Age-specific contribution of contacts to transmission of SARS-CoV-2 in Germany

Rodiah, Isti; Vanella, Patrizio; Kuhlmann, Alexander; Jaeger, Veronika K.; Harries, Manuela; Krause, Gérard; Karch, André; Böck, Wolfgang; Lange, Berit

doi:10.1007/s10654-022-00938-6

Cited by 13 publications

(10 citation statements)

References 51 publications

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“…The main analysis omits the age group of children under 15 years as only 2.7% of cases, and a single death was observed in this group in the study period until May 2020. Later studies confirmed that the transmission activity in children was particularly low in the first wave in Germany: Seroprevalence surveys 45 found children 1 to 10 years old had particularly low levels of antibodies in measures taken in May 2020, and a transmission study using age‐specific contacts 46 found children to have little contribution in the first wave in Germany.…”

Section: Methodsmentioning

confidence: 97%

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Inference under superspreading: Determinants of SARS‐CoV‐2 transmission in Germany

Schmidt

2024

Statistics in Medicine

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Superspreading, under‐reporting, reporting delay, and confounding complicate statistical inference on determinants of disease transmission. A model that accounts for these factors within a Bayesian framework is estimated using German Covid‐19 surveillance data. Compartments based on date of symptom onset, location, and age group allow to identify age‐specific changes in transmission, adjusting for weather, reported prevalence, and testing and tracing. Several factors were associated with a reduction in transmission: public awareness rising, information on local prevalence, testing and tracing, high temperature, stay‐at‐home orders, and restaurant closures. However, substantial uncertainty remains for other interventions including school closures and mandatory face coverings. The challenge of disentangling the effects of different determinants is discussed and examined through a simulation study. On a broader perspective, the study illustrates the potential of surveillance data with demographic information and date of symptom onset to improve inference in the presence of under‐reporting and reporting delay.

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Section: Methodsmentioning

confidence: 97%

“…Recently, contact matrices for Germany during the first wave were used to model transmission 46 . A simulation study in Section 5 uses those contact matrices to investigate the model's performance in the presence of age importation.…”

Section: Methodsmentioning

confidence: 99%

Inference under superspreading: Determinants of SARS‐CoV‐2 transmission in Germany

Schmidt

2024

Statistics in Medicine

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“…The preliminary age-group related OD matrices have been published freely in [28]. Note that in this study age group related traffic has been recomputed by (10).…”

Section: Mobility and Contact Patternsmentioning

confidence: 99%

“…Over the last years, to predict SARS-CoV-2 development in Germany, contributions have been made by a variety of different approaches. Among these approaches are agent-based models [6,7], models based on ordinary differential equations (ODE) [8][9][10] or delay differential equations [11], advanced ODE-based models [12] using the linear chain trick [13] to waive the implicit assumption of exponentially distributed transition times, and in particular metapopulation or graph-ODE models [14].…”

Section: Introductionmentioning

confidence: 99%

Novel travel time aware metapopulation models and multi-layer waning immunity for late-phase epidemic and endemic scenarios

