A particle filter for sequential infection source estimation

Tang, Wenchang; Tay, Wee Peng

doi:10.1109/icassp.2017.7952926

Cited by 10 publications

(7 citation statements)

References 21 publications

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“…In [29], a two-stage algorithm was proposed to locate a single source in large networks. The reference [30] proposed a sequential source estimation algorithm that allows online update of the source estimate as timestamps are observed sequentially. In [31], a time-reversal backward spreading (TRBS) algorithm was proposed to infer a single source in a weighted network.…”

Section: A Related Workmentioning

confidence: 99%

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Estimating Infection Sources in Networks Using Partial Timestamps

Tang

Tay

2018

IEEE Trans.Inform.Forensic Secur.

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We study the problem of identifying infection sources in a network based on the network topology, and a subset of infection timestamps. In the case of a single infection source in a tree network, we derive the maximum likelihood estimator of the source and the unknown diffusion parameters. We then introduce a new heuristic involving an optimization over a parametrized family of Gromov matrices to develop a single source estimation algorithm for general graphs. Compared with the breadth-first search tree heuristic commonly adopted in the literature, simulations demonstrate that our approach achieves better estimation accuracy than several other benchmark algorithms, even though these require more information like the diffusion parameters. We next develop a multiple sources estimation algorithm for general graphs, which first partitions the graph into source candidate clusters, and then applies our single source estimation algorithm to each cluster. We show that if the graph is a tree, then each source candidate cluster contains at least one source. Simulations using synthetic and real networks, and experiments using real-world data suggest that our proposed algorithms are able to estimate the true infection source(s) to within a small number of hops with a small portion of the infection timestamps being observed.

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Section: A Related Workmentioning

confidence: 99%

“…We first study the single source estimation problem with less assumptions compared with the works [27], [29], [30]. Similar to [27], [29], [30], we assume that the propagation delay along each edge can be modeled using a Gaussian distribution.…”

Section: B Our Contributionsmentioning

confidence: 99%

Estimating Infection Sources in Networks Using Partial Timestamps

Tang

Tay

2018

IEEE Trans.Inform.Forensic Secur.

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“…57 This paper seeks to support more accurate estimation and prediction of measles 58 dynamics by applying a computational statistics technique that combines the best 59 features of insights from ongoing (although noisy) empirical data and dynamic models 60 (although fraught by systematic errors, omissions, and stochastic divergence over time) 61 while mitigating important weaknesses of each. The use of sequential Monte Carlo 62 methods in the form of particle filtering [16][17][18][19][20][21][22][23][24][25][26][27][28] has provided an effective and versatile 63 approach to solving this problem in other infectious diseases, such as the influenza. 64 Specifically, this paper investigates the combination of particle filtering methods with a 65 compartmental model (SEIR model) of measles to recurrently estimate the latent state 66 of the population with respect to the natural history of infection with measles, to 67 anticipate measles evolution and outbreak transitions in pre-vaccination era.…”

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confidence: 99%

Applying particle filtering in both aggregated and age-structured population compartmental models of pre-vaccination measles

Doroshenko

Osgood

2018

Preprint

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Measles is a highly transmissible disease and is one of the leading causes of death among young children under 5 globally. While the use of ongoing surveillance data andrecently -dynamic models offer insight on measles dynamics, both suffer notable shortcomings when applied to measles outbreak prediction. In this paper, we apply the Sequential Monte Carlo approach of particle filtering, incorporating reported measles incidence for Saskatchewan during the pre-vaccination era, using an adaptation of a previously contributed measles compartmental model. To secure further insight, we also perform particle filtering on an age structured adaptation of the model in which the population is divided into two interacting age groups -children and adults. The results indicate that, when used with a suitable dynamic model, particle filtering can offer high predictive capacity for measles dynamics and outbreak occurrence in a low vaccination context. We have investigated five particle filtering models in this project. Based on the most competitive model as evaluated by predictive accuracy, we have performed prediction and outbreak classification analysis. The prediction results demonstrated that this model could predict the measles transmission patterns and classify whether there will be an outbreak or not in the next month (Area under the ROC Curve of 0.89). We conclude that anticipating the outbreak dynamics of measles in low vaccination regions by applying particle filtering with simple measles transmission models, and incorporating time series of reported case counts, is a valuable technique to assist public health authorities in estimating risk and magnitude of measles outbreaks. Such approach offer particularly strong value proposition for other pathogens with little-known dynamics, critical latent drivers, and in the context of the growing number of high-velocity electronic data sources. Strong additional benefits are also likely to be realized from extending the application of this technique to highly vaccinated populations. Author summaryMeasles is a highly infectious disease and is one of the leading causes of death among young children globally. In 2016, close to 90,000 people died from measles. Measles can cause outbreaks particularly in people who did not receive protective vaccine. Understanding how measles outbreaks unfold can help public health agencies to design June 1, 2018 1/25 . CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/340661 doi: bioRxiv preprint first posted online Jun. 6, 2018; intervention strategies to prevent and control this potentially deadly infection. Although traditional methods -including the use of ongoing monitoring of infectious diseases trends by public health agencies and simulation of such trends using scientific technique of mathematical modeling -offer insight on measle...

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“…In [52], a two-stage algorithm was proposed to locate a single source in large networks. The reference [53] proposed a sequential source estimation algorithm that allows online update of the source estimate as timestamps are observed sequentially.…”

Section: Misinformation Sources Estimationmentioning

confidence: 99%

“…We then study the single source estimation problem with fewer assumptions compared with the works [50,52,53]. Similar to [50,52,53], we assume that the propagation delay along each edge can be modeled using a Gaussian distribution.…”

Section: Misinformation Sources Estimationmentioning

confidence: 99%

Identifying misinformation and their sources in social networks

Tang¹

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A particle filter for sequential infection source estimation

Cited by 10 publications

References 21 publications

Estimating Infection Sources in Networks Using Partial Timestamps

Estimating Infection Sources in Networks Using Partial Timestamps

Applying particle filtering in both aggregated and age-structured population compartmental models of pre-vaccination measles

Identifying misinformation and their sources in social networks

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