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Spatiotemporal analysis and forecasting model of hemorrhagic fever with renal syndrome in mainland China

  • Ling Sun (a1) and Lu-Xi Zou (a2)

Abstract

Hemorrhagic fever with renal syndrome (HFRS) caused by hantaviruses is a serious public health problem in China, accounting for 90% of HFRS cases reported globally. In this study, we applied geographical information system (GIS), spatial autocorrelation analyses and a seasonal autoregressive-integrated moving average (SARIMA) model to describe and predict HFRS epidemic with the objective of monitoring and forecasting HFRS in mainland China. Chinese HFRS data from 2004 to 2016 were obtained from National Infectious Diseases Reporting System (NIDRS) database and Chinese Centre for Disease Control and Prevention (CDC). GIS maps were produced to detect the spatial distribution of HFRS cases. The Moran's I was adopted in spatial global autocorrelation analysis to identify the integral spatiotemporal pattern of HFRS outbreaks, while the local Moran's Ii was performed to identify ‘hotspot’ regions of HFRS at province level. A fittest SARIMA model was developed to forecast HFRS incidence in the year 2016, which was selected by Akaike information criterion and Ljung–Box test. During 2004–2015, a total of 165 710 HFRS cases were reported with the average annual incidence at province level ranged from 0 to 13.05 per 100 000 persons. Global Moran's I analysis showed that the HFRS outbreaks presented spatially clustered distribution, with the degree of cluster gradually decreasing from 2004 to 2009, then turned out to be randomly distributed and reached lowest point in 2012. Local Moran's Ii identified that four provinces in northeast China contributed to a ‘high–high’ cluster as a traditional epidemic centre, and Shaanxi became another HFRS ‘hotspot’ region since 2011. The monthly incidence of HFRS decreased sharply from 2004 to 2009 in mainland China, then increased markedly from 2010 to 2012, and decreased again since 2013, with obvious seasonal fluctuations. The SARIMA ((0,1,3) × (1,0,1)12) model was the most fittest forecasting model for the dataset of HFRS in mainland China. The spatiotemporal distribution of HFRS in mainland China varied in recent years; together with the SARIMA forecasting model, this study provided several potential decision supportive tools for the control and risk-management plan of HFRS in China.

