Mathematical Modeling of COVID-19 Spread Using Genetic Programming Algorithm
1st Serbian International Conference on Applied Artificial Intelligence, SICAAI 2022
; 659 LNNS:320-331, 2023.
Article
in English
| Scopus | ID: covidwho-2292163
ABSTRACT
This paper analyses the possibilities of using Machine learning to develop a forecasting model for COVID-19 with a publicly available dataset from the Johns Hopkins University COVID-19 Data Repository and with the addition of a percentage of each variant from the GISAID Variant database. Genetic programming (GP), a symbolic regressor algorithm, is used for the estimation of new confirmed infected cases, hospitalized cases, cases in intensive care units (ICUs), and deceased cases. This metaheuristics method algorithm was used on a dataset for Austria and neighboring countries Czechia, Hungary, Slovenia, and Slovakia. Machine learning was done to create individual models for each country. Variance-based sensitivity analysis was initiated using the obtained mathematical models. This analysis showed us which input variables the output of the obtained models is sensitive to, like in the case of how much each covid variant affects the spread of the virus or the number of deceased cases. Individual short-term models have achieved very high R2 scores, while long-term predictions have achieved lower R2 scores. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Artificial intelligence; COVID-19; genetic programming; mathematical prediction models; variants; Forecasting; Genetic algorithms; Intensive care units; Machine learning; Sensitivity analysis; Viruses; Austria; Data repositories; Forecasting models; Genetic programming algorithms; John Hopkins University; Machine-learning; Meta-heuristic methods; Paper analysis; Variant
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
1st Serbian International Conference on Applied Artificial Intelligence, SICAAI 2022
Year:
2023
Document Type:
Article
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