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

Bicher, Martin; Rippinger, Claire; Urach, Christoph; Brunmeir, Dominik; Siebert, Uwe; Popper, Niki

doi:10.1177/0272989x211013306

Cited by 23 publications

(32 citation statements)

References 17 publications

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“…A downside of ABMs is that they are comparatively complex to code, are often not very parsimonious, and can be very computationally intensive to run, limiting the ability to explore a wide range of parameter combinations. ABMs have been used throughout the COVID19 pandemic to inform the public health response [9][10][11][12][13][14]. Here, we focus on developing OpenABM-Covid19, an agent-based simulation which addresses these downsides, by focussing on parsimony, computational efficiency, code transparency, and a robust testing framework.…”

Section: Introductionmentioning

confidence: 99%

“…The observed hospitalisation data for Fig 5 are from UK govt coronavirus dashboard available here: https://coronavirus.data.gov.uk/ The seroprevalence data for Fig 5 are from ONS: https://www.ons.gov.uk/ peoplepopulationandcommunity/ healthandsocialcare/conditionsanddiseases/ articles/ apps that record proximity events, can contribute to epidemic control [15]. Several other groups have approached this problem with similar ABMs [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…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 [ 15 ]. Several other groups have approached this problem with similar ABMs [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with computational models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing, and vaccination programmes. It can simulate a population of 1 million people in seconds per day, allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 are its Python and R interfaces, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…For both tasks, we apply an agent-based model (ABM) in combination with data from performed cross-sectional SARS-CoV-2 prevalence and serological tests in Austria. This ABM has already been used for several scientific studies such as the investigation of tracing measures 2 or the general dynamics of undetected cases 3 , and is one of three models that participate in the official Austrian COVID forecasting panel by the ministry of health (COVID Prognose Konsortium 4 ), which publishes short time forecasts on a weekly basis. For the herd effect, we will also compare the results from the ABM with analytical results from a classic differential equation based SIR-model.…”

Section: Introductionmentioning

confidence: 99%

Model based estimation of the SARS-CoV-2 immunization level in austria and consequences for herd immunity effects

Bicher

Rippinger

Schneckenreither

et al. 2022

Sci Rep

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Several systemic factors indicate that worldwide herd immunity against COVID-19 will probably not be achieved in 2021. On the one hand, vaccination programs are limited by availability of doses and on the other hand, the number of people already infected is still too low to have a disease preventing impact and new emerging variants of the virus seem to partially neglect developed antibodies from previous infections. Nevertheless, by February 2021 after one year of observing high numbers of reported COVID-19 cases in most European countries, we might expect that the immunization level should have an impact on the spread of SARS-CoV-2. Here we present an approach for estimating the immunization of the Austrian population and discuss potential consequences on herd immunity effects. To estimate immunization we use a calibrated agent-based simulation model that reproduces the actual COVID-19 pandemic in Austria. From the resulting synthetic individual-based data we can extract the number of immunized persons. We then extrapolate the progression of the epidemic by varying the obtained level of immunization in simulations of an hypothetical uncontrolled epidemic wave indicating potential effects on the effective reproduction number. We compared our theoretical findings with results derived from a classic differential equation SIR-model. As of February 2021, $$14.7\%$$ 14.7 % of the Austrian population has been affected by a SARS-CoV-2 infection which causes a $$9\%$$ 9 % reduction of the effective reproduction number and a $$24.7\%$$ 24.7 % reduction of the prevalence peak compared to a fully susceptible population. This estimation is now recomputed on a regular basis to publish model based analysis of immunization level in Austria also including the fast growing effects of vaccination programs. This provides substantial information for decision makers to evaluate the necessity of non pharmaceutical intervention measures based on the estimated impact of natural and vaccinated immunization.

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“…The effectiveness of the method for decision support was described in [ 4 ]. In January 2020 our group developed an agent-based COVID-19 epidemic model [ 5 , 6 ], which is still in heavy use for counseling of political decision makers in Austria [ 7 ]. The present question revealed an interesting new chance for us.…”

Section: Methodsmentioning

confidence: 99%

How an election can be safely planned and conducted during a pandemic: Decision support based on a discrete event model

Weibrecht

Rößler²,

Bicher

et al. 2021

PLoS ONE

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In 2020, the ongoing COVID-19 pandemic caused major limitations for any aspect of social life and in specific for all events that require a gathering of people. While most events of this kind can be postponed or cancelled, democratic elections are key elements of any democratic regime and should be upheld if at all possible. Consequently, proper planning is required to establish the highest possible level of safety to both voters and scrutineers. In this paper, we present the novel and innovative way how the municipal council and district council elections in Vienna were planned and conducted using an discrete event simulation model. Key target of this process was to avoid queues in front of polling stations to reduce the risk of related infection clusters. In cooperation with a hygiene expert, we defined necessary precautions that should be met during the election in order to avoid the spread of COVID-19. In a next step, a simulation model was established and parametrized and validated using data from previous elections. Furthermore, the planned conditions were simulated to see whether excessive queues in front of any polling stations could form, as these could on the one hand act as an infection herd, and on the other hand, turn voters away. Our simulation identified some polling stations where long queues could emerge. However, splitting up these electoral branches resulted in a smooth election across all of Vienna. Looking back, the election did not lead to a significant increase of COVID-19 incidences. Therefore, it can be concluded that careful planning led to a safe election, despite the pandemic.

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

Cited by 23 publications

References 17 publications

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

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

Model based estimation of the SARS-CoV-2 immunization level in austria and consequences for herd immunity effects

How an election can be safely planned and conducted during a pandemic: Decision support based on a discrete event model

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