Sensor-based localization of epidemic sources on human mobility networks

Li, Jun; Manitz, Juliane; Bertuzzo, Enrico; Kolaczyk, Eric D.

doi:10.1371/journal.pcbi.1008545

Cited by 4 publications

(3 citation statements)

References 37 publications

(57 reference statements)

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“…Ma et al, [29] proposed several data recovery methods that exploits the spatio-temporal correlation to achieve reliable data inference. Some studies take advantage of the geographical patterns of social network [16] or sensor data [25] to infer the patterns of disease outbreak. In [41], the authors investigate the mobility patterns of city residents to infer the prevalence rate of multiple chronic NCDs, which is somewhat similar to our idea.…”

Section: Population Health Data Inferencementioning

confidence: 99%

Quality-Guaranteed and Cost-Effective Population Health Profiling: A Deep Active Learning Approach

Chen,

Wang,

Thakuriah

2023

ACM Trans. Comput. Healthcare

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Reliability and cost are two primary consideration for profiling population-scale prevalence ( PPP ) of multiple None Communicable Diseases ( NCDs ). In this paper, we exploit intra-disease and inter-disease correlation in different traditionally-sensed-areas ( TS-A ) to reduce the required number of the profiling task allocated without compromising the data reliability. Specifically, we propose a novel approach called Compressive Population Health TS-A Selection ( CPH-TS ), which blends the state-of-the-art profile inference, data augmentation and active learning in a unified deep learning framework. It can actively select a minimum number of TS-A regions for profiling task allocation in each profiling cycle, while deducting of the missing data of the unprofiled regions with a probabilistic guarantee of reliability. We evaluate our approach on real-world prevalence datasets of London, which shows the effectiveness of CPH-TS . In general, CPH-TS assigned 11.1-27.3% fewer tasks than baselines, assigning tasks to only 34.7% of the sub-regions while the profiling error below 5% for 95% of the cycles.

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Section: Population Health Data Inferencementioning

confidence: 99%

Quality-Guaranteed and Cost-Effective Population Health Profiling: A Deep Active Learning Approach

Chen,

Wang,

Thakuriah

2023

ACM Trans. Comput. Healthcare

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“…As one of the significant measures, propagation source locating has attracted widespread attentions, many effective methods are proposed in recent years (Jiang et al 2017;Paluch et al 2020). These methods can provide effective solutions for many important issues in reality, including locating the source(s) of SARS (Brockmann and Helbing 2013), COVID-19 (Tian et al 2020), Cholera (Li et al 2021), identifying the source of delay in public transportation networks (Manitz et al 2017), estimating the source of foodborne disease (Horn and Friedrich 2019), etc.…”

Section: Introductionmentioning

confidence: 99%

Locating the propagation source in complex networks with observers-based similarity measures and direction-induced search

Peng

Liu

et al. 2023

Soft Comput

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Locating the propagation source is one of the most important strategies to control the harmful diffusion process on complex networks. Most existing methods only consider the infection time information of the observers, but the diffusion direction information of the observers is ignored, which is helpful to locate the source. In this paper, we consider both of the diffusion direction information and the infection time information to locate the source. We introduce a relaxed direction-induced search (DIS) to utilize the diffusion direction information of the observers to approximate the actual diffusion tree on a network. Based on the relaxed DIS, we further utilize the infection time information of the observers to define two kinds of observers-based similarity measures, including the Infection Time Similarity and the Infection Time Order Similarity. With the two kinds of similarity measures and the relaxed DIS, a novel source locating method is proposed. We validate the performance of the proposed method on a series of synthetic and real networks. The experimental results show that the proposed method is feasible and effective in accurately locating the propagation source.

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“…One of the important measures is identifying the diffusion source that initiates the diffusion process on networks, which has attracted widespread attentions in recent years [ 5 ]. Many existing source identification methods provided effective solutions for some important issues in reality, such as identifying the source of SARS [ 6 ], COVID-19 [ 7 ], Cholera [ 8 ], finding the source of foodborne disease [ 9 ], etc. However, accurately identifying the diffusion source is still an opening challenge.…”

Section: Introductionmentioning

confidence: 99%

Diffusion characteristics classification framework for identification of diffusion source in complex networks

2023

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The diffusion phenomena taking place in complex networks are usually modelled as diffusion process, such as the diffusion of diseases, rumors and viruses. Identification of diffusion source is crucial for developing strategies to control these harmful diffusion processes. At present, accurately identifying the diffusion source is still an opening challenge. In this paper, we define a kind of diffusion characteristics that is composed of the diffusion direction and time information of observers, and propose a neural networks based diffusion characteristics classification framework (NN-DCCF) to identify the source. The NN-DCCF contains three stages. First, the diffusion characteristics are utilized to construct network snapshot feature. Then, a graph LSTM auto-encoder is proposed to convert the network snapshot feature into low-dimension representation vectors. Further, a source classification neural network is proposed to identify the diffusion source by classifying the representation vectors. With NN-DCCF, the identification of diffusion source is converted into a classification problem. Experiments are performed on a series of synthetic and real networks. The results show that the NN-DCCF is feasible and effective in accurately identifying the diffusion source.

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Sensor-based localization of epidemic sources on human mobility networks

Cited by 4 publications

References 37 publications

Quality-Guaranteed and Cost-Effective Population Health Profiling: A Deep Active Learning Approach

Quality-Guaranteed and Cost-Effective Population Health Profiling: A Deep Active Learning Approach

Locating the propagation source in complex networks with observers-based similarity measures and direction-induced search

Diffusion characteristics classification framework for identification of diffusion source in complex networks

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