Projektion der COVID-19-Epidemie in Deutschland : = Projecting the spread of COVID-19 for Germany / Jean Roch Donsimoni, René Glawion, Bodo Plachter, Klaus Wälde
Die COVID-19-Epidemie wird in Deutschland im optimistischen Szenario mindestens bis Juli 2020 dauern, im normalen Szenario bis August. Öffentliche Maßnahmen wie Kontaktverbote flachen den Anstieg der Zahl der Erkrankungen temporär ab und verlängern die Dauer der Epidemie. In der Spitze sind im optimistischen Szenario gleichzeitig bis zu 200.000 Menschen erkrankt, im normalen Szenario liegen die Werte um 1 Million. Langfristig werden im normalen Szenario 6 % der Bevölkerung (gemeldet) erkrankt gewesen sein..
The authors model the evolution of the number of confi rmed cases of COVID-19 in Germany. Their theoretical framework builds on a continuous time Markov chain with four physical states: healthy, sick, recovered or asymptomatic infected, and dead. Their quantitative solution matches the number of sick individuals based on the most recent fi gures with the share of sick individuals following from infection rates and sickness probabilities. They employ this framework to study the expected peak of the number of sick individuals in a scenario without public regulation of social contacts. They also study the impact of public regulations. For all scenarios, they report the expected end of the COVID-19 epidemic..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:100 |
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| Enthalten in: |
Wirtschaftsdienst - 100(2020), 4 vom: Apr., Seite 272-276 |
| Sprache: |
Deutsch Zusammenfassung in englischer Sprache |
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| Weiterer Titel: |
Projecting the spread of COVID-19 for Germany |
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| Beteiligte Personen: |
Donsimoni, Jean Roch, 1989- [VerfasserIn] |
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| Links: |
link.springer.com [kostenfrei] |
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| doi: |
10.1007/s10273-020-2631-5 |
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| Weitere IDs: |
10419/221977 |
| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
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| 520 | |a The authors model the evolution of the number of confi rmed cases of COVID-19 in Germany. Their theoretical framework builds on a continuous time Markov chain with four physical states: healthy, sick, recovered or asymptomatic infected, and dead. Their quantitative solution matches the number of sick individuals based on the most recent fi gures with the share of sick individuals following from infection rates and sickness probabilities. They employ this framework to study the expected peak of the number of sick individuals in a scenario without public regulation of social contacts. They also study the impact of public regulations. For all scenarios, they report the expected end of the COVID-19 epidemic. | ||
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