A simulated measurement for COVID-19 pandemic using the effective reproductive number on an empirical portion of population: epidemiological models

Belal Alsinglawi, Omar Mubin, Fady Alnajjar, Khalid Kheirallah, Mahmoud Elkhodr, Mohammed Al Zobbi, Mauricio Novoa, Mudassar Arsalan, Tahmina Nasrin Poly, Munkhjargal Gochoo, Gulfaraz Khan, Kapal Dev

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

COVID-19 as a global pandemic has had an unprecedented impact on the entire world. Projecting the future spread of the virus in relation to its characteristics for a specific suite of countries against a temporal trend can provide public health guidance to governments and organizations. Therefore, this paper presented an epidemiological comparison of the traditional SEIR model with an extended and modified version of the same model by splitting the infected compartment into asymptomatic mild and symptomatic severe. We then exposed our derived layered model into two distinct case studies with variations in mitigation strategies and non-pharmaceutical interventions (NPIs) as a matter of benchmarking and comparison. We focused on exploring the United Arab Emirates (a small yet urban centre (where clear sequential stages NPIs were implemented). Further, we concentrated on extending the models by utilizing the effective reproductive number (Rt) estimated against time, a more realistic than the static R0, to assess the potential impact of NPIs within each case study. Compared to the traditional SEIR model, the results supported the modified model as being more sensitive in terms of peaks of simulated cases and flattening determinations.

Original languageEnglish
JournalNeural Computing and Applications
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • COVID-19
  • Effective reproductive number
  • Epidemiologic methods
  • Outbreak
  • SEIR
  • Simulation

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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