Covid-19 Global Risk
Expectation vs. Reality
Bibliographic Data
| ID | 15465694 |
|---|---|
| Authors | Mudassar Hassan Arsalan (0000-0001-9622-5930, Western Sydney University), Omar Mubin (0000-0002-6435-6407, Western Sydney University), Fady Alnajjar (0000-0001-6102-3765, United Arab Emirates University, corresponding author), Belal Alsinglawi (0000-0003-0316-3641, Western Sydney University) |
| Year | 2020 |
| Volume | 17 |
| Issue | 15 |
| Pages | 5592-5592 |
| Publication date | 2020-08-03 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph17155592 |
| PMID | 32756513 |
| OpenAlex | W3046385424 |
| Language | EN |
| Citations received | 1 |
| References cited | 32 |
Background and Objective : COVID-19 has engulfed the entire world, with many countries struggling to contain the pandemic. In order to understand how each country is impacted by the virus compared with what would have been expected prior to the pandemic and the mortality risk on a global scale, a multi-factor weighted spatial analysis is presented. Method : A number of key developmental indicators across three main categories of demographics, economy, and health infrastructure were used, supplemented with a range of dynamic indicators associated with COVID-19 as independent variables. Using normalised COVID-19 mortality on 13 May 2020 as a dependent variable, a linear regression (N = 153 countries) was performed to assess the predictive power of the various indicators. Results : The results of the assessment show that when in combination, dynamic and static indicators have higher predictive power to explain risk variation in COVID-19 mortality compared with static indicators alone. Furthermore, as of 13 May 2020 most countries were at a similar or lower risk level than what would have been expected pre-COVID, with only 44/153 countries experiencing a more than 20% increase in mortality risk. The ratio of elderly emerges as a strong predictor but it would be worthwhile to consider it in light of the family makeup of individual countries. Conclusion : In conclusion, future avenues of data acquisition related to COVID-19 are suggested. The paper concludes by discussing the ability of various factors to explain COVID-19 mortality risk. The ratio of elderly in combination with the dynamic variables associated with COVID-19 emerge as more significant risk predictors in comparison to socio-economic and demographic indicators
Actuarial science · Coronavirus disease 2019 (COVID-19 · Demographics · Disease · Econometrics · Economics · Geography · Pandemic · Predictive power · Regression analysis · Risk assessment · Scale (ratio · Statistics · Variable (mathematics · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Demography · Mathematics · Medicine · Zoonotic diseases and public health
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| Unique citing works | 1 |
|---|---|
| Citations per year | 0,2 |
| Citation span | 2021 - 2021 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |