Pandemetrics
Systematically assessing, monitoring, and controlling the evolution of a pandemic
Bibliographic Data
| ID | 21378266 |
|---|---|
| Authors | Stefano Barone (0000-0003-0437-9452, University of Palermo, corresponding author), Alexander Chakhunashvili (0000-0002-2616-8093, Karolinska University Hospital) |
| Year | 2023 |
| Volume | 57 |
| Issue | 2 |
| Pages | 1701-1723 |
| Publication date | 2023-04-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Quality & Quantity (JOURNAL) |
| Journal identifiers | ISSN: 0033-5177 • E-ISSN: 1573-7845 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11135-022-01424-7 |
| PMID | 35694109 |
| OpenAlex | W4281698229 |
| Language | EN |
| References cited | 25 |
The still ongoing pandemic of SARS-CoV-2 virus and COVID-19 disease, affecting the population worldwide, has demonstrated the need of more accurate methodologies for assessing, monitoring, and controlling an outbreak of such devastating proportions. Authoritative attempts have been made in traditional fields of medicine (epidemiology, virology, infectiology) to address these shortcomings, mainly by relying on mathematical and statistical modeling. However, here, we propose approaching the methodological work from a different, and to some extent alternative, standpoint. Applied systematically, the concepts and tools of statistical engineering and quality management, developed not only in healthcare settings, but also in other scientific contexts, can be very useful in assessing, monitoring, and controlling pandemic events. We propose a methodology based on a set of tools and techniques, formulas, graphs, and tables to support the decision-making concerning the management of a pandemic like COVID-19. This methodological body is hereby named Pandemetrics. This name intends to emphasize the peculiarity of our approach to measuring, and graphically presenting the unique context of the COVID-19 pandemic
Context (archaeology) · Coronavirus disease 2019 (COVID-19) · Data science · Disease · Epistemology · Geography · Infectious disease (medical specialty) · Management science · Operations research · Pandemic · Population · Quality (philosophy) · Risk analysis (engineering) · Set (abstract data type) · Work (physics) · Advanced Statistical Methods and Models · Computer Science · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Engineering · Medicine
| Highly cited | No |
|---|