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

Jaewoo Park1,2, Won Chang3, Boseung Choi4,5

  • 1Department of Statistics and Data Science, Yonsei University, Seoul, South Korea.

Spatial Statistics
|December 13, 2021
PubMed

Insights

Understanding COVID-19 spatial patterns is key to controlling outbreaks. This study introduces a new model to analyze patient visit clusters, improving infectious disease public health interventions like social distancing.

Area of Science:

  • Epidemiology
  • Spatial Statistics
  • Computational Biology

Background:

  • Coronavirus disease 2019 (COVID-19) rapid transmission causes significant global mortality and societal challenges.
  • Effective control of infectious disease outbreaks requires understanding spatial patterns of patient visits and identifying local cluster centers.
  • Analyzing contact tracing data is complex due to strong clustering and intricate interactions between cluster centers.

Purpose of the Study:

  • To develop a novel statistical model for analyzing spatial patterns in COVID-19 patient visit data.
  • To accurately describe the complex interaction behaviors of disease cluster centers.
  • To provide visualizations that can inform public health interventions for infectious diseases.

Main Methods:

  • Development of a novel interaction Neyman-Scott process to model patient visit events as offspring from parent cluster centers.
  • Implementation of an auxiliary variable algorithm within Markov chain Monte Carlo for model inference, addressing intractable likelihood functions.
  • Fitting the model to simulated and real COVID-19 contact tracing data from Seoul under various outbreak scenarios.

Main Results:

  • The proposed model effectively describes the spatial patterns of patient visits in COVID-19 contact tracing data.
  • The model captures the complex attraction and repulsion dynamics between disease cluster centers.
  • Demonstrated accuracy across different simulated and real-world outbreak scenarios.

Conclusions:

  • The developed interaction Neyman-Scott process provides a robust method for analyzing infectious disease spatial epidemiology.
  • Accurate modeling of patient visit patterns can significantly enhance the effectiveness of public health interventions, including social distancing measures.
  • The findings offer valuable insights for controlling future infectious disease outbreaks through data-driven spatial analysis.

Keywords:
Bayesian hierarchical modelCluster point processDoubly-intractable distributionsInfectious diseaseMarkov chain Monte Carlo

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