» Articles » PMID: 26852019

Prevalence of Hemorrhagic Fever with Renal Syndrome in Yiyuan County, China, 2005-2014

Overview
Journal BMC Infect Dis
Publisher Biomed Central
Date 2016 Feb 8
PMID 26852019
Citations 19
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in mainland China, where human cases account for 90 % of the total global cases. Yiyuan County is one of the most serious affected areas in China. Therefore, there is an urgent need for monitoring and predicting HFRS incidence in Yiyuan to make the control of HFRS more effective.

Methods: The study was based on the reported cases of HFRS from the National Notifiable Disease Surveillance System. The demographic and spatial distributions of HFRS in Yiyuan were established. Then we fit autoregressive integrated moving average (ARIMA) models and predict the HFRS epidemic trend.

Results: There were 362 cases reported in Yiyuan during the 10-year study period. The human infections in the fall and winter reflected a seasonal characteristic pattern of Hantaan virus (HTNV) transmission. The best model was ARIMA (2, 1, 1) × (0, 1, 1)12 (AIC value 516.86) with a high validity.

Conclusion: The ARIMA model fits the fluctuations in HFRS frequency and it can be used for future forecasting when applied to HFRS prevention and control.

Citing Articles

Epidemiological characteristics and prediction model construction of hand, foot and mouth disease in Quzhou City, China, 2005-2023.

Xu W, Zheng C, Fu C, Gong X, Fang Q, Yin Z Front Public Health. 2025; 12:1474855.

PMID: 39744358 PMC: 11688476. DOI: 10.3389/fpubh.2024.1474855.


Epidemiology of Hemorrhagic Fever with Renal Syndrome and Host Surveillance in Zhejiang Province, China, 1990-2021.

Su F, Liu Y, Ling F, Zhang R, Wang Z, Sun J Viruses. 2024; 16(1).

PMID: 38275955 PMC: 10818760. DOI: 10.3390/v16010145.


Epidemiological characteristics and prediction model construction of hemorrhagic fever with renal syndrome in Quzhou City, China, 2005-2022.

Gao Q, Wang S, Wang Q, Cao G, Fang C, Zhan B Front Public Health. 2024; 11:1333178.

PMID: 38274546 PMC: 10808376. DOI: 10.3389/fpubh.2023.1333178.


Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh.

Rahman M, Chowdhury A, Amrin M PLOS Glob Public Health. 2023; 2(5):e0000495.

PMID: 36962227 PMC: 10021465. DOI: 10.1371/journal.pgph.0000495.


Trends and focuses of hantavirus researches: a global bibliometric analysis and visualization from 1980 to 2020.

Wei X, Li X, Song S, Wen X, Jin T, Zhao C Arch Public Health. 2022; 80(1):218.

PMID: 36182906 PMC: 9526533. DOI: 10.1186/s13690-022-00973-5.


References
1.
Kuhn L, Davidson L, Durkin M . Use of Poisson regression and time series analysis for detecting changes over time in rates of child injury following a prevention program. Am J Epidemiol. 1994; 140(10):943-55. DOI: 10.1093/oxfordjournals.aje.a117183. View

2.
Reichert T, Simonsen L, Sharma A, Pardo S, Fedson D, Miller M . Influenza and the winter increase in mortality in the United States, 1959-1999. Am J Epidemiol. 2004; 160(5):492-502. DOI: 10.1093/aje/kwh227. View

3.
Lin H, Lu L, Tian L, Zhou S, Wu H, Bi Y . Spatial and temporal distribution of falciparum malaria in China. Malar J. 2009; 8:130. PMC: 2700130. DOI: 10.1186/1475-2875-8-130. View

4.
Sato R . Disease management with ARIMA model in time series. Einstein (Sao Paulo). 2013; 11(1):128-31. PMC: 4872983. DOI: 10.1590/s1679-45082013000100024. View

5.
Liu Q, Liu X, Jiang B, Yang W . Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model. BMC Infect Dis. 2011; 11:218. PMC: 3169483. DOI: 10.1186/1471-2334-11-218. View