Inferring the effect of interventions on COVID-19 transmission networks

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.

Optimal vaccine roll-out strategies including social distancing for pandemics

“…To study the SARS-CoV-2 epidemic dynamics, a discrete agent-based model on the complex network has been developed, extending (Syga et al, 2021), see Figure 2A. Each node in the network represents an individual agent with undirected edges as social contacts, see Figure 2B.…”

“…After being exposed, the agents change their states from susceptible to exposed and a waiting time t E (waiting time before the agent becomes infectious) is assigned which is drawn from a G distribution (Syga et al, 2021;Linton et al, 2020) that is, t E $ Gða E ;b E Þ, where a E ; b E denote the parameters of the G distribution. In each time step, the waiting time t E is reduced by 1, that is, t E = t E À 1 and t E > 0.…”

“…The parameters of G-distributed waiting times were inferred from COVID-19 disease characteristics, as these have been found to fit optimally (Syga et al, 2021;Linton et al, 2020) the real waiting times. Specifically for the exposed state t E $ Gða E ; b E Þ and t I $ Gða I ; b I Þ, where a E = 9; b E = 1=3 and a I = 100=3;b I = 3=10.…”

Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany

“…Agent-based models (ABMs) have been used by many authors since they model infection dynamics in a natural way [ 40 – 48 ]. Another ABM based on a traffic simulation and mobile phone data was proposed by [ 49 ] and [ 50 , 51 ] presented agent-based models which build upon a predefined contact networks. While ABMs do not have the limitations of the homogeneous mixing assumption or the lack of stochastic events, their use comes at a huge computational overhead.…”

Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany

“…Agent-based models (ABMs) have been used by many authors since they model infection dynamics in a natural way [40, 41, 42, 43, 44, 45, 46, 47, 48]. Another ABM based on a traffic simulation and mobile phone data was proposed by [49] and [50, 51] presented agent-based models which build upon a predefined contact networks. While ABMs do not have the limitations of the homogeneous mixing assumption or the lack of stochastic events, their use comes at a huge computational overhead.…”