Are you getting sick? Predicting influenza-like symptoms using human mobility behaviors

Barlacchi, Gianni; Perentis, Christos; Mehrotra, Anil K.; Musolesi, Mirco; Lepri, Bruno

doi:10.1140/epjds/s13688-017-0124-6

Cited by 38 publications

(31 citation statements)

References 26 publications

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“…Relying on the extraction of spatial-temporal characteristics, existing works label trajectories as belonging to different motion patterns, e.g., walking/driving/biking in transportation classification [5], or occupied/non-occupied in taxi status inference [13]. Other works use human mobility data to assess the users' physical and mental health conditions, such as to predict flu-like symptoms [14], daily mood states [15], and stress levels [16]. However, despite the presence of a large number of works on semantic trajectory mining and classification, the problem of inferring nationalities from foreign tourists' motion traces has never been formally defined and addressed.…”

Section: Related Workmentioning

confidence: 99%

Identifying Foreign Tourists’ Nationality from Mobility Traces via LSTM Neural Network and Location Embeddings

Crivellari

Beinat

2019

Applied Sciences

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The interest in human mobility analysis has increased with the rapid growth of positioning technology and motion tracking, leading to a variety of studies based on trajectory recordings. Mapping the routes that people commonly perform was revealed to be very useful for location-based service applications, where individual mobility behaviors can potentially disclose meaningful information about each customer and be fruitfully used for personalized recommendation systems. This paper tackles a novel trajectory labeling problem related to the context of user profiling in “smart” tourism, inferring the nationality of individual users on the basis of their motion trajectories. In particular, we use large-scale motion traces of short-term foreign visitors as a way of detecting the nationality of individuals. This task is not trivial, relying on the hypothesis that foreign tourists of different nationalities may not only visit different locations, but also move in a different way between the same locations. The problem is defined as a multinomial classification with a few tens of classes (nationalities) and sparse location-based trajectory data. We hereby propose a machine learning-based methodology, consisting of a long short-term memory (LSTM) neural network trained on vector representations of locations, in order to capture the underlying semantics of user mobility patterns. Experiments conducted on a real-world big dataset demonstrate that our method achieves considerably higher performances than baseline and traditional approaches.

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Section: Related Workmentioning

confidence: 99%

Identifying Foreign Tourists’ Nationality from Mobility Traces via LSTM Neural Network and Location Embeddings

Crivellari

Beinat

2019

Applied Sciences

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“…In order to explore the impact of the network structure on the performance of our models to predict depressive states, we optimize the number of hidden layers (denoted by h in this paper) to construct autoencoders. More specically, we construct autoencoders with the dierent number of hidden layers (i.e., excluding the input and output layers) such that h 2 [1, 3,5,7]. We use 1 as a lower bound because that is a minimum number of hidden layer an autoencoder should have.…”

Section: Selecting and Tuning Of The Experimental Parametersmentioning

confidence: 99%

“…All of these studies show the potential of using mobile sensor data for inferring emotional states of users in real-time. In particular, information on human mobility behavior derived from GPS data has been shown to be an invaluable source for passively inferring users' mental health and well-being [5,9,41,42]. However, these studies rely on hand-crafted features in order to build predictive models.…”

Section: Exploiting Mobile Sensor Data To Infer Users' Mood and Well-mentioning

confidence: 99%

“…The process of training a prediction model. It comprises of seven steps: (1) train three generic autoencoders; (2) extract the three trained encoders; (3) compute mobility features; (4) use these feature as input to ML algorithm;(5) compute PHQ scores; (6) feed the training labels into ML algorithm as dependent variables;(7) train the model. The process of making predictions using the trained encoder and prediction model.…”

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confidence: 99%

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Using Autoencoders to Automatically Extract Mobility Features for Predicting Depressive States

Mehrotra

Musolesi

2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Recent studies have shown the potential of exploiting GPS data for passively inferring people's mental health conditions. However, feature extraction for characterizing human mobility remains a heuristic process that relies on the domain knowledge of the condition under consideration. Moreover, we do not have guarantees that these "hand-crafted" metrics are able to eectively capture mobility behavior of users. Indeed, informative emerging patterns in the data might not be characterized by them. This is also a complex and often time-consuming task, since it usually consists of a lengthy trial-and-error process.In this paper, we investigate the potential of using autoencoders for automatically extracting features from the raw input data. Through a series of experiments we show the eectiveness of autoencoder-based features for predicting depressive states of individuals compared to "hand-crafted" ones. Our results show that automatically extracted features lead to an improvement of the performance of the prediction models, while, at the same time, reducing the complexity of the feature design task. Moreover, through an extensive experimental performance analysis, we demonstrate the optimal conguration of the key parameters at the basis of the proposed approach.

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“…In this part, we will show how developing accurate predictive and generative mobility models can be greatly beneficial for several aspects of social good, from mobility in emergency scenarios [14] to the prevention of epidemic diffusion [9], nowcast well-being [26] and even the design of more sustainable smart cities [11]. We will discuss about present and future challenges on mobility-related problems such as ridesharing [29], automatic discovery of urban regions [37,42], prediction of health from human displacements [1,5] and traffic forecasting [20,28]. Finally, we discuss privacy issues related to the analysis of human mobility data [27].…”

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confidence: 99%

Human Mobility from theory to practice:Data, Models and Applications

Pappalardo

Barlacchi

Pellungrini

et al. 2019

Companion Proceedings of the 2019 World Wide Web Conference

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The inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mobility and machine learning models applicable to specific problems. We review the state of the art of five main aspects in human mobility: (1) human mobility data landscape; (2) key measures of individual and collective mobility; (3) generative models at the level of individual, population and mixture of the two; (4) next location prediction algorithms; (5) applications for social good. For each aspect, we show experiments and simulations using the Python library "scikit-mobility" developed by the presenters of the tutorial. CCS CONCEPTS • Information systems → Data mining; • Computing methodologies → Modeling methodologies.

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Are you getting sick? Predicting influenza-like symptoms using human mobility behaviors

Cited by 38 publications

References 26 publications

Identifying Foreign Tourists’ Nationality from Mobility Traces via LSTM Neural Network and Location Embeddings

Identifying Foreign Tourists’ Nationality from Mobility Traces via LSTM Neural Network and Location Embeddings

Using Autoencoders to Automatically Extract Mobility Features for Predicting Depressive States

Human Mobility from theory to practice:Data, Models and Applications

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