Predicted Infection Risk for Aerosol Transmission of SARS-CoV-2

Kriegel, Martin; Buchholz, Udo; Gastmeier, Petra; Bischoff, Philip; Abdel-Gawad, Ibrahim; Hartmann, Anne

doi:10.1101/2020.10.08.20209106

Cited by 44 publications

(43 citation statements)

References 32 publications

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“…Small airborne particles and aerosols (diameter < 5 µm) can remain up to several hours in the air [43]. So far, most COVID-19 risk assessments for indoor environment are based on calculations applying spreadsheet models including relevant environmental and physiological parameters [8,44,45]. Typical standard settings represent classrooms, office spaces or receptions with space volume < 400 m 3 and less than 50 persons present.…”

Section: Discussionmentioning

confidence: 99%

Experimental Investigation of Aerosol and CO2 Dispersion for Evaluation of COVID-19 Infection Risk in a Concert Hall

Schade

Reimer

Seipenbusch³

et al. 2021

IJERPH

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The dispersion of small aerosols in a concert hall is experimentally studied for estimating the risk of infection with SARS-CoV-2 during a concert. A mannequin was modified to emit an air stream containing aerosols and CO2. The aerosols have a size distribution with a peak diameter (δ) close to 0.3 µm and a horizontal initial particle velocity (vp,x ) of 2.4 m/s. The CO2 -concentration (c) emitted simultaneously is 7500 ppm. It is investigated, if the spatial dissipation of aerosols and CO2 can be correlated. This would allow the use of technically easier CO2 measurements to monitor compliance with aerosol concentration limits. Both aerosol and CO2 concentrations are mapped by different sensors placed around the mannequin. As a result, no significant enrichment of aerosols and CO2 was obtained outside a radius of 1.5 m when the fresh air ventilation in the concert hall has a steady vertical flow with a velocity of vg,z= 0.05 m/s and the installed ventilation system was operating at an air change rate per hour (ACH) of 3, corresponding to an air exchange rate of 51,000 m3/h. A Pearson correlation coefficient of 0.77 was obtained for CO2 and aerosol concentrations measured simultaneously at different positions within the concert hall.

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Section: Discussionmentioning

confidence: 99%

Experimental Investigation of Aerosol and CO2 Dispersion for Evaluation of COVID-19 Infection Risk in a Concert Hall

Schade

Reimer

Seipenbusch³

et al. 2021

IJERPH

View full text Add to dashboard Cite

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“…For γ and the maximum tracing capacity, we compared simulation results. 5 Changes in γ make relatively little difference. For the maximum tracing capacity, one can see that larger capacities would have kept the new infections under control for longer than what happened in reality.…”

Section: Masks and Contact Tracingmentioning

confidence: 99%

“…Overall, the above results in v = fs · h · ae is the air volume flow per person at capacity, which is also used by other models as the decisive quantity to decide about the relatively safety of rooms [5].…”

Section: Model Detailsmentioning

confidence: 99%

“…Once they went down, we used that as a data feed for our model to reduce the number of out-of-home activities accordingly. The model was then improved to include the effect of aerosol transmission [4], by taking facility size and air exchange rates into account, different for each activity type [5]. We also included lower susceptibility and infectiousness of children as was by then reported in the literature [6, 7].…”

Section: Introductionmentioning

confidence: 99%

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Predicting the effects of COVID-19 related interventions in urban settings by combining activity-based modelling, agent-based simulation, and mobile phone data

Müller

Balmer²,

Charlton

et al. 2021

Preprint

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Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19.This paper presents an approach that combines a well-established approach from transportation modelling that uses person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. The model includes the consequences of different room sizes, air exchange rates, disease import, changed activity participation rates over time (coming from mobility data), masks, indoors vs. outdoors leisure activities, and of contact tracing. The model is validated against the infection dynamics in Berlin (Germany).The model can be used to understand the contributions of different activity types to the infection dynamics over time. The model predicts the effects of contact reductions, school closures/vacations, masks, or the effect of moving leisure activities from outdoors to indoors in fall, and is thus able to quantitatively predict the consequences of interventions. It is shown that these effects are best given as additive changes of the reinfection rate R. The model also explains why contact reductions have decreasing marginal returns, i.e. the first 50% of contact reductions have considerably more effect than the second 50%.Our work shows that is is possible to build detailed epidemiological simulations from microscopic mobility models relatively quickly. They can be used to investigate mechanical aspects of the dynamics, such as the transmission from political decisions via human behavior to infections, consequences of different lockdown measures, or consequences of wearing masks in certain situations. The results can be used to inform political decisions.Author summaryEvidently, there is an interest in models that are able to predict the effect of interventions in the face of pandemic diseases. The so-called compartmental models have difficulties to include effects that stem from spatial, demographic or temporal inhomongeneities. Person-centric models, often using social contact matrices, are difficult and time-consuming to build up. In the present paper, we describe how we built a largely data-driven person-centric infection model within less than a month when COVID-19 took hold in Germany. The model is based on our extensive experience with mobility modelling, and a synthetic data pipeline that starts with mobile phone data, while taking the infection dynamics and the disease progression from the literature. The approach makes the model portable to all places that have similar so-called activity-based models of travel in place, which are many places world-wide, and the number is continuously increasing. The model has been used since its inception to regularly advise the German government on expected consequences of interventions.

