Forecasting the Temporal Evolution of COVID-19
AIST 2022 - 4th International Conference on Artificial Intelligence and Speech Technology
; 2022.
Article
in English
| Scopus | ID: covidwho-2299440
ABSTRACT
COVID-19 epidemic has resulted in severe chaos across the globe. Complex frameworks can be investigated and studied using mathematical models, which are reliable and efficient. The objective of this research is to scrutinize the progression and prediction of parameters that evaluate the emergence and transmission of COVID-19 in the two most affected nations, i.e., the USA and India. Five models including the standard and hybrid epidemic models, viz, SIR (Susceptible-Infectious-Removed), SIRD (Susceptible-Infectious-Recovered-Death), SIRD with vaccination, SIRD with vital dynamics (i.e., including birth rate and death rate) and, SIRD with vital dynamics and vaccination have been developed. Worldwide statistics have been observed utilizing graphical layouts. Model evaluation measures such as Mean Absolute error (MAE), Mean-square error (MSE), and Root Mean Square Error (RMSE) for different parameters namely infection rate, recovery rate, and death rate have been estimated. © 2022 IEEE.
Data visualization; Errors; Forecasting; Mean square error; Population statistics; Vaccines; Visualization; Birth rates; Death rates; Epidemic modeling; Evaluation measures; Mean absolute error; Means square errors; Model evaluation; Temporal evolution; Treemap visualization; Visual analytics; COVID-19; Data Analysis; Mathematical Models
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
AIST 2022 - 4th International Conference on Artificial Intelligence and Speech Technology
Year:
2022
Document Type:
Article
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