Evolution of within- and between-city COVID-19 dynamics in Guangdong Province, China: An endemic-epidemic modeling study (Preprint)
Year: 2023
Type: preprint
Abstract: BACKGROUND Background: From January 2020 to June 2022, strict public health interventions against COVID-19 were implemented in Guangdong Province, China. OBJECTIVE Objectives: This study aims to investigate the evolution of within- and between-city COVID-19 dynamics in Guangdong Province in this period. METHODS Methods: Data of COVID-19 cases and synchronous interventions from January 2020 to June 2022 in Guangdong Province were collected. The epidemiological characteristics were described, and the effective reproduction number (Rt) was estimated using a sequential Bayesian method. Endemic-epidemic multivariate time-series model was employed to quantitatively analyze the spatiotemporal component values and variations, to identify the evolution of within- and between-city COVID-19 dynamics. RESULTS Results: The incidence of COVID-19 in Guangdong Province was 12.6/100,000 population (15,989 cases) from January 2020 to June 2022. The Rt predominantly remained below 1 and increased to a peak of 1.68 in Stage 5. Results from the endemic-epidemic multivariate time-series model revealed a strong follow-up impact from previous infections in Dongguan, Guangzhou and Zhanjiang, with autoregressive components of 0.48, 0.45 and 0.36, respectively. Local risk was relative high in Yunfu, Shanwei and Shenzhen, with endemic components of 1.17, 1.04 and 0.71, respectively. The impact of the epidemic on the neighboring regions was significant in Zhanjiang, Shenzhen and Zhuhai, with epidemic components of 2.14, 1.92, and 1.89, respectively. In addition, the endemic-epidemic components have been dynamic transformation during the study period. CONCLUSIONS Conclusion: The findings indicate the presence of spatiotemporal variation of COVID-19 in Guangdong Province, even with the implementation of strict interventions. These results underscore the need for specific interventions and coordinated interventions to control the epidemic of COVID-19.
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4388945103