Dynamic multiplex social network models on multiple time scales for simulating contact formation and patterns in epidemic spread

Schneckenreither, Günter; Popper, Niki

doi:10.1109/wsc.2017.8248138

Cited by 10 publications

(14 citation statements)

References 24 publications

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“…Our method to evaluate the total number of people who have been in contact with SARS-CoV-2 differs from other approaches using estimates of the infection fatality rate [1] and resulting in an estimated immunization level of 7% for Austria in February 2021. Moreover, our method to quantify the effect of this immunization level is vastly different to methods applied for other infectious diseases that rely on observational studies performed before and after vaccination of a large portion of the population [12] [13].…”

Section: Discussionmentioning

confidence: 99%

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Model Based Estimation of the SARS-CoV-2 Immunization Level in Austria and Consequences for Herd Immunity Effects

Bicher

Rippinger

Schneckenreither

et al. 2021

Preprint

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Several systemic factors indicate, that worldwide herd immunity against COVID-19 will probably not be achieved in 2021. Vaccination programs are limited by availability of doses, 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, after one year of COVID-19 observing high numbers of reported cases in most European countries, we might expect that the immunization level should have an impact on the spread of SARS-CoV-2. We used an agent-based simulation model to reproduce the COVID-19 pandemic in Austria to estimate the immunization level of the population as of February 2021. We ran several simulations of an uncontrolled epidemic wave with varying initial immunization scenarios to assess the effect on the effective reproduction number. We also used a classic differential equation SIR-model to cross-validate the simulation model. As of February 2021, 14.7% of the Austrian population has been affected by a SARS-CoV-2 infection which causes a 9% reduction of the effective reproduction number and a 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 NPI-measures based on the estimated impact of natural and vaccinated immunization.

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Section: Discussionmentioning

confidence: 99%

“…In this way, regional and household clusters from the original simulation are preserved and the spread is more realistic. This approach allows to asses the impact of heterogeneous immunization on our results (compare [11]).…”

Section: Herd Effect Estimation Using An Agent-based Approachmentioning

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. 2021

Preprint

Self Cite

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“…By successive improvement in the simulation of physical contact behavior and disease transmission, we hope to increase the insight into actual transmission paths and in the occurrence of infection clusters (Leclerc et al 2020). In particular, by interlacing of statistical information on social relations and social structuring (Schneckenreither and Popper 2017) and on geographic mobility patterns (Heiler et al 2020) we infer and dynamically reproduce transmission trajectories as observed or anticipated in reality. We intend to provide synthetic data on transmission clusters in combination with geographic and socio-structural information in the future.…”

Section: Results and Evaluationmentioning

confidence: 94%

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

Popper

Zechmeister²,

Brunmeir³

et al. 2020

Preprint

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We generate synthetic data documenting COVID-19 cases in Austria by the means of an agent-based simulation model. The model simulates the transmission of the SARS-CoV-2 virus in a statistical replica of the population and reproduces typical patient pathways on an individual basis while simultaneously integrating historical data on the implementation and expiration of population-wide countermeasures. The resulting data semantically and statistically aligns with an official epidemiological case reporting data set and provides an easily accessible, consistent and augmented alternative. Our synthetic data set provides additional insight into the spread of the epidemic by synthesizing information that cannot be recorded in reality.

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“…We use an agent-based model that was previously developed to simulate the spread of COVID-19 in Austria (M. R. Bicher et al 2020;dwh GmbH 2020). The technical implementation of our model aligns with the general approach in agent-based infection models found in literature (Chang et al 2020;Cuevas 2020;Karatayev, Anand, and Bauch 2020;Mahmood et al 2020;Meyer 2015;Miksch et al 2014;Schneckenreither and Popper 2017;Silva et al 2020) and is based on the combination of a population model with a model for face-to-face encounters and disease transmission.…”

Section: Individual-based Simulation Modelmentioning

confidence: 99%

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

Popper

Zechmeister

Brunmeir

et al. 2021

Data Science Journal

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Dynamic multiplex social network models on multiple time scales for simulating contact formation and patterns in epidemic spread

Cited by 10 publications

References 24 publications

Model Based Estimation of the SARS-CoV-2 Immunization Level in Austria and Consequences for Herd Immunity Effects

Model Based Estimation of the SARS-CoV-2 Immunization Level in Austria and Consequences for Herd Immunity Effects

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

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

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