Modeling approaches for early warning and monitoring of pandemic situations as well as decision support
Bibliographic Data
| ID | 22070293 |
|---|---|
| Authors | Jonas Botz (0009-0004-6056-3144, Fraunhofer Institute for Algorithms and Scientific Computing, corresponding author), Danqi Wang (0000-0002-3064-235X, University of Bonn), Nicolas Lambert (0000-0003-4976-6560), Nicolas Wagner (0000-0001-5383-711X), Marie Génin, Edward Thommes, Edward W Thommes (0000-0001-6800-2000, Sanofi (France)), Sumit Madan (0000-0001-9970-4144, University of Bonn), Laurent Coudeville (0000-0003-3651-2959, Sanofi (France)), Holger Fröhlich (0000-0002-5328-1243, University of Bonn, corresponding author) |
| Year | 2022 |
| Volume | 10 |
| Pages | 994949-994949 |
| Publication date | 2022-11-14 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2022.994949 |
| PMID | 36452960 |
| OpenAlex | W4309039920 |
| Language | EN |
| Citations received | 4 |
| References cited | 100 |
The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments
Data science · Decision support system · Disease · Environmental health · Health care · Management science · Operations research · Pandemic · Political science · Population · Preparedness · Warning system · Computer Science · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Engineering · Influenza Virus Research Studies · Medicine · Artificial Intelligence
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Covidsenti
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| Unique citing works | 4 |
|---|---|
| Citations per year | 1,33 |
| Citation span | 2023 - 2025 (3) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 4 |