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Spatiotemporal analysis and forecasting model of hemorrhagic fever with renal syndrome in mainland China.
Sun, Ling; Zou, Lu-Xi.
Afiliación
  • Sun L; Department of Nephrology,Xuzhou Central Hospital,Medical College of Southeast University,Xuzhou, Jiangsu,China.
  • Zou LX; School of Management,Zhejiang University,Hangzhou,Zhejiang,China.
Epidemiol Infect ; 146(13): 1680-1688, 2018 10.
Article en En | MEDLINE | ID: mdl-30078384
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 1_ASSA2030 Problema de salud: 1_sistemas_informacao_saude / 1_surtos_doencas_emergencias Asunto principal: Brotes de Enfermedades / Fiebre Hemorrágica con Síndrome Renal Tipo de estudio: Incidence_studies / Prognostic_studies Límite: Humans País/Región como asunto: Asia Idioma: En Revista: Epidemiol Infect Asunto de la revista: DOENCAS TRANSMISSIVEIS / EPIDEMIOLOGIA Año: 2018 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 1_ASSA2030 Problema de salud: 1_sistemas_informacao_saude / 1_surtos_doencas_emergencias Asunto principal: Brotes de Enfermedades / Fiebre Hemorrágica con Síndrome Renal Tipo de estudio: Incidence_studies / Prognostic_studies Límite: Humans País/Región como asunto: Asia Idioma: En Revista: Epidemiol Infect Asunto de la revista: DOENCAS TRANSMISSIVEIS / EPIDEMIOLOGIA Año: 2018 Tipo del documento: Article País de afiliación: China
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