Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories

Gabriel Owusu; Han Yu; Hong Huang; Gabriel Owusu; Han Yu; Hong Huang

doi:10.3934/publichealth.2022049

AIMS Public Health

2022, Volume 9, Issue 4: 703-717. doi: 10.3934/publichealth.2022049

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Research article

Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories

1.
Department of Applied Statistics and Research Methods, University of Northern Colorado, Greeley, CO 80639, USA
2.
School of Information, University of South Florida, Tampa, FL, 33620, USA

Received: 20 April 2022 Revised: 17 August 2022 Accepted: 01 September 2022 Published: 14 October 2022

The dynamic mechanism of the COVID-19 pandemic has been studied for disease prevention and health protection through areal unit-based log-linear Poisson processes to understand the outbreak of the virus with confirmed daily empirical cases. The predictor of the evolution is structured as a function of a short-term dependence and a long-term trend to identify the pattern of exponential growth in the main epicenters of the virus. The study provides insight into the possible pandemic path of each areal unit and a guide to drive policymaking on preventive measures that can be applied or relaxed to mitigate the spread of the virus. It is significant that knowing the trend of the virus is very helpful for institutions and organizations in terms of instituting resources and measures to help provide a safe working environment and support for all workers/staff/students.
- latent log-linear Poisson process,
- temporal dynamics,
- COVID-19,
- hierarchical models,
- co-occurrence
Citation: Gabriel Owusu, Han Yu, Hong Huang. Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories[J]. AIMS Public Health, 2022, 9(4): 703-717. doi: 10.3934/publichealth.2022049

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Abstract

The dynamic mechanism of the COVID-19 pandemic has been studied for disease prevention and health protection through areal unit-based log-linear Poisson processes to understand the outbreak of the virus with confirmed daily empirical cases. The predictor of the evolution is structured as a function of a short-term dependence and a long-term trend to identify the pattern of exponential growth in the main epicenters of the virus. The study provides insight into the possible pandemic path of each areal unit and a guide to drive policymaking on preventive measures that can be applied or relaxed to mitigate the spread of the virus. It is significant that knowing the trend of the virus is very helpful for institutions and organizations in terms of instituting resources and measures to help provide a safe working environment and support for all workers/staff/students.

Conflict of interests

The authors declare that there are no competing interests.

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