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

Popper, Niki; Zechmeister, Melanie; Brunmeir, Dominik; Rippinger, Claire; Weibrecht, Nadine; Urach, Christoph; Bicher, Martin; Schneckenreither, Günter; Rauber, Andreas

doi:10.5334/dsj-2021-016

Cited by 7 publications

(7 citation statements)

References 27 publications

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“…The EffDI complements existing indicators (e.g., effective reproduction factor) which are used for assessing and anticipating the dynamics of epidemic outbreaks. Furthermore, the separation and indication of periods with different spreading characteristics could also improve individual-based [ 4 , 8 , 9 , 44 ] and aggregate models for the simulation and forecasting of epidemics and could provide a reference for combining or switching between different modeling approaches. We also assume that providing indications about the time-varying progression of heterogeneity in epidemic outbreaks could contribute to the general perception of epidemic spreading as complex dynamics on multiple scales.…”

Section: Discussionmentioning

confidence: 99%

Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data

et al. 2023

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Structural features and the heterogeneity of disease transmissions play an essential role in the dynamics of epidemic spread. But these aspects can not completely be assessed from aggregate data or macroscopic indicators such as the effective reproduction number. We propose in this paper an index of effective aggregate dispersion (EffDI) that indicates the significance of infection clusters and superspreading events in the progression of outbreaks by carefully measuring the level of relative stochasticity in time series of reported case numbers using a specially crafted statistical model for reproduction. This allows to detect potential transitions from predominantly clustered spreading to a diffusive regime with diminishing significance of singular clusters, which can be a decisive turning point in the progression of outbreaks and relevant in the planning of containment measures. We evaluate EffDI for SARS-CoV-2 case data in different countries and compare the results with a quantifier for the socio-demographic heterogeneity in disease transmissions in a case study to substantiate that EffDI qualifies as a measure for the heterogeneity in transmission dynamics.

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Section: Discussionmentioning

confidence: 99%

Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data

et al. 2023

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“…The EffDI complements existing indicators (e.g., effective reproduction factor) which are used for assessing and anticipating the dynamics of epidemic outbreaks. Furthermore, the separation and indication of periods with different spreading characteristics could also improve individual-based [8, 41, 44, 47] and aggregate models for the simulation and forecasting of epidemics and could provide a reference for combining or switching between different modeling approaches. We also assume that providing indications about the time-varying progression of heterogeneity in epidemic outbreaks could contribute to the general perception of epidemic spreading as complex dynamics on multiple scales.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, our framework does currently not regard truncation or other aspects that should be considered in the analysis of time series data. Besides technical improvements, we assume that investigating effective aggregate dispersion with synthetic data that was obtained from individual-based or network models [8, 41, 44, 46, 47, 50, 51] could provide additional insight and improvements.…”

Section: Discussionmentioning

confidence: 99%

Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data

Schneckenreither

Herrmann

Reisenhofer

et al. 2022

Preprint

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Structural features and the heterogeneity of disease transmissions play an essential role in the dynamics of epidemic spread. But these aspects can not completely be assessed from aggregate data or macroscopic indicators such as the effective reproduction number. We propose an index of effective aggregate dispersion (EffDI) that indicates the significance of infection clusters and superspreading events in the progression of outbreaks by carefully measuring the level of stochasticity in time series of reported case numbers. This allows to detect the transition from predominantly clustered spreading to a diffusive regime with diminishing significance of singular clusters, which can be decisive for the future progression of outbreaks and relevant in the planning of containment measures. We evaluate EffDI for SARS-CoV-2 case data in different countries and compare the results with a quantifier for the socio-demographic heterogeneity in disease transmissions in a case study to substantiate that EffDI qualifies as a measure for the heterogeneity in transmission dynamics.

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“…We use an agent-based model mainly developed in spring 2020 to simulate the spread of SARS-CoV-2 in Austria. This model has been subject to multiple studies (see [ 11 , 12 ]) and is actively used as a decision support tool for the Austrian COVID-19 containment policies [ 13 ].…”

Section: Methodsmentioning

confidence: 99%

An iterative algorithm for optimizing COVID-19 vaccination strategies considering unknown supply

Bicher

Rippinger²,

Zechmeister³

et al. 2022

PLoS ONE

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Background and objective The distribution of the newly developed vaccines presents a great challenge in the ongoing SARS-CoV-2 pandemic. Policy makers must decide which subgroups should be vaccinated first to minimize the negative consequences of the pandemic. These decisions must be made upfront and under uncertainty regarding the amount of vaccine doses available at a given time. The objective of the present work was to develop an iterative optimization algorithm, which provides a prioritization order of predefined subgroups. The results of this algorithm should be optimal but also robust with respect to potentially limited vaccine supply. Methods We present an optimization meta-heuristic which can be used in a classic simulation-optimization setting with a simulation model in a feedback loop. The meta-heuristic can be applied in combination with any epidemiological simulation model capable of depicting the effects of vaccine distribution to the modeled population, accepts a vaccine prioritization plan in a certain notation as input, and generates decision making relevant variables such as COVID-19 caused deaths or hospitalizations as output. We finally demonstrate the mechanics of the algorithm presenting the results of a case study performed with an epidemiological agent-based model. Results We show that the developed method generates a highly robust vaccination prioritization plan which is proven to fulfill an elegant supremacy criterion: the plan is equally optimal for any quantity of vaccine doses available. The algorithm was tested on a case study in the Austrian context and it generated a vaccination plan prioritization favoring individuals age 65+, followed by vulnerable groups, to minimize COVID-19 related burden. Discussion The results of the case study coincide with the international policy recommendations which strengthen the applicability of the approach. We conclude that the path-dependent optimum optimum provided by the algorithm is well suited for real world applications, in which decision makers need to develop strategies upfront under high levels of uncertainty.

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

Cited by 7 publications

References 27 publications

Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data

Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data

Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data

An iterative algorithm for optimizing COVID-19 vaccination strategies considering unknown supply

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