Zunker,

Schmieding,

Kerkmann

et al. 2024

Preprint

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In the realm of infectious disease control, accurate modeling of the transmission dynamics is pivotal. As human mobility and commuting patterns are key components of communicable disease spread, we introduce a novel travel time aware metapopulation model. Our model aims to enhance estimations of disease transmission. By providing more reliable assessments on the efficacy of interventions, curtailing personal rights or human mobility behavior through interventions can be minimized. The proposed model is an advancement over traditional compartmental models, integrating explicit transmission on travel and commute, a factor available in agent-based models but often neglected with metapopulation models.Our approach employs a multi-edge graph ODE-based (Graph-ODE) model, which represents the intricate interplay between mobility and disease spread. This granular modeling is particularly important when assessing the dynamics in densely connected urban areas or when heterogeneous structures across entire countries have to be assessed. The given approach can be coupled with any kind of ODE-based model.In addition, we propose a novel multi-layer waning immunity model that integrates waning of different paces for protection against mild and severe courses of the disease. As this is of particular interest for late-phase epidemic or endemic scenarios, we consider the late-phase of SARS-CoV-2 in Germany.The results of this work show that accounting for resolved mobility significantly influences the pattern of outbreaks. The improved model provides a refined tool for predicting outbreak trajectories and evaluating intervention strategies in relation to mobility by allowing us to assess the transmission that result on traveling. The insights derived from this model can serve as a basis for decisions on the implementation or suspension of interventions, such as mandatory masks on public transportation. Eventually, our model contributes to maintaining mobility as a social good while reducing exuberant disease dynamics potentially driven by travel activities.Author summaryAs human contacts and contact networks are key to the development and prediction of infectious disease spread, travel and commuting activities are important components to be considered in mathematical-epidemiological modeling. Two, often contrasting modeling approaches, based on subpopulations and based on individuals can provide insights of different granularity but also come at different levels of complexity.With this article, we extend a recently introduced Graph-ODE-based model by the explicit introduction of mobility-based infection models in which we allow focused nonpharmaceutical interventions, like face mask mandates in public transport, and in which we can explicitly keep track of secondary cases induced by travel activities, a component mostly not available with equation-based models. In addition, we introduce a novel multi-layer waning immunity model particularly suitable for late-phase epidemic or endemic scenarios.On a daily level and geographically small scale, the newly proposed model often develops similarly, although our results show that complex mobility networks can lead to substantially different disease dynamics in the entirety of a federal state or country. The proposed model thus enables a better understanding of infectious disease dynamics through mobility. It allows for targeted numerical investigations and thus leads to more appropriate real-world interventions.

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“…These estimate the number of unreported cases with data from blood donations or estimation models, for example, but mainly refer to the rst waves of infection during the pandemic. [15][16][17] Therefore, the objective of this study was to estimate the rate of unreported SARS-CoV-2 infections in the population using test data from various indicators collected over a longer period and thus including multiple waves of infection.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of intrahospital test data as an indicator of SARS-CoV-2 incidence underreporting

Mees,

Rauschenberger,

Pscheidl

et al. 2023

Preprint

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The incidence has been widely used to assess the epidemiological situation during the COVID-19 pandemic and guide health policy. As testing requirements changed during the pandemic, more SARS-CoV-2 infections may have remained undetected. This study aims to evaluate the use of different indicators for estimating unreported SARS-CoV-2 infections. Monthly SARS-CoV-2 incidences in the general Bavarian population were compared with three indicators: incidence in healthcare workers (HCWs), incidence of patients, and incidence in visitors. Based on this, the number of unreported SARS-CoV-2 cases in the Bavarian population was estimated. The incidence from the visitors correlated moderately with the official incidences (r = 0.63). The general population incidence and the inpatient incidence are highly correlated (r = 0.91), as are the general population incidence and the HCWs incidence (r = 0.94). Between April 2020 and June 2023, the general population incidence was on average higher than the average incidence of the three indicators, after which the relationship reversed. The reversal of the relationship between the data series of the Bavarian population and the chosen indicators in June 2022 suggests that SARS-CoV-2 infections were underreported. Inpatient screening incidence and HCWs’ incidence in settings with low-threshold test accessibility are suitable indicators of current epidemiology in the general population.

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Age-specific contribution of contacts to transmission of SARS-CoV-2 in Germany

Cited by 13 publications

References 51 publications

Inference under superspreading: Determinants of SARS‐CoV‐2 transmission in Germany

Inference under superspreading: Determinants of SARS‐CoV‐2 transmission in Germany

Novel travel time aware metapopulation models and multi-layer waning immunity for late-phase epidemic and endemic scenarios

Evaluation of intrahospital test data as an indicator of SARS-CoV-2 incidence underreporting

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