A Generalized Data-Driven Modelling for COVID-19 Pandemic Outbreak: Jordan Case Study

Walid A. Salameh, Ola M. Surakhi, Ibrahim Abu Alhaol, Pak Lun Fung, Mohammad AlKhanafseh, Tareq Hussein

Research output: Contribution to journalArticleScientificpeer-review


The explosion of the COVID-19 pandemic shows the limitations of the existing healthcare system in handling health emergencies. Innovative technologies like artificial intelligence and mathematical modelling can play an essential role in effective planning operations for fighting coronavirus pandemic. In this paper, a comparison between the mathematical model performance for forecasting the number of positive cases in Jordan and three data-driven models is performed to provide more insight on the appropriate model that can be utilized for COVID-19 disease spreading analysis. Our finding shows that both types of models can assess disease-spreading research. Including more factors in the forecasting process can increase the complexity of mathematical model analysis. On the other hand, the datadriven models and mechanisms will be more appropriate for this purpose. However, much work is still needed to capture more factors in the modelling process and provide a reliable solution for stopping this pandemic.
Original languageEnglish
JournalInternational Journal of Emerging Technology and Advanced Engineering
Issue number2
Pages (from-to)170-181
Publication statusPublished - 4 Feb 2023
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 3142 Public health care science, environmental and occupational health
  • 5142 Social policy

Cite this