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“…A quantum unit is defined as the number of infectious particles required to cause 63% of people getting infected, and is usually back-calculated using data on numbers of individuals infected in well-defined indoor outbreaks. The Well-Riley model has been extensively used for predicting the number of infections expected to arise at large gatherings and assessing the effect of interventions such as capping the maximum of occupancy and reducing the total event duration (Bazant & Bush (2020), Kriegel et al (2020)). It is often used for quick risk assessments of large events.…”

Section: Modelling Indoor Transmission Of Respiratory Virusesmentioning

confidence: 99%

The multi-dimensional challenges of controlling respiratory virus transmission in indoor spaces: Insights from the linkage of a microscopic pedestrian simulation and SARS-CoV-2 transmission model

Duives

Chang

Sparnaaij

et al. 2021

Preprint

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Since its introduction in December of 2019, SARS-CoV-2, the virus that causes COVID-19 disease, has rapidly spread across the world. Whilst vaccines are being rolled out, non-pharmaceutical interventions remain the most important tools for mitigating the spread of SARS-CoV-2. Quantifying the impact of these measures as well as determining what settings are prone to instigating (super)spreading events is important for informed and safe reopening of spaces and the targeting of interventions. Mathematical models can help decipher the complex interactions that underlie virus transmission. Currently, most mathematical models developed during the COVID-19 epidemic evaluate interventions at national or subnational levels. Smaller scales of transmission, such as at the level of indoor spaces, have received less attention, despite the central role they play in both transmission and control. Models that do act on this scale use simplified descriptions of human behavior, impeding a valid quantitative analysis of the impact of interventions on transmission in indoor spaces, particularly those that aim for physical distancing. To more accurately predict the transmission of SARS-CoV-2 through a pedestrian environment, we introduce a model that links pedestrian movement and choice dynamics with SARS-CoV-2 spreading models. The objective of this paper is to investigate the spread of SARS-CoV-2 in indoor spaces as it arises from human interactions and assess the relative impact of non-pharmaceutical interventions thereon. We developed a world-wide unique combined Pedestrian Dynamics - Virus Spread model (PeDViS model), which combines insights from pedestrian modelling, epidemiology, and IT-design. In particular, an expert-driven activity assignment model is coupled with the microscopic simulation model (Nomad) and a virus spread model (QVEmod). We first describe the non-linear relationships between the risks of exposure to the virus and the duration, distance, and context of human interactions. We compared virus exposure relative to a benchmark contact (1.5meters for 15 minutes): a threshold often used by public health agencies to determine at risk contacts. We discuss circumstances under which individuals that adhere to common distancing measures may nevertheless be at risk. Specifically, we illustrate the stark increase in exposure at shorter distances, as well as longer contact durations. These risks increase when the infected individual was present in the space before the interaction occurred, as a result of buildup of virus in the environment. The latter is particularly true in poorly ventilated spaces and highlights the importance of good ventilation to prevent potential virus exposure through indirect transmission routes. Combining intervention tools that target different routes of transmission can aid in accumulating impact. We use face masks as an example, which are particularly effective at reducing virus spread that is not affected by ventilation. We then demonstrate the use of PeDViS using a simple restaurant case study, focussing on transmission between guests. In this setting the exposure risk to individuals that are not seated at the same table is limited, but guests seated at nearby tables are estimated to experience exposure risks that surpass that of the benchmark contact. These risks are larger in low ventilation scenarios. Lastly, we illustrate that the impact of intervention measures on the number of new infections heavily depends on the relative efficiency of the direct and indirect transmission routes considered. This uncertainty should be considered when assessing the risks of transmission upon different types of human interactions in indoor spaces. The PeDViS case study shows the multi-dimensionality of SARS-CoV-2 that emerges from the interplay of human behaviour and the spread of respiratory viruses in indoor spaces. A modelling strategy that incorporates this in risk assessments can be an important tool to inform policy makers and citizens. It can empower them to make better design and policy decisions pertaining to the most effective use of measures to limit the spread of SARS-CoV-2 and safely open up indoor spaces.

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Predicted Infection Risk for Aerosol Transmission of SARS-CoV-2

Cited by 44 publications

References 32 publications

Experimental Investigation of Aerosol and CO2 Dispersion for Evaluation of COVID-19 Infection Risk in a Concert Hall

Experimental Investigation of Aerosol and CO2 Dispersion for Evaluation of COVID-19 Infection Risk in a Concert Hall

Predicting the effects of COVID-19 related interventions in urban settings by combining activity-based modelling, agent-based simulation, and mobile phone data

The multi-dimensional challenges of controlling respiratory virus transmission in indoor spaces: Insights from the linkage of a microscopic pedestrian simulation and SARS-CoV-2 transmission model

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