A biological sub-sequences detection using integrated BA-PSO based on infection propagation mechanism: Case study COVID-19

Issa, Mohamed; Helmi, Ahmed; Elsheikh, Ammar H.; Elaziz, Mohamed Abd

doi:10.1016/j.eswa.2021.116063

Cited by 21 publications

(7 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Metaheuristic optimization approaches have been also used to predict the prevalence of this pandemic [ 76 ]. A genetic-based programming approach has been developed to predict the number of total cases and total deaths in three Indian states, namely, Gujarat, Maharashtra and Delhi [ 77 ].…”

Section: Comparative Study and Discussionmentioning

confidence: 99%

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview

Elsheikh

Saba

Panchal³

et al. 2021

Healthcare

Self Cite

View full text Add to dashboard Cite

Since the discovery of COVID-19 at the end of 2019, a significant surge in forecasting publications has been recorded. Both statistical and artificial intelligence (AI) approaches have been reported; however, the AI approaches showed a better accuracy compared with the statistical approaches. This study presents a review on the applications of different AI approaches used in forecasting the spread of this pandemic. The fundamentals of the commonly used AI approaches in this context are briefly explained. Evaluation of the forecasting accuracy using different statistical measures is introduced. This review may assist researchers, experts and policy makers involved in managing the COVID-19 pandemic to develop more accurate forecasting models and enhanced strategies to control the spread of this pandemic. Additionally, this review study is highly significant as it provides more important information of AI applications in forecasting the prevalence of this pandemic.

show abstract

Section: Comparative Study and Discussionmentioning

confidence: 99%

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview

Elsheikh

Saba

Panchal³

et al. 2021

Healthcare

Self Cite

View full text Add to dashboard Cite

show abstract

“…The SARS-CoV-2 virus is the infectious agent that causes the sickness known as coronavirus disease (COVID-19) (Issa et al, 2022). Most persons who get the virus will suffer from a respiratory illness that is either mild or moderate in severity, and they will recover without needing any specialised therapy.…”

Section: Covid-19 Impactmentioning

confidence: 99%

Assessment of Socioeconomic Development of the Aspirational District in Central India: A Methodological Comparison

Katekar

Deshmukh

2022

Journal of Asian and African Studies

View full text Add to dashboard Cite

This study examines the Washim district’s socioeconomic growth to determine the criteria for district development. This study found that the Washim district comes under the rural area, where the child sex ratio is inferior. Even in some villages, it has zero value. In some regions where the Scheduled Caste (SC)/Scheduled Tribe (ST) population is more, the child sex ratio is less. Out of six sub-districts, four sub-districts have a low literacy rate (less than 75%). The mean literacy rate is found as 81.7%, and its standard deviation is evaluated as 7.0. In the region where the literacy rate is higher, people are less busy with farming, and hence the percentage of the cultivator is less and vice versa. In the region where SC and ST population is more, the percentage of Household Industry (HHI) worker is higher and vice versa. Out of 698 inhabited villages, 692 villages are electrified in the district, with 99.1% of towns having installed power supply. The use of liquefied petroleum gas (LPG)/piped natural gas (PNG) is minimal in the Washim district. In the region where firewood availability is accessible and adequate, the use of LPG/PNG is found to be less than its average value.

show abstract

“…However, no single model performs best across different countries, due to their diverse pandemic trends 19 . Recently, AI/machine learning models have also been used in other various prediction problems during the COVID-19 epidemic, such as detecting COVID-19 infections from CT/X-ray images using CNNs 20 and identifying virus subsequences based on infection propagation mechanisms using the Fragmented Local Aligner Technique (FLAT) model 21 .…”

Section: Introductionmentioning

confidence: 99%

Forecasting the spread of COVID-19 based on policy, vaccination, and Omicron data

Han,

Lee,

Lee

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The COVID-19 pandemic caused by the novel SARS-COV-2 virus poses a great risk to the world. During the COVID-19 pandemic, observing and forecasting several important indicators of the epidemic (like new confirmed cases, new cases in intensive care unit, and new deaths for each day) helped prepare the appropriate response (e.g., creating additional intensive care unit beds, and implementing strict interventions). Various predictive models and predictor variables have been used to forecast these indicators. However, the impact of prediction models and predictor variables on forecasting performance has not been systematically well analyzed. Here, we compared the forecasting performance using a linear mixed model in terms of prediction models (mathematical, statistical, and AI/machine learning models) and predictor variables (vaccination rate, stringency index, and Omicron variant rate) for seven selected countries with the highest vaccination rates. We decided on our best models based on the Bayesian Information Criterion (BIC) and analyzed the significance of each predictor. Simple models were preferred. The selection of the best prediction models and the use of Omicron variant rate were considered essential in improving prediction accuracies. For the test data period before Omicron variant emergence, the selection of the best models was the most significant factor in improving prediction accuracy. For the test period after Omicron emergence, Omicron variant rate use was considered essential in deciding forecasting accuracy. For prediction models, ARIMA, lightGBM, and TSGLM generally performed well in both test periods. Linear mixed models with country as a random effect has proven that the choice of prediction models and the use of Omicron data was significant in determining forecasting accuracies for the highly vaccinated countries. Relatively simple models, fit with either prediction model or Omicron data, produced best results in enhancing forecasting accuracies with test data.

show abstract

A biological sub-sequences detection using integrated BA-PSO based on infection propagation mechanism: Case study COVID-19

Cited by 21 publications

References 50 publications

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview

Assessment of Socioeconomic Development of the Aspirational District in Central India: A Methodological Comparison

Forecasting the spread of COVID-19 based on policy, vaccination, and Omicron data

Contact Info

Product

Resources

About