A hierarchical point process with application to storm cell modelling

Albert-Green, Alisha; Braun, W. John; Dean, C. B.; Miller, Craig A.

doi:10.1002/cjs.11485

Cited by 3 publications

(6 citation statements)

References 28 publications

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“…Yau and Loh (2012) considers a special case where the parent process exhibits repulsion. Albert-Green et al (2019) generalizes the Neyman–Scott process by allowing parents to follow the log-Gaussian Cox process ( Møller et al, 1998 ). However, these existing models may not be appropriate for COVID-19 data because of the complex interactions of cluster centers.…”

Section: Introductionmentioning

confidence: 99%

An interaction Neyman–Scott point process model for coronavirus disease-19

2022

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With rapid transmission, the coronavirus disease 2019 (COVID-19) has led to over three million deaths worldwide, posing significant societal challenges. Understanding the spatial patterns of patient visits and detecting local cluster centers are crucial to controlling disease outbreaks. We analyze COVID-19 contact tracing data collected from Seoul, which provide a unique opportunity to understand the mechanism of patient visit occurrence. Analyzing contact tracing data is challenging because patient visits show strong clustering patterns, while cluster centers may have complex interaction behavior. Cluster centers attract each other at mid-range distances because other cluster centers are likely to appear in nearby regions. At the same time, they repel each other at too small distances to avoid merging. To account for such behaviors, we develop a novel interaction Neyman–Scott process that regards the observed patient visit events as offsprings generated from a parent cluster center. Inference for such models is challenging since the likelihood involves intractable normalizing functions. To address this issue, we embed an auxiliary variable algorithm into our Markov chain Monte Carlo. We fit our model to several simulated and real data examples under different outbreak scenarios and show that our method can describe the spatial patterns of patient visits well. We also provide useful visualizations that can inform public health interventions for infectious diseases, such as social distancing.

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Section: Introductionmentioning

confidence: 99%

An interaction Neyman–Scott point process model for coronavirus disease-19

2022

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“…Under the assumption of stationarity, the properties of the point process are invariant under translation and the process is homogeneous. The second-order stationarity states that the second-order intensity only depends on the difference between points λ (2)…”

Section: Definitionsmentioning

confidence: 99%

“…Then, from Equation (1), the Papangelou conditional intensity is λ(ξ|x) = λ(ξ) and λ (2) (ξ i , ξ j ) = λ(ξ i )λ(ξ j ), so that g(ξ i , ξ j ) = 1.…”

Section: Poisson Point Processesmentioning

confidence: 99%

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On spatial and spatio-temporal multi-structure point process models

Raeisi,

Bonneu,

Gabriel

2020

Preprint

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Spatial and spatio-temporal single-structure point process models are widely used in epidemiology, biology, ecology, seismology . . . . However, most natural phenomena present multiple interaction structure or exhibit dependence at multiple scales in space and/or time, leading to define new spatial and spatiotemporal multi-structure point process models. In this paper, we investigate and review such multi-structure point process models mainly based on Gibbs and Cox processes.

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“…Yau and Loh (2012) considers a special case where the parent process exhibits repulsion. Albert-Green et al (2019) generalizes the Neyman-Scott process by allowing the parents to follow the log-Gaussian Cox process (Møller et al, 1998). However, these existing models may not be appropriate for our COVID-19 data because of the complex interactions of local spreading events.…”

Section: Introductionmentioning

confidence: 99%

An Interaction Neyman-Scott Point Process Model for Coronavirus Disease-19

Park¹,

Chang²,

Choi³

2021

Preprint

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With rapid transmission, the coronavirus disease 2019 (COVID-19) has led to over 2 million deaths worldwide, posing significant societal challenges. Understanding the spatial patterns of patient visits and detecting the local spreading events are crucial to controlling disease outbreaks. We analyze highly detailed COVID-19 contact tracing data collected from Seoul, which provides a unique opportunity to understand the mechanism of patient visit occurrence. Analyzing contact tracing data is challenging because patient visits show strong clustering patterns while clusters of events may have complex interaction behavior.To account for such behaviors, we develop a novel interaction Neyman-Scott process that regards the observed patient visit events as offsprings generated from a parent spreading event. Inference for such models is complicated since the likelihood involves intractable normalizing functions. To address this issue, we embed an auxiliary variable algorithm into our Markov chain Monte Carlo. We fit our model to several simulated and real data examples under different outbreak scenarios and show that our method can describe spatial patterns of patient visits well. We also provide visualization tools that can inform public

show abstract

A hierarchical point process with application to storm cell modelling

Cited by 3 publications

References 28 publications

An interaction Neyman–Scott point process model for coronavirus disease-19

An interaction Neyman–Scott point process model for coronavirus disease-19

On spatial and spatio-temporal multi-structure point process models

An Interaction Neyman-Scott Point Process Model for Coronavirus Disease-19

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