Dataset Information

Modeling and predicting hemorrhagic fever with renal syndrome trends based on meteorological factors in Hu County, China.

ABSTRACT:

Background

Hu County is a serious hemorrhagic fever with renal syndrome (HFRS) epidemic area, with notable fluctuation of the HFRS epidemic in recent years. This study aimed to explore the optimal model for HFRS epidemic prediction in Hu.

Methods

Three models were constructed and compared, including a generalized linear model (GLM), a generalized additive model (GAM), and a principal components regression model (PCRM). The fitting and predictive adjusted R2 of each model were calculated. Ljung-Box Q tests for fitted and predicted residuals of each model were conducted. The study period was stratified into before (1971-1993) and after (1994-2012) vaccine implementation epochs to avoid the confounding factor of vaccination.

Results

The autocorrelation of fitted and predicted residuals of the GAM in the two epochs were not significant (Ljung-Box Q test, P>.05). The adjusted R2 for the predictive abilities of the GLM, GAM, and PCRM were 0.752, 0.799, and 0.665 in the early epoch, and 0.669, 0.756, and 0.574 in the recent epoch. The adjusted R2 values of the three models were lower in the early epoch than in the recent epoch.

Conclusions

GAM is superior to GLM and PCRM for monthly HFRS case number prediction in Hu County. A shift in model reliability coincident with vaccination implementation demonstrates the importance of vaccination in HFRS control and prevention.

SUBMITTER: Xiao D

PROVIDER: S-EPMC4395290 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Modeling and predicting hemorrhagic fever with renal syndrome trends based on meteorological factors in Hu County, China.

Xiao Dan D Wu Kejian K Tan Xin X Le Jing J Li Haitao H Yan Yongping Y Xu Zhikai Z

PloS one 20150413 4

<h4>Background</h4>Hu County is a serious hemorrhagic fever with renal syndrome (HFRS) epidemic area, with notable fluctuation of the HFRS epidemic in recent years. This study aimed to explore the optimal model for HFRS epidemic prediction in Hu.<h4>Methods</h4>Three models were constructed and compared, including a generalized linear model (GLM), a generalized additive model (GAM), and a principal components regression model (PCRM). The fitting and predictive adjusted R2 of each model were calc ...[more]

PMID: 25875211

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Project description:BackgroundHemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by Hantaviruses. It is endemic in all 31 provinces, autonomous regions, and metropolitan areas in mainland China where human cases account for 90% of the total global cases. Shandong Province is among the most serious endemic areas. HFRS cases in Shandong Province were first reported in Yutai County in 1968. Since then, the disease has spread across the province, and as of 2005, all 111 counties were reported to have local human infections. However, causes underlying such rapid spread and wide distribution remain less well understood.Methods and findingsHere we report a spatiotemporal analysis of human HFRS cases in Shandong using data spanning 1973 to 2005. Seasonal incidence maps and velocity vector maps were produced to analyze the spread of HFRS over time in Shandong Province, and a panel data analysis was conducted to explore the association between HFRS incidence and climatic factors. Results show a rapid spread of HFRS from its epicenter in Rizhao, Linyi, Weifang Regions in southern Shandong to north, east, and west parts of the province. Based on seasonal shifts of epidemics, three epidemic phases were identified over the 33-year period. The first phase occurred between 1973 and 1982 during which the foci of HFRS was located in the south Shandong and the epidemic peak occurred in the fall and winter, presenting a seasonal characteristic of Hantaan virus (HTNV) transmission. The second phase between 1983 and 1985 was characterized by northward and westward spread of HFRS foci, and increases in incidence of HFRS in both fall-winter and spring seasons. The human infections in the spring reflected a characteristic pattern of Seoul virus (SEOV) transmission. The third phase between 1986 and 2005 was characterized by the northeast spread of the HFRS foci until it covered all counties, and the HFRS incidence in the fall-winter season decreased while it remained high in the spring. In addition, our findings suggest that precipitation, humidity, and temperature are major environmental variables that are associated with the seasonal variation of HFRS incidence in Shandong Province.ConclusionsThe spread of HFRS in Shandong Province may have been accompanied by seasonal shifts of HTNV-dominated transmission to SEOV-dominated transmission over the past three decades. The variations in HFRS incidence were significantly associated with local precipitation, humidity, and temperature.

