Interaction data from the Copenhagen Networks Study

Sapieżyński, Piotr; Stopczynski, Arkadiusz; Lassen, David Dreyer; Lehmann, Sune

doi:10.1038/s41597-019-0325-x

Cited by 130 publications

(129 citation statements)

References 53 publications

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“…Hence, the Wi-Fi based approach taken in this paper is tailored towards transportation science applications where either further discrimination between public and other modes is desired or accelerometer data is unavailable. A portion of the data in this paper (see Results section 5.2) originates from a large-scale mobile phone-based data collection undertaken as a part of the Copenhagen Networks Project [71,72]. The data collector app employed in this project recorded sparse mobile sensor data from GPS and existing Wi-Fi scans but did not collect high frequency accelerometer data to conserve battery life.…”

Section: Plos Onementioning

confidence: 99%

Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth

et al. 2020

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Understanding which transportation modes people use is critical for smart cities and planners to better serve their citizens. We show that using information from pervasive Wi-Fi access points and Bluetooth devices can enhance GPS and geographic information to improve transportation detection on smartphones. Wi-Fi information also improves the identification of transportation mode and helps conserve battery since it is already collected by most mobile phones. Our approach uses a machine learning approach to determine the mode from pre-prepocessed data. This approach yields an overall accuracy of 89% and average F 1 score of 83% for inferring the three grouped modes of self-powered, car-based, and public transportation. When broken out by individual modes, Wi-Fi features improve detection accuracy of bus trips, train travel, and driving compared to GPS features alone and can substitute for GIS features without decreasing performance. Our results suggest that Wi-Fi and Bluetooth can be useful in urban transportation research, for example by improving mobile travel surveys and urban sensing applications.

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Section: Plos Onementioning

confidence: 99%

Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth

et al. 2020

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“…To this aim, we couple this model with a realistic quantification of the effect of these two measures based on real-world contact and interaction data. The following results are indeed obtained by simulations using the Copenhagen Networks Study (CNS) dataset 50 that describes real proximity relations of smartphone users measured via Bluetooth (see Section 4.1). Moreover, we present in the Supplementary Information simulations performed using two other datasets collected by the SocioPatterns collaboration with a different type of wearable sensors.…”

Section: Resultsmentioning

confidence: 79%

“…First, we provide a realistic quantification of the tracing ability by performing simulations of spreading processes and of contact tracing strategies on real-world data sets collected across different social settings (i.e., a university campus, a workplace, a high school). [50][51][52] This allows us to estimate the actual "tracing ability" parameter ε T for different possible tracing policies (i.e., the thresholds considered to define a contact measured by the app as "at risk") and for different values of ε I (Section 4.1). The parameter ε T can then be inserted into the mathematical model to study the impact of the tracing policy on the spread.…”

Section: Introductionmentioning

confidence: 99%

Digital Proximity Tracing in the COVID-19 Pandemic on Empirical Contact Networks

Cencetti

Santin

Longa

et al. 2020

Preprint

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Digital contact tracing is increasingly considered as a tool to control infectious disease outbreaks. As part of a broader test, trace, isolate, and quarantine strategy, digital contract tracing apps have been proposed to alleviate lock-downs, and to return societies to a more normal situation in the ongoing COVID-19 crisis. Early work evaluating digital contact tracing did not consider important features and heterogeneities present in real-world contact patterns which impact epidemic dynamics. Here, we fill this gap by considering a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing apps in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread of COVID-19 in realistic scenarios such as a university campus, a workplace, or a high school. We find that restrictive policies are more effective in confining the epidemics but come at the cost of quarantining a large part of the population. It is possible to avoid this effect by considering less strict policies, which only consider contacts with longer exposure and at shorter distance to be at risk. Our results also show that isolation and tracing can help keep re-emerging outbreaks under control provided that hygiene and social distancing measures limit the reproductive number to 1.5. Moreover, we confirm that a high level of app adoption is crucial to make digital contact tracing an effective measure. Our results may inform app-based contact tracing efforts currently being implemented across several countries worldwide.

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“…However, while the spreading parameters are subject to a broad scientific discussion, publicly available data, which could be used for inferring a realistic contact network, practically does not exist. Therefore real-world data on contact networks are rare [30,45,23,32,43] and not available for large-scale populations. A reasonable approach is to generate the data synthetically, for instance by using mobility and population data based on geographical diffusion [46,17,36,3].…”

Section: Related Workmentioning

confidence: 99%

Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study

Großmann

Backenköhler

Wolf

2020

Preprint

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In the recent COVID-19 pandemic, computer simulations are used to predict the evolution of the virus propagation and to evaluate the prospective effectiveness of non-pharmaceutical interventions. As such, the corresponding mathematical models and their simulations are central tools to guide political decision-making. Typically, ODE-based models are considered, in which fractions of infected and healthy individuals change deterministically and continuously over time.In this work, we translate an ODE-based COVID-19 spreading model from literature to a stochastic multi-agent system and use a contact network to mimic complex interaction structures. We observe a large dependency of the epidemic's dynamics on the structure of the underlying contact graph, which is not adequately captured by existing ODEmodels. For instance, existence of super-spreaders leads to a higher infection peak but a lower death toll compared to interaction structures without super-spreaders. Overall, we observe that the interaction structure has a crucial impact on the spreading dynamics, which exceeds the effects of other parameters such as the basic reproduction number R0. We conclude that deterministic models fitted to COVID-19 outbreak data have limited predictive power or may even lead to wrong conclusions while stochastic models taking interaction structure into account offer different and probably more realistic epidemiological insights.

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Interaction data from the Copenhagen Networks Study

Cited by 130 publications

References 53 publications

Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth

Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth

Digital Proximity Tracing in the COVID-19 Pandemic on Empirical Contact Networks

Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study

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