Journals / CMC / Vol.67, No.3
Table of Content

Research Article


Prediction Models for COVID-19 Integrating Age Groups, Gender, and Underlying Conditions

Imran Ashraf1, Waleed S. Alnumay2, Rashid Ali3, Soojung Hur1, Ali Kashif Bashir4, Yousaf Bin Zikria1,*
1 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, 38541, Korea
2 Department of Computer Science, King Saud University, Riyadh, Saudi Arabia
3 School of Intelligent Mechatronics Engineering, Sejong University, Korea
4 Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom
* Corresponding Author: Yousaf Bin Zikria. Email:
(This article belongs to this Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)


The COVID-19 pandemic has caused hundreds of thousands of deaths, millions of infections worldwide, and the loss of trillions of dollars for many large economies. It poses a grave threat to the human population with an excessive number of patients constituting an unprecedented challenge with which health systems have to cope. Researchers from many domains have devised diverse approaches for the timely diagnosis of COVID-19 to facilitate medical responses. In the same vein, a wide variety of research studies have investigated underlying medical conditions for indicators suggesting the severity and mortality of, and role of age groups and gender on, the probability of COVID-19 infection. This study aimed to review, analyze, and critically appraise published works that report on various factors to explain their relationship with COVID-19. Such studies span a wide range, including descriptive analyses, ratio analyses, cohort, prospective and retrospective studies. Various studies that describe indicators to determine the probability of infection among the general population, as well as the risk factors associated with severe illness and mortality, are critically analyzed and these findings are discussed in detail. A comprehensive analysis was conducted on research studies that investigated the perceived differences in vulnerability of different age groups and genders to severe outcomes of COVID-19. Studies incorporating important demographic, health, and socioeconomic characteristics are highlighted to emphasize their importance. Predominantly, the lack of an appropriated dataset that contains demographic, personal health, and socioeconomic information implicates the efficacy and efficiency of the discussed methods. Results are overstated on the part of both exclusion of quarantined and patients with mild symptoms and inclusion of the data from hospitals where the majority of the cases are potentially ill.


COVID-19; age & gender vulnerability for COVID-19; machine learning-based prognosis; COVID-19 vulnerability; psychological factors; prediction of COVID-19
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