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

Jaewoo Park, Won Chang, Boseung Choi

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Article number100561
JournalSpatial Statistics
Publication statusPublished - 2022 Mar

Bibliographical note

Funding Information:
Jaewoo Park was supported by the Yonsei University, South Korea Research Fund of 2020-22-0501 and the National Research Foundation of Korea ( NRF-2020R1C1C1A0100386811 ). Boseung Choi was supported by the National Research Foundation of Korea ( 2020R1F1A1A01066082 ). The Ohio Super Computer Center (OSC) provided part of computational resources for this study. The authors are grateful to Tomáš Mrkvička for providing useful sample codes and the anonymous reviewers for their careful reading and valuable comments. The authors are also grateful to Korea Spatial Information & Community Co. Ltd. for their support with data and Sungjae Kim and Eunjin Eom for data collection and organization.

Publisher Copyright:
© 2021 Elsevier B.V.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computers in Earth Sciences
  • Management, Monitoring, Policy and Law


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