Evaluation of Contact-Tracing Policies Against the Spread of SARS-CoV-2 in Austria – An Agent-Based Simulation

Abstract

The decline of active COVID-19 cases in many countries in the world has proved that lockdown policies are indeed a very effective measure to stop the exponential spread of the virus. Still, the danger of a second wave of infections is omnipresent and it is clear, that every policy of the lockdown has to be carefully evaluated and possibly replaced by a different, less restrictive policy, before it can be lifted. Tracing of contacts and consequential tracing and breaking of infection-chains is a promising and comparably straightforward strategy to help containing the disease, although its precise impact on the epidemic is unknown. In order to quantify the benefits of tracing and similar policies we developed an agent-based model that not only validly depicts the spread of the disease, but allows for exploratory analysis of containment policies. We will describe our model and perform case studies in which we use the model to quantify impact of contact tracing in different characteristics and draw valuable conclusions about contact tracing policies in general.

OpenABM-Covid19 - an agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing

“…A particular focus of our work applying OpenABM-Covid19 has been exploring different ways in which contact tracing, and in particular digital contact tracing using mobile phone apps that record proximity events, can contribute to epidemic control [9] . Several other groups have approached this problem with similar agent based models [10,11] ; compared to those, our model places more emphasis on simulating larger populations, computational efficiency, and on code generalisability that allows other researchers to use and develop the code.…”

Synthetic Reproduction and Augmentation of COVID-19 Case Reporting Data by Agent-Based Simulation

“…Our data statistically reflects the figures in official infectious disease reporting and is further augmented with additional information on SARS-CoV-2 infections that are not detected in reality. The synthetic data is generated from an agent-based simulation model (M. R. Bicher et al 2020;M. Bicher, Urach, and Popper 2018;dwh GmbH 2020) that is carefully parameterized and calibrated based on additional data sources and expert knowledge.…”

Assessment of effective mitigation and prediction of the spread of SARS-CoV-2 in Germany using demographic information and spatial resolution

“…The equations (2)- (9) represent the transition of people from one state to another. Note that people in an age group Z i cannot transit to another group Z j for i = j.…”

“…These considerations are summarized in Figure 3. (9). First, we evolve County A and County B separately, resulting in updated compartments in both counties.…”