Project description:BackgroundHemorrhagic fever with renal syndrome (HFRS), a rodent-borne disease, is a severe public health threat. Previous studies have discovered the influence of meteorological factors on HFRS incidence, while few studies have concentrated on the stratified analysis of delayed effects and interaction effects of meteorological factors on HFRS.ObjectiveHuludao City is a representative area in north China that suffers from HFRS with primary transmission by Rattus norvegicus. This study aimed to evaluate the climate factors of lag, interaction, and stratified effects of meteorological factors on HFRS incidence in Huludao City.MethodsOur researchers collected meteorological data and epidemiological data of HFRS cases in Huludao City during 2007-2018. First, a distributed lag nonlinear model (DLNM) for a maximum lag of 16 weeks was developed to assess the respective lag effect of temperature, precipitation, and humidity on HFRS incidence. We then constructed a generalized additive model (GAM) to explore the interaction effect between temperature and the other two meteorological factors on HFRS incidence and the stratified effect of meteorological factors.ResultsDuring the study period, 2751 cases of HFRS were reported in Huludao City. The incidence of HFRS showed a seasonal trend and peak times from February to May. Using the median WAT, median WTP, and median WARH as the reference, the results of DLNM showed that extremely high temperature (97.5th percentile of WAT) had significant associations with HFRS at lag week 15 (RR = 1.68, 95% CI: 1.04-2.74) and lag week 16 (RR = 2.80, 95% CI: 1.31-5.95). Under the extremely low temperature (2.5th percentile of WAT), the RRs of HFRS infection were significant at lag week 5 (RR = 1.28, 95% CI: 1.01-1.67) and lag 6 weeks (RR = 1.24, 95% CI: 1.01-1.57). The RRs of relative humidity were statistically significant at lag week 10 (RR = 1.19, 95% CI: 1.00-1.43) and lag week 11 (RR = 1.24, 95% CI: 1.02-1.50) under extremely high relative humidity (97.5th percentile of WARH); however, no statistically significance was observed under extremely low relative humidity (2.5th percentile of WARH). The RRs were significantly high when WAT was -10 degrees Celsius (RR = 1.34, 95% CI: 1.02-1.76), -9 degrees Celsius (1.37, 95% CI: 1.04-1.79), and -8 degrees Celsius (RR = 1.34, 95% CI: 1.03-1.75) at lag week 5 and more than 23 degrees Celsius after 15 weeks. Interaction and stratified analyses showed that the risk of HFRS infection reached its highest when both temperature and precipitation were at a high level.ConclusionsOur study indicates that meteorological factors, including temperature and humidity, have delayed effects on the occurrence of HFRS in the study area, and the effect of temperature can be modified by humidity and precipitation. Public health professionals should pay more attention to HFRS control when the weather conditions of high temperature with more substantial precipitation and 15 weeks after the temperature is higher than 23 degrees Celsius.

Project description:BackgroundHemorrhagic fever with renal syndrome (HFRS) is an important infectious disease caused by different species of hantaviruses. As a rodent-borne disease with a seasonal distribution, external environmental factors including climate factors may play a significant role in its transmission. The city of Shenyang is one of the most seriously endemic areas for HFRS. Here, we characterized the dynamic temporal trend of HFRS, and identified climate-related risk factors and their roles in HFRS transmission in Shenyang, China.MethodsThe annual and monthly cumulative numbers of HFRS cases from 2004 to 2009 were calculated and plotted to show the annual and seasonal fluctuation in Shenyang. Cross-correlation and autocorrelation analyses were performed to detect the lagged effect of climate factors on HFRS transmission and the autocorrelation of monthly HFRS cases. Principal component analysis was constructed by using climate data from 2004 to 2009 to extract principal components of climate factors to reduce co-linearity. The extracted principal components and autocorrelation terms of monthly HFRS cases were added into a multiple regression model called principal components regression model (PCR) to quantify the relationship between climate factors, autocorrelation terms and transmission of HFRS. The PCR model was compared to a general multiple regression model conducted only with climate factors as independent variables.ResultsA distinctly declining temporal trend of annual HFRS incidence was identified. HFRS cases were reported every month, and the two peak periods occurred in spring (March to May) and winter (November to January), during which, nearly 75% of the HFRS cases were reported. Three principal components were extracted with a cumulative contribution rate of 86.06%. Component 1 represented MinRH0, MT1, RH1, and MWV1; component 2 represented RH2, MaxT3, and MAP3; and component 3 represented MaxT2, MAP2, and MWV2. The PCR model was composed of three principal components and two autocorrelation terms. The association between HFRS epidemics and climate factors was better explained in the PCR model (F = 446.452, P < 0.001, adjusted R2 = 0.75) than in the general multiple regression model (F = 223.670, P < 0.000, adjusted R2 = 0.51).ConclusionThe temporal distribution of HFRS in Shenyang varied in different years with a distinctly declining trend. The monthly trends of HFRS were significantly associated with local temperature, relative humidity, precipitation, air pressure, and wind velocity of the different previous months. The model conducted in this study will make HFRS surveillance simpler and the control of HFRS more targeted in Shenyang.

Dataset Information

Modeling and predicting hemorrhagic fever with renal syndrome trends based on meteorological factors in Hu County, China.

Background

Methods

Results

Conclusions

Publications

Modeling and predicting hemorrhagic fever with renal syndrome trends based on meteorological factors in Hu County, China.

Similar Datasets

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