COVID-19 infections, recoveries, and mortality: an ANOVA model of locations and administrative areas in Saudi Arabia

Front Public Health. 2024 Jan 17:12:1281289. doi: 10.3389/fpubh.2024.1281289. eCollection 2024.

Abstract

Background: Saudi Arabia has 13 administrative areas, all of which have been seriously affected by the COVID-19 epidemic regardless of their features. Being the largest and a prominent Arab country, epidemic intensity and dynamics have importance, especially in the era of Vision 2030 where infrastructure development and growth to enhance quality of life has of prime focus.

Aims: This analysis aims to trace the differentials in COVID-19 infections, recoveries, and deaths across the country depending upon various demographic and developmental dimensions and interactions.

Data and methods: This analysis used Saudi Arabia Ministry of Health data from March 15th, 2020 to August 31st, 2022, by classifying administrative areas and locations to build a generalized linear model (3 × 3): three types of administrative areas (major, middle-sized, and others) and localities (major, medium-sized, and others). Apart from two-way ANOVA, an one-way ANOVA also carried out in addition to calculating mean values of infections, recoveries, and deaths.

Results: A total of 205 localities were affected with varying severity, which are based on local demographics. Both the administrative areas and localities had a significant number of cases of infections, recoveries, and mortality, which are influenced by relationships and interactions, leading to differential mean values and proportional distributions across various types of administrative areas and localities.

Conclusion: There is dynamism that major administrative areas have lesser threats from the epidemics whereas medium-sized ones have serious threats. Moreover, an interaction of administrative areas and localities explains the dynamics of epidemic spread under varying levels of infrastructure preparedness. Thus, this study presents lessons learned to inform policies, programs, and development plans, especially for regional, urban, and infrastructure areas, considering grassroots level issues and diversity.

Keywords: grassroot level interventions; infrastructure development; mean number of infected cases; univariate general linear model; urbanization.

Publication types

  • Review
  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19* / epidemiology
  • Epidemics*
  • Humans
  • Middle East
  • Quality of Life
  • Saudi Arabia / epidemiology

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This manuscript has been funded by The Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University, Riyadh, through the Research Grant No. IMSIU-RG23162.