Evaluation of Undetected Cases During the COVID-19 Epidemic in Austria

Rippinger, Claire; Bicher, Martin; Urach, Christoph; Brunmeir, Dominik; Zauner, Günther; Sroczynski, Gabi; Jahn, Beate; Mühlberger, Nikolai; Siebert, Uwe; Popper, Niki

doi:10.2139/ssrn.3689596

Cited by 8 publications

(14 citation statements)

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“…Furthermore, for all scenarios, the timeline of the new detected cases is delayed by about 9 days compared to the timeline of new infections. This delay occurs because cases are detected about 9 days after their infection as the mean incubation time is 5.1 days and the mean reaction time between symptom onset and positive test results is 3.8 days [3]. The detected cases are also reduced to 35% of all cases which corresponds to the detection probability applied for these scenarios.…”

Section: Resultsmentioning

confidence: 99%

“…For the estimation of the immunization level, we ran our simulation model for the time span between February 2020 and February 2021. We identified the parameter for the detection rate based on study data and the method published in [3].…”

Section: Methodsmentioning

confidence: 99%

“…In specific, applying the method introduced in [3], the results of a prevalence study (SORA [6]) conducted in April 2020 result in a detection probability of 13% (CI 95%: [2%,86%]) [3]. The second study (Statistics Austria [7]), performed during the second wave of COVID-19, indicates an increase of the detection probability to 35% (CI 95%: [24%, 58%]).…”

Section: Methodsmentioning

confidence: 99%

“…To guarantee that the detected cases in the model match the officially reported case numbers, we proceeded analogous to [2] and [3] and calibrated the infection probability parameter as well as all parameters corresponding to governmental policies for this time period using an iterative bisection method. Hereby, the match of the cumulative detected cases and the officially reported numbers for the positive tests is the calibration target.…”

Section: Methodsmentioning

confidence: 99%

“…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 perform cross model validation of the ABM with analytical results from a classic differential equation based SIR-model.…”

Section: Introductionmentioning

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: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

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

Self Cite

View full text Add to dashboard Cite

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Data modelling recipes for SARS-CoV-2 wastewater-based epidemiology

Rauch¹,

Schenk²,

Insam³

et al. 2022

Environmental Research

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Supporting COVID-19 policy-making with a predictive epidemiological multi-model warning system

et al. 2022

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Background In response to the SARS-CoV-2 pandemic, the Austrian governmental crisis unit commissioned a forecast consortium with regularly projections of case numbers and demand for hospital beds. The goal was to assess how likely Austrian ICUs would become overburdened with COVID-19 patients in the upcoming weeks. Methods We consolidated the output of three epidemiological models (ranging from agent-based micro simulation to parsimonious compartmental models) and published weekly short-term forecasts for the number of confirmed cases as well as estimates and upper bounds for the required hospital beds. Results We report on three key contributions by which our forecasting and reporting system has helped shaping Austria’s policy to navigate the crisis, namely (i) when and where case numbers and bed occupancy are expected to peak during multiple waves, (ii) whether to ease or strengthen non-pharmaceutical intervention in response to changing incidences, and (iii) how to provide hospital managers guidance to plan health-care capacities. Conclusions Complex mathematical epidemiological models play an important role in guiding governmental responses during pandemic crises, in particular when they are used as a monitoring system to detect epidemiological change points.

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Evaluation of Undetected Cases During the COVID-19 Epidemic in Austria

Cited by 8 publications

References 0 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

Data modelling recipes for SARS-CoV-2 wastewater-based epidemiology

Supporting COVID-19 policy-making with a predictive epidemiological multi-model warning system

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