Pandemien und die Welt der Mathematik
Background During a pandemic, it is necessary to make far-reaching decisions to protect the population in order to bring the course of infection under control. To this end, suitable indicators must be identified and mathematical models used. These measures may have a direct and longer-term impact on...
Gespeichert in:
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Deutsch |
| Veröffentlicht: |
May 2025
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| In: |
Notfall & Rettungsmedizin
Year: 2025, Jahrgang: 28, Heft: 3, Pages: 158-164 |
| ISSN: | 1436-0578 |
| DOI: | 10.1007/s10049-025-01465-z |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/s10049-025-01465-z Verlag, lizenzpflichtig, Volltext: https://link.springer.com/article/10.1007/s10049-025-01465-z 
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| Verfasserangaben: | Christel Weiß |
MARC
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| 520 | |a Background During a pandemic, it is necessary to make far-reaching decisions to protect the population in order to bring the course of infection under control. To this end, suitable indicators must be identified and mathematical models used. These measures may have a direct and longer-term impact on the frequency and the type of emergency medical services interventions. Objectives The goal is to explain relevant terms for describing a pandemic in an understandable way. The SIR (susceptible, infected, recovered/removed) model is also used to show how an epidemiological model is mathematically generated to describe the course of a pandemic. Materials and methods First, the temporal course of an infection is described. Then the importance of relevant parameters such as basic reproduction number, incidence or case-fatality rate is explained. The SIR model is used to show how the course of the pandemic can be described using a differential equation system and how it is possible to predict the expected course and the impact of nonpharmacological measures based on different framework conditions. Finally, the benefits and limitations of epidemiologic models are discussed. Results and conclusion Despite some limitations (e.g., due to poor data structure, an inefficient testing strategy or an unknown number of unreported cases), these models are an important tool for simulating possible scenarios and estimating the impact of different prevention measures. | ||
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