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Copyright

Corresponding author

Author for correspondence: Ling Sun, E-mail: slpku@163.com

References

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1.Li, SJ et al. (2014) Spatiotemporal heterogeneity analysis of hemorrhagic fever with renal syndrome in China using geographically weighted regression models. International Journal of Environmental Research & Public Health 11, 1212912147.
2.Zhang, YZ et al. (2010) Hantavirus infections in humans and animals, China. Emerging Infectious Diseases 16, 11951203.
3.Zou, LX, Chen, MJ and Sun, L (2016) Haemorrhagic fever with renal syndrome: literature review and distribution analysis in China. International Journal of Infectious Diseases 43, 95100.
4.Hardestam, J et al. (2007) Ex vivo stability of the rodent-borne Hantaan virus in comparison to that of arthropod-borne members of the Bunyaviridae family. Applied & Environmental Microbiology 73, 25472551.
5.Pedrosa, PBS and Cardoso, TAO (2011) Viral infections in workers in hospital and research laboratory settings: a comparative review of infection modes and respective biosafety aspects. International Journal of Infectious Diseases 15, E366E376.
6.Reusken, C and Heyman, P (2013) Factors driving hantavirus emergence in Europe. Current Opinion in Virology 3, 9299.
7.Xiao, H et al. (2013) Investigating the effects of food available and climatic variables on the animal host density of hemorrhagic fever with renal syndrome in Changsha, China. PLoS ONE 8, e61536.
8.Fang, LQ et al. (2010) Spatiotemporal trends and climatic factors of hemorrhagic fever with renal syndrome epidemic in Shandong Province, China. PLoS Neglected Tropical Diseases 4, e789. doi: 10.1371/journal.pntd.0000789.
9.Lin, HL et al. (2007) Analysis of the geographic distribution of HFRS in Liaoning Province between 2000 and 2005. BMC Public Health 7, 207. doi: 10.1186/1471-2458-7-207.
10.Wu, W et al. (2011) Clusters of spatial, temporal, and space-time distribution of hemorrhagic fever with renal syndrome in Liaoning Province, Northeastern China. BMC Infectious Diseases 11, 229. doi: 10.1186/1471-2334-11-229.
11.Ge, L et al. (2016) Spatio-temporal pattern and influencing factors of hemorrhagic fever with renal syndrome (HFRS) in Hubei Province (China) between 2005 and 2014. PLoS ONE 11, e0167836. doi: 10.1371/journal.pone.0167836.
12.Cliff, AD and Ord, JK (1973) Spatial autocorrelation. Trends in Ecology & Evolution 14, 196.
13.Li, RZ et al. (2017) Epidemiological characteristics and spatial-temporal clusters of mumps in Shandong Province, China, 2005–2014. Scientific Reports 7, 46328. doi: 10.1038/srep46328.
14.Ge, E et al. (2016) Spatial and temporal analysis of tuberculosis in Zhejiang Province, China, 2009–2012. Infectious Diseases of Poverty 5, 11. doi: 10.1186/s40249-016-0104-2.
15.Moran, PAP (1948) The interpretation of statistical maps. Journal of the Royal Statistical Society 10, 243251.
16.Anselin, L (1995) Local indicators of spatiala association – LISA. Geographical Analysis 27, 93115.
17.Brown, TT, Wood, JD and Griffith, DA (2017) Using spatial autocorrelation analysis to guide mixed methods survey sample design decisions. Journal of Mixed Methods Research 11, 394414.
18.Azeez, A et al. (2016) Seasonality and trend forecasting of tuberculosis prevalence data in Eastern Cape, South Africa, using a hybrid model. International Journal of Environmental Research and Public Health 13, 757.
19.Fernandez-Gonzalez, M et al. (2016) Prediction of biological sensors appearance with ARIMA models as a tool for integrated pest management protocols. Annals of Agricultural and Environmental Medicine 23, 129137.
20.Ansari, H et al. (2015) Predicting CCHF incidence and its related factors using time-series analysis in the southeast of Iran: comparison of SARIMA and Markov switching models. Epidemiology and Infection 143, 839850.
21.Allard, R (1998) Use of time-series analysis in infectious disease surveillance. Bulletin of the World Health Organization 76, 327333.
22.Wei, WD et al. (2016) Application of a combined model with autoregressive integrated moving average (ARIMA) and generalized regression neural network (GRNN) in forecasting hepatitis incidence in Heng County, China. PLoS ONE 11, e0156768. doi: 10.1371/journal.pone.0156768.
23.Zhang, H et al. (2017) Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan, China. Journal of the Air & Waste Management Association 67, 776788.
24.Ljung, GM and Box, GEP (1978) Measure of lack of fit in time-series models. Biometrika 65, 297303.
25.Anwar, MY et al. (2016) Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence. Malaria Journal 15, 566. doi: 10.1186/s12936-016-1602-1.
26.Wang, KW et al. (2017) Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network. Epidemiology and Infection 145, 11181129.
27.Liu, L et al. (2016) Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model. Epidemiology and Infection 144, 144151.
28.Bi, P et al. (1998) Seasonal rainfall variability, the incidence of hemorrhagic fever with renal syndrome, and prediction of the disease in low-lying areas of China. American Journal of Epidemiology 148, 276281.
29.Khalil, H et al. (2014) The importance of bank vole density and rainy winters in predicting nephropathia epidemica incidence in Northern Sweden. PLoS ONE 9, e111663. doi: 10.1371/journal.pone.0111663.
30.Zhang, S et al. (2014) Epidemic characteristics of hemorrhagic fever with renal syndrome in China, 2006–2012. BMC Infectious Diseases 14, 384. doi: 10.1186/1471-2334-14-384.
31.Luo, ZZ (2002) Progress of epidemiology and vaccine research of epidemic hemorrhagic fever. Chinese Journal of Disease Control & Prevention 6, 58.
32.Liu, W et al. (2000) Safety and immunogenicity of inactivated bivalent EHF vaccine in humans. Chinese Journal of Epidemiology 21, 445447.
33.Chen, HX et al. (2000) Preventive effects of three kinds of inactive vaccines against epidemic hemorrhagic fever (EHF) after 5 years of vaccination. Chinese Journal of Epidemiology 21, 347348.
34.Liu, XD et al. (2011) Temporal trend and climate factors of hemorrhagic fever with renal syndrome epidemic in Shenyang City, China. BMC Infectious Diseases 11, 331. doi: 10.1186/1471-2334-11-331.
35.Zhang, WY et al. (2010) Climate variability and hemorrhagic fever with renal syndrome transmission in Northeastern China. Environmental Health Perspectives 118, 915920.
36.Luo, L et al. (2017) Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. BMC Health Services Research 17, 469. doi: 10.1186/s12913-017-2407-9.
37.Ke, GB et al. (2016) Epidemiological analysis of hemorrhagic fever with renal syndrome in China with the seasonal-trend decomposition method and the exponential smoothing model. Scientific Reports 6, 39350. doi: 10.1038/srep39350.
38.Zhang, YH et al. (2014) The epidemic characteristics and changing trend of hemorrhagic fever with renal syndrome in Hubei Province, China. PLoS ONE 9, e92700. doi: 10.1371/journal.pone.0092700.

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Spatiotemporal analysis and forecasting model of hemorrhagic fever with renal syndrome in mainland China

  • Ling Sun (a1) and Lu-Xi Zou (a2)

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