JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1124 10.6339/24-JDS1124 Statistical Data Science Dynamic Network Poisson Autoregression with Application to COVID-19 Count Data ✩ AsaiManabu1 https://orcid.org/0000-0002-9543-747X ChuAmanda M. Y.2 https://orcid.org/0000-0003-0781-8166 SoMike K. P.immkpso@ust.hk3∗ Faculty of Economics, Soka University, Hachioji, Tokyo, Japan Department of Social Sciences and Policy Studies, The Education University of Hong Kong, Hong Kong Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Hong Kong

The authors are most grateful to Yoshihisa Baba and two anonymous reviewers for their very helpful comments and suggestions. The first author acknowledges the financial support of the Japan Society for the Promotion of Science (grant number 22KK0022). This work was partially supported by The Hong Kong University of Science and Technology research grant “Risk Analytics and Applications” (grant number SBMDF21BM07). The funding recipient was MKPS.

Corresponding author. Email: immkpso@ust.hk. 2025572024231208224 Supplementary Material

Programming code can be found at https://github.com/ManabuAsai/Dynamic_network_poisson.

24620231232024 2025 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2025 Open access article under the CC BY license.

There is growing interest in accommodating network structure in panel data models. We consider dynamic network Poisson autoregressive (DN-PAR) models for panel count data, enabling their use in regard to a time-varying network structure. We develop a Bayesian Markov chain Monte Carlo technique for estimating the DN-PAR model, and conduct Monte Carlo experiments to examine the properties of the posterior quantities and compare dynamic and constant network models. The Monte Carlo results indicate that the bias in the DN-PAR models is negligible, while the constant network model suffers from bias when the true network is dynamic. We also suggest an approach for extracting the time-varying network from the data. The empirical results for the count data for confirmed cases of COVID-19 in the United States indicate that the extracted dynamic network models outperform the constant network models in regard to the deviance information criterion and out-of-sample forecasting.

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