» Articles » PMID: 39984565

Analysis and Prediction of the Incidence Temporal Trends of Echinococcosis in China from 2010 to 2021

Overview
Journal Sci Rep
Specialty Science
Date 2025 Feb 21
PMID 39984565
Authors
Affiliations
Soon will be listed here.
Abstract

Echinococcosis poses a significant health burden in China, yet previous studies have mainly concentrated on its prevalence rather than incidence trends. This study analyzes echinococcosis incidence from 2010 to 2021 and projects future trends to enhance prevention and control strategies. The annual percentage change (APC) and average annual percentage change (AAPC) were calculated to determine temporal trends, while the time series was decomposed to assess seasonal patterns. During this period, China documented 53,141 new echinococcosis cases (excluding Hong Kong, Macao, and Taiwan), averaging 0.320 cases per 100,000 annually. The AAPC indicated a non-significant decline of -2.718%. The analysis identified two critical inflection points in 2014 and 2017, leading to three distinct trends: a non-significant decline from 2010 to 2014, a significant rise from 2014 to 2017, and a significant decrease from 2017 to 2021. Cases peaked in December, and predictions from the Seasonal Autoregressive Integrated Moving Average (SARIMA) model suggest a slight rise in incidence from September 2022 to August 2025, advising intensified efforts in preventive measures to prevent resurgence.

References
1.
Torgerson P, Keller K, Magnotta M, Ragland N . The global burden of alveolar echinococcosis. PLoS Negl Trop Dis. 2010; 4(6):e722. PMC: 2889826. DOI: 10.1371/journal.pntd.0000722. View

2.
McManus D, Zhang W, Li J, Bartley P . Echinococcosis. Lancet. 2003; 362(9392):1295-304. DOI: 10.1016/S0140-6736(03)14573-4. View

3.
Wang Q, Yu W, Zhong B, Shang J, Huang L, Mastin A . Seasonal pattern of Echinococcus re-infection in owned dogs in Tibetan communities of Sichuan, China and its implications for control. Infect Dis Poverty. 2016; 5(1):60. PMC: 4932717. DOI: 10.1186/s40249-016-0155-4. View

4.
Zhang L, Xiong S, Zhu S, Tian J, Chen Q, Luo X . Construction of Prediction Model of Foodborne Disease Outbreaks and Its Trend Prediction - Guizhou Province, China, 2023-2025. China CDC Wkly. 2024; 6(18):408-412. PMC: 11082649. DOI: 10.46234/ccdcw2024.079. View

5.
Orang A, Berke O, Poljak Z, Greer A, Rees E, Ng V . Forecasting seasonal influenza activity in Canada-Comparing seasonal Auto-Regressive integrated moving average and artificial neural network approaches for public health preparedness. Zoonoses Public Health. 2024; 71(3):304-313. DOI: 10.1111/zph.13114. View