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Schlich, Robert; Axhausen, Kay W.

doi:10.1023/a:1021230507071

Cited by 279 publications

(45 citation statements)

References 19 publications

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“…For instance, it is known that traffic and activity patterns differ vastly between weekdays and weekends [90] - but we assumed weekday patterns throughout the simulation.…”

Section: Resultsmentioning

confidence: 99%

Reconstructing the 2003/2004 H3N2 influenza epidemic in Switzerland with a spatially explicit, individual-based model

et al. 2011

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BackgroundSimulation models of influenza spread play an important role for pandemic preparedness. However, as the world has not faced a severe pandemic for decades, except the rather mild H1N1 one in 2009, pandemic influenza models are inherently hypothetical and validation is, thus, difficult. We aim at reconstructing a recent seasonal influenza epidemic that occurred in Switzerland and deem this to be a promising validation strategy for models of influenza spread.MethodsWe present a spatially explicit, individual-based simulation model of influenza spread. The simulation model bases upon (i) simulated human travel data, (ii) data on human contact patterns and (iii) empirical knowledge on the epidemiology of influenza. For model validation we compare the simulation outcomes with empirical knowledge regarding (i) the shape of the epidemic curve, overall infection rate and reproduction number, (ii) age-dependent infection rates and time of infection, (iii) spatial patterns.ResultsThe simulation model is capable of reproducing the shape of the 2003/2004 H3N2 epidemic curve of Switzerland and generates an overall infection rate (14.9 percent) and reproduction numbers (between 1.2 and 1.3), which are realistic for seasonal influenza epidemics. Age and spatial patterns observed in empirical data are also reflected by the model: Highest infection rates are in children between 5 and 14 and the disease spreads along the main transport axes from west to east.ConclusionsWe show that finding evidence for the validity of simulation models of influenza spread by challenging them with seasonal influenza outbreak data is possible and promising. Simulation models for pandemic spread gain more credibility if they are able to reproduce seasonal influenza outbreaks. For more robust modelling of seasonal influenza, serological data complementing sentinel information would be beneficial.

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“…For instance, it is known that traffic and activity patterns differ vastly between weekdays and weekends [90] - but we assumed weekday patterns throughout the simulation.…”

Section: Resultsmentioning

confidence: 99%

Reconstructing the 2003/2004 H3N2 influenza epidemic in Switzerland with a spatially explicit, individual-based model

et al. 2011

View full text Add to dashboard Cite

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“…Some can be direct approaches such as traffic forecasting [1], activity based modeling [2], and the design of transportation networks [3], while some others require more simplified models of human mobility such as the spreading of viruses and the evolution of epidemics [4,5,6,7,8], measuring human exposure to air pollutants [9], and simulating mobile networks of wireless devices [10]. In order to develop models of human mobility, conventionally, information is extracted from travel surveys [11,12,13,14,15]. These questionnaires provide us with the detailed itinerary of individual travels.…”

Section: Introductionmentioning

confidence: 99%

“…These data sources offer us the unique opportunity to understand and characterize the patterns of human travel behavior at a massive scale, unimaginable before. These big data sources contains incomplete information about individuals trips; which brings particular challenges to relate individual mobility to conventional models [11,12,13,14,15]. In general, these data sources lack the demographic information of individuals, the traces of their locations are not continuously collected and there is no detailed information about the specific choices of the individuals daily activities.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Patterns of Urban Human Mobility

et al. 2012

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Abstract. The modeling of human mobility is adopting new directions due to the increasing availability of big data sources from human activity. These sources enclose digital information about daily visited locations of a large number of individuals. Examples of these data include: mobile phone calls, credit card transactions, bank notes dispersal, check-ins in internet applications, among several others. In this study, we consider the data obtained from smart subway fare card transactions to characterize and model urban mobility patterns. We present a simple mobility model for predicting peoples' visited locations using the popularity of places in the city as an interaction parameter between different individuals. This ingredient is sufficient to reproduce several characteristics of the observed travel behavior such as: the number of trips between different locations in the city, the exploration of new places and the frequency of individual visits of a particular location. Moreover, we indicate the limitations of the proposed model and discuss open questions in the current state of the art statistical models of human mobility.

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“…One of the remarkable characteristics is its day-to-day variability (1)(2)(3)(4)(5)(6)(7)(8)(9)(10). In this study, it is noteworthy to first clarify four technical terms: heterogeneity, variability, variation and variance.…”

Section: Introductionmentioning

confidence: 99%

Exploring Variation Properties of Departure Time Choice Behavior by Using Multilevel Analysis Approach

Chikaraishi

Fujiwara

Zhang

et al. 2009

Transportation Research Record

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This paper examines the variation properties of departure time choice behavior by activity type, using a continuous six-week travel survey collected in the cities of Karlsruhe and Halle in Germany in 1999. Total variation of departure time choice is decomposed into five variation components: spatial variation, temporal variation at aggregate level, inter-household variation, inter-individual variation, and intra-individual variation. These variations are first quantitatively analyzed using multilevel modeling approach without considering the influences of explanatory variables. Then, based on the clarified variations patterns, the paper further examines how much of the variations can be captured by explanatory variables. It is confirmed that different activities show quite different variations and among the variations, the intra-individual variation accounts for the largest percentage of the total variation. Model estimation results further underscore the needs of simultaneously dealing with unobserved macro-level variations, especially inter-individual, inter-household and spatial variations, as well as the intra-individual variation, even when the explanatory variables are included in the model. Such quantitative assessment of various types of variations based on the multi-level modeling approach could provide a useful guide for the specification of behavior models, and deepen the knowledge of behavioral variability by going beyond the traditional paradigm which only focuses on a limited set of variations.

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Cited by 279 publications

References 19 publications

Reconstructing the 2003/2004 H3N2 influenza epidemic in Switzerland with a spatially explicit, individual-based model

Reconstructing the 2003/2004 H3N2 influenza epidemic in Switzerland with a spatially explicit, individual-based model

Spatiotemporal Patterns of Urban Human Mobility

Exploring Variation Properties of Departure Time Choice Behavior by Using Multilevel Analysis Approach

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