health care system of the tremendous burden of COVID-19.

scholarly journals Modeling COVID-19 in Iran using Particle Swarm Optimization algorithm

2021 ◽  
Author(s):  
Ebrahim Sahafizadeh ◽  
MohammadAli Khajeian
Keyword(s):  
Particle Swarm Optimization ◽  
Reproduction Number ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Future Trend ◽  
Cumulative Number ◽  
Future Trends ◽  
Short Term ◽  
Swarm Optimization ◽  
Proposed Model

AbstractBackgroundThe first confirmed cases of COVID-19 in Iran were reported on February 19, 2020. The coronavirus expanded rapidly in all Iranian provinces and three waves of COVID-19 cases have been observed since the pandemic took effect and the fourth wave of Covid-19 cases will likely be observed soon. This study aimed to model the spread of COVID-19 in Iran and to estimate the epidemic parameters and to predict the short-term future trend of COVID-19 in Iran.MethodsWe proposed a modified SEIR epidemic spreading model and we used data from February 20, 2020, to April 9, 2021, on the number of cases reported by Iranian governments to fit the proposed model on the reported data. Particle Swarm Optimization (PSO) algorithm was employed to estimate the parameters of the proposed model and the numerical simulation results were obtained by Runge-Kutta method. The estimated parameters were employed to calculate the effective reproduction number and to predict the short-term future trends of COVID-19 cases.ResultsThe results indicated that the effective reproduction number has increased during Nowruz (Persian New Year) and it was estimated to be 1.28. Considering only two exposed cases as the initial cases in the model, the cumulative number of exposed cases was estimated to be 15,252,372 individuals since the beginning of the outbreak. The prediction of the short-term future trends of COVID-19 cases with different scenarios showed that another peak of the pandemic cases occurs in the next weeks. By immediate lockdown implementation the number of active infected cases was estimated to be 397,585.ConclusionDifferent scenarios of short-term prediction of the future trends of COVID-19 cases indicated that immediate strict social distancing policies need to be implemented to prevent a tremendous burden of the fourth major wave of COVID-19 infections on the health care system of Iran.

Development of a Technique for Short-Term Prediction of Hydrometeors Using Advection and Physical Forcing.

1983 ◽  
Author(s):  
Gregory S. Forbes ◽  
John J. Cahir ◽  
Paul B. Dorian ◽  
Walter D. Lottes ◽  
Kathy Chapman
Keyword(s):  
Short Term ◽  
Physical Forcing ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

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