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Epidemiological changes of scarlet fever before, during and after the COVID-19 pandemic in Chongqing, China: a 19-year surveillance and prediction study

Authors :
Rui Wu
Yu Xiong
Ju Wang
Baisong Li
Lin Yang
Han Zhao
Jule Yang
Tao Yin
Jun Sun
Li Qi
Jiang Long
Qin Li
Xiaoni Zhong
Wenge Tang
Yaokai Chen
Kun Su
Source :
BMC Public Health, Vol 24, Iss 1, Pp 1-11 (2024)
Publication Year :
2024
Publisher :
BMC, 2024.

Abstract

Abstract Background This study aimed to investigate the epidemiological changes in scarlet fever before, during and after the COVID-19 pandemic (2005–2023) and predict the incidence of the disease in 2024 and 2025 in Chongqing Municipality, Southwest China. Methods Descriptive analysis was used to summarize the characteristics of the scarlet fever epidemic. Spatial autocorrelation analysis was utilized to explore the distribution pattern of the disease, and the seasonal autoregressive integrated moving average (SARIMA) model was constructed to predict its incidence in 2024 and 2025. Results Between 2005 and 2023, 9,593 scarlet fever cases were reported in Chongqing, which resulted in an annual average incidence of 1.6694 per 100,000 people. Children aged 3–7 were the primary victims of this disease, with the highest average incidence found among children aged 6 (5.0002 per 100,000 people). Kindergarten children were the dominant infected population, accounting for as much as 54.32% of cases, followed by students (34.09%). The incidence for the male was 1.51 times greater than that for the female. The monthly distribution of the incidence showed a bimodal pattern, with one peak occurring between April and June and another in November or December. The spatial autocorrelation analysis revealed that scarlet fever cases were markedly clustered; the areas with higher incidence were mainly concentrated in Chongqing’s urban areas and its adjacent districts, and gradually spreading to remote areas after 2020. The incidence of scarlet fever increased by 106.54% and 39.33% in the post-upsurge period (2015–2019) and the dynamic zero-COVID period (2020–2022), respectively, compared to the pre-upsurge period (2005–2014) (P

Details

Language :
English
ISSN :
14712458
Volume :
24
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BMC Public Health
Publication Type :
Academic Journal
Accession number :
edsdoj.027e19852ea949fba864e3854085c007
Document Type :
article
Full Text :
https://doi.org/10.1186/s12889-024-20116-5