Inferring the effect of interventions on COVID-19 transmission networks

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Abstract

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 previously unappreciated transition from an exponential to a constant regime of new cases. 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.

Details

OriginalspracheEnglisch
Aufsatznummer21913
FachzeitschriftScientific Reports
Jahrgang11
Ausgabenummer1
PublikationsstatusVeröffentlicht - Dez. 2021
Peer-Review-StatusJa

Externe IDs

Scopus 85118653497
ORCID /0000-0001-9955-9012/work/142239122
ORCID /0000-0002-1270-7885/work/142250321
PubMed 34754025

Schlagworte

Schlagwörter

  • Covid-19, transmission networks, nonpharmaceutical interventions (NPIs)