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  The Covid-19 pandemic: basic insights from basic mathematical models

Traulsen, A., Gokhale, C. S., Shah, S., & Uecker, H. (2022). The Covid-19 pandemic: basic insights from basic mathematical models. NAL-live / Deutsche Akademie der Naturforscher Leopoldina e. V. - Nationale Akademie der Wissenschaften, 3: 01000. doi:10.34714/leopoldina_nal-live_0003_01000.

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 Creators:
Traulsen, Arne1, Author              
Gokhale, Chaitanya S.2, Author              
Shah, Saumil1, 3, Author              
Uecker, Hildegard4, Author              
Affiliations:
1Department Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_1445641              
2Research Group Theoretical Models of Eco-Evolutionary Dynamics, Department Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_2355692              
3IMPRS for Evolutionary Biology, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_1445639              
4Research Group Stochastic Evolutionary Dynamics, Department Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_2640692              

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Free keywords: heoretical Biology, Theoretical Epidemiology, Mathematical Biology, SIR model, Branching processes
 Abstract: Mathematical models for the spread of infectious diseases have a long history. From the start of the Covid-19 pandemic, there was a huge public interest in applying such models, since they help to understand general features of epidemic spread and support the assessment of possible mitigation measures – and their later relaxation. We describe and discuss some well-established mathematical models for epidemic spread, starting from the susceptible-infected-recovered (SIR) model and branching processes and discussing insights from network-based models. During the Covid-19 pandemic, such classical models have also been extended to include many additional aspects that affect epidemic spread, such as mobility patterns or testing possibilities. However, such complex models are increasingly difficult to assess from the outside. In a situation where their predictions can directly affect the lives of millions of people, this can become a severe problem. We argue that simple mathematical models have huge merits and can explain many of the key features of more complex models, such as the importance of heterogeneity in disease transmission. For example, basic models allow inferring whether super-spreading, where very few infected individuals cause the vast majority of secondary cases, should be the rule or the exception – with wide-ranging consequences for the possible success of mitigation measures. In addition, these basic models are simple enough to be understood and implemented without expert knowledge in theoretical epidemiology or computer science. Thus, they offer a level of transparency that can be important for a society to accept mitigation measures.

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Language(s): eng - English
 Dates: 2022-03-03
 Publication Status: Published in print
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Title: NAL-live / Deutsche Akademie der Naturforscher Leopoldina e. V. - Nationale Akademie der Wissenschaften
  Other : NAL-Live
Source Genre: Journal
 Creator(s):
Beiglböck, Matthias, Editor
Affiliations:
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Publ. Info: Halle (Saale) : Deutsche Akademie der Naturforscher Leopoldina e. V. - Nationale Akademie der Wissenschaften
Pages: 20 Volume / Issue: 3 Sequence Number: 01000 Start / End Page: - Identifier: ISSN: 2699-8955
CoNE: https://pure.mpg.de/cone/journals/resource/2699-8955