The predictability of Covid-19 mortality rates based on ex-ante economic, health and social indicators
Bibliographic Data
| ID | 21281950 |
|---|---|
| Authors | Emese Kovács (0000-0003-3838-3679, Corvinus University of Budapest), Péter Mihályi (0000-0002-8563-6950, Corvinus University of Budapest) |
| Year | 2021 |
| Volume | 71 |
| Issue | S1 |
| Pages | 53-71 |
| Publication date | 2021-11-25 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Acta Oeconomica (JOURNAL) |
| Journal identifiers | ISSN: 0001-6373 • E-ISSN: 1588-2659 |
| Publisher | Akademiai Kiado Zrt. (PUBLISHER • HU) |
| DOI | 10.1556/032.2021.00029 |
| OpenAlex | W4205624988 |
| Language | EN |
| Citations received | 1 |
| References cited | 4 |
The paper analyses the differences of COVID-19 mortality rates (MR) in 24 European countries. We explain MRs on the available, reliable ex-ante economic, health and social indicators pertaining to the year 2019 – i.e., before the outbreak of the pandemic. Using simple regression equations, we received statistically significant results for 11 such variables out of 28 attempts. Our best model with two ex-ante independent variables explains 0.76 of the variability of our ex-post dependent variable, the logarithm of Cumulative COVID Deaths. The estimated coefficient for the variable Density of Nurses shows that having one more nurse per 1,000 of population decreases cumulative COVID deaths by almost 15%. Similarly, one more unit Consumption of Non-Prescribed Medicine decreases cumulative deaths by 5%. It seems that until now those European countries were successful in minimising the fatalities where the population had a high level of health literacy, people pursue healthier lifestyle and the healthcare systems worked with a relatively large nursing force already prior to the COVID pandemic
Coronavirus disease 2019 (COVID-19) · Demographic economics · Economics · Ex-ante · Pandemic · Population · Predictability · Regression analysis · Sociology · Statistics · Variable (mathematics) · COVID-19 and healthcare impacts · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Demography · Mathematics · Medicine
| Citations per year | 0.25 |
|---|---|
| Citation span | 2022 - 2022 (1) |
| Highly cited | No |