Statistical study for Covid-19 spread during the armed crisis faced by Ukrainians

Mustafa Kamal, Mintodê Nicodème Atchadé, Yves Morel Sokadjo, Nayabuddin, Eslam Hussam, Ahmed M. Gemeay, Fatimah A. Almulhim, Amirah Saeed Alharthi, Hassan M. Aljohani

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Russia and Ukraine got into an armed conflict on 24th February 2022. In addition, the World Health Organisation still warns of a fast growth in infections and deaths. Infectious disease remains a serious issue in Ukraine and poorly governed cities, such as those in armed conflicts. During this period of security instability, the coronavirus situation in Ukraine is alarming and needs more attention. In this context, our focus in the current work is to model COVID-19 spread risk from Ukrainian international refugees in neighboring countries. This study aims to estimate the number of daily coronavirus cases among Ukrainian international refugees for informed decisions for the pandemics' spread risk. For that reason, we used “Coronavirus Pandemic (COVID-19)” data from “Our World in Data” (from 2020-03-03 to 2022-02-22) and the data about Ukrainian International Refugees provided by United Nations High Commissioner for Refugees related (from 2022-02-22 to 2022-03-11). We performed ARIMA, TBATS, and ETS and selected the best model. Through a cross-validation process, the findings revealed that around 6 individuals [95% CI: 5%–7%] over 10,000 Ukrainian international refugees are likely COVID-19 cases. ARIMA is the best model to fit the Ukrainian daily number of cases among the refugees fleeing the crisis. On average, they are daily 100 possible COVID-19 cases among Ukrainian international refugees and authorities and humanitarian actors need be informed decisions to control the pandemic and support refugees effectively.

Original languageEnglish
Pages (from-to)419-425
Number of pages7
JournalAlexandria Engineering Journal
Volume78
DOIs
StatePublished - 1 Sep 2023
Externally publishedYes

Keywords

  • Covid-19
  • Migration
  • Public health
  • Statistical modeling
  • Ukraine

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