Countries around the world implemented non-pharmaceutical interventions (NPIs) to flatten the curve of COVID-19 cases but failed to eradicate the disease . Design of efficient NPIs requires identification of the structure of the underlying transmission network. We combine Bayesian parameter inference with a network-based epidemiological modelthat allows to interpolate between random and small-world transmission networks. Using epidemiological data from Germany, we show that NPIs reduced non-local contacts in the transmission network, resulting in a change from an exponential to a constant regime . Due to the small-world nature of the inferred network, exponential spread can be efficiently prevented by reducing non-local contacts. Eliminating the disease remains costly as it requires to reduce all contacts. Our code is freely available and can be readily adapted to any country .
Countries around the world implement nonpharmaceutical interventions (NPIs) to mitigate the spread of COVID-19 . Design of efficient NPIs requires identification of the structure of the disease transmission network. We here identify the key parameters of the COVID-19 transmission network for time periods before, during, and after the application of strict NPIs for the first wave of COVID-19 infections in Germany combining Bayesian parameter inference with an agent-based epidemiological model. We assume a Watts-Strogatz small-world network which allows to distinguish contacts within clustered cliques and unclustered, random contacts in the population, which have been shown to be crucial in sustaining the epidemic. In contrast to other works, which use coarse-grained network structures from anonymized data, like cell phone data, we consider the contacts of individual agents explicitly. We show that NPIs drastically reduced random contacts in the transmission network, increased network clustering, and resulted in a change from an exponential to a constant regime of newcases. In this regime, the disease spreads like a wave with a finite wave speed that depends on the number of contacts in a nonlinear fashion, which we can predict by mean field theory. Our analysis indicates that besides the well-known transitionbetween exponential increase and exponential decrease in the number of new cases, NPIs can induce a transition to another, previously unappreciated regime of constant new cases .
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implemented non-pharmaceutical
implement nonpharmaceutical
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flatten the curve
mitigate the spread
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cases but failed to eradicate the disease
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underlying
disease
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combine
here identify the key parameters of the COVID-19 transmission network for time periods before, during, and after the application of strict NPIs for the first wave of COVID-19 infections in Germany combining
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a network-based epidemiological modelthat allows to interpolate between random and
an agent-based epidemiological model. We assume a Watts-Strogatz
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transmission networks. Using epidemiological data from Germany, we
network which allows to distinguish contacts within clustered cliques and unclustered, random contacts in the population, which have been shown to be crucial in sustaining the epidemic. In contrast to other works, which use coarse-grained network structures from anonymized data, like cell phone data, we consider the contacts of individual agents explicitly. We
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reduced non-local
drastically reduced random
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resulting
increased network clustering, and resulted
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. Due to the small-world nature of the inferred network, exponential spread can be efficiently prevented by reducing non-local contacts. Eliminating the disease remains costly as it requires to reduce all contacts. Our code is freely available and can be readily adapted to any country
of newcases. In this regime, the disease spreads like a wave with a finite wave speed that depends on the number of contacts in a nonlinear fashion, which we can predict by mean field theory. Our analysis indicates that besides the well-known transitionbetween exponential increase and exponential decrease in the number of new cases, NPIs can induce a transition to another, previously unappreciated regime of constant new cases