Dataset Information

Analysis of an outbreak of hemorrhagic fever with renal syndrome in college students in Xi'an, China.

ABSTRACT: The aim of the present study was to analyze an outbreak of hemorrhagic fever with renal syndrome (HFRS), caused by a Hantavirus, in college students in the northern urban area of Xi'an in 2012. The outbreak affected six students and included two deaths. The epidemiological survey revealed that both of the deceased cases were misdiagnosed initially, and treatment was delayed. Furthermore, a higher rodent population density and lower HFRS vaccine coverage were observed in the affected area, which indicates a possible role in the outbreak. Rattus norvegicus (Rn) and Mus musculus (Mm) were the predominant host populations in the area. Genotyping revealed that all HVs from patients and rodents were Hantaan virus (HTNV). Sequence analysis of the S segments revealed that the HTNVs reported in this study had high similarity with strains reported in 2011 and 1985, but these viruses diverged from a strain isolated in 1984 and the HTNV prototype strain 76-118. Detection of anti-HV IgG and amplification of the S segment of HTNV from a non-natural HTNV reservoir indicates that further investigations by increased rodent trapping are necessary.

SUBMITTER: Ma C

PROVIDER: S-EPMC3939468 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Analysis of an outbreak of hemorrhagic fever with renal syndrome in college students in Xi'an, China.

Ma Chaofeng C Wang Zengguo Z Li Shen S Xing Yuan Y Wu Rui R Wei Jing J Nawaz Muhammad M Tian Huaiyu H Xu Bing B Wang Jingjun J Yu Pengbo P

Viruses 20140129 2

The aim of the present study was to analyze an outbreak of hemorrhagic fever with renal syndrome (HFRS), caused by a Hantavirus, in college students in the northern urban area of Xi'an in 2012. The outbreak affected six students and included two deaths. The epidemiological survey revealed that both of the deceased cases were misdiagnosed initially, and treatment was delayed. Furthermore, a higher rodent population density and lower HFRS vaccine coverage were observed in the affected area, which ...[more]

PMID: 24481251

Similar Datasets

Project description:BackgroundHemorrhagic fever with renal syndrome (HFRS) is still attracting public attention because of its outbreak in various cities in China. Predicting future outbreaks or epidemics disease based on past incidence data can help health departments take targeted measures to prevent diseases in advance. In this study, we propose a multistep prediction strategy based on extreme gradient boosting (XGBoost) for HFRS as an extension of the one-step prediction model. Moreover, the fitting and prediction accuracy of the XGBoost model will be compared with the autoregressive integrated moving average (ARIMA) model by different evaluation indicators.MethodsWe collected HFRS incidence data from 2004 to 2018 of mainland China. The data from 2004 to 2017 were divided into training sets to establish the seasonal ARIMA model and XGBoost model, while the 2018 data were used to test the prediction performance. In the multistep XGBoost forecasting model, one-hot encoding was used to handle seasonal features. Furthermore, a series of evaluation indices were performed to evaluate the accuracy of the multistep forecast XGBoost model.ResultsThere were 200,237 HFRS cases in China from 2004 to 2018. A long-term downward trend and bimodal seasonality were identified in the original time series. According to the minimum corrected akaike information criterion (CAIC) value, the optimal ARIMA (3, 1, 0) × (1, 1, 0)12 model is selected. The index ME, RMSE, MAE, MPE, MAPE, and MASE indices of the XGBoost model were higher than those of the ARIMA model in the fitting part, whereas the RMSE of the XGBoost model was lower. The prediction performance evaluation indicators (MAE, MPE, MAPE, RMSE and MASE) of the one-step prediction and multistep prediction XGBoost model were all notably lower than those of the ARIMA model.ConclusionsThe multistep XGBoost prediction model showed a much better prediction accuracy and model stability than the multistep ARIMA prediction model. The XGBoost model performed better in predicting complicated and nonlinear data like HFRS. Additionally, Multistep prediction models are more practical than one-step prediction models in forecasting infectious diseases.

Dataset Information

Analysis of an outbreak of hemorrhagic fever with renal syndrome in college students in Xi'an, China.

Publications

Analysis of an outbreak of hemorrhagic fever with renal syndrome in college students in Xi'an, China.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets