ArticleBMC infectious diseases2019
Spatiotemporal analysis of the dengue outbreak in Guangdong Province, China.
Article in BMC infectious diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- The Impact of Extreme Weather on Dengue Fever in Guangzhou, China: A Zero-Inflated Negative Binomial Spatial Lag Analysis.GeoHealth · 2025Article
- Fine-Scale Risk Mapping for Dengue Vector Using Spatial Downscaling in Intra-Urban Areas of Guangzhou, China.Insects · 2025Article
- Dynamics of vector competence for dengue virus type 2 in rural and urban populations of Aedes albopictus: implications for infectious disease control.Parasites & vectors · 2025Article
- Spatial patterns and clustering of dengue incidence in Mexico: Analysis of Moran's index across 2,471 municipalities from 2022 to 2024.PloS one · 2025Article
- Endemic-epidemic modelling of school closure to prevent spread of COVID-19 in Switzerland.BMC infectious diseases · 2024Article
- High-risk spatiotemporal patterns of leprosy in the southeastern region of Yunnan province from 2010 to 2022: an analysis at the township level.BMC public health · 2024Article
- Evolution of COVID-19 dynamics in Guangdong Province, China: an endemic-epidemic modeling study.Archives of public health = Archives belges de sante publique · 2024Article
- Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand.BMC medical research methodology · 2024Article
- Using surveillance data for early warning modelling of highly pathogenic avian influenza in Europe reveals a seasonal shift in transmission, 2016-2022.Scientific reports · 2023Article
- Spatio-temporal dynamic of the COVID-19 epidemic and the impact of imported cases in Rwanda.BMC public health · 2023Article
- Article
- Modelling the impact of antimicrobial use and external introductions on commensal E. coli colistin resistance in small-scale chicken farms of the Mekong delta of Vietnam.Transboundary and emerging diseases · 2022Article
- Spatio-temporal evolution and trend prediction of the incidence of Class B notifiable infectious diseases in China: a sample of statistical data from 2007 to 2020.BMC public health · 2022Article
- Knockdown resistance mutations distribution and characteristics ofFrontiers in cellular and infection microbiology · 2022Article
- Integrating Spatial Modelling and Space-Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan.International journal of environmental research and public health · 2021Article
- Are regions equal in adversity? A spatial analysis of spread and dynamics of COVID-19 in Europe.The European journal of health economics : HEPAC : health economics in prevention and care · 2021Article
- Determination of Factors Affecting Dengue Occurrence in Representative Areas of China: A Principal Component Regression Analysis.Frontiers in public health · 2020Article
- Long-Term Protection Elicited by a DNA Vaccine Candidate Expressing the prM-E Antigen of Dengue Virus Serotype 3 in Mice.Frontiers in cellular and infection microbiology · 2020Article
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6 authors.
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Abstract
backgroundDengue is becoming a major public health concern in Guangdong (GD) Province of China. The problem was highlighted in 2014 by an unprecedented explosive outbreak, where the number of cases was larger than the total cases in previous 30 years. The present study aimed to clarify the spatial and temporal patterns of this dengue outbreak.
methodsBased on the district/county-level epidemiological, demographic and geographic data, we first used Moran's I statistics and Spatial scan method to uncover spatial autocorrelation and clustering of dengue incidence, and then estimated the spatial distributions of mosquito ovitrap index (MOI) by using inverse distance weighting. We finally employed a multivariate time series model to quantitatively decompose dengue cases into endemic, autoregressive and spatiotemporal components.
resultsThe results indicated that dengue incidence was highly spatial-autocorrelated with the inclination of clustering and nonuniformity. About 12 dengue clusters were discovered around Guangzhou and Foshan with significant differences by district/county, where the most likely cluster with the largest relative risk located in central Guangzhou in October. Three significant high-MOI areas were observed around Shaoguan, Qingyuan, Shanwei and Guangzhou. It was further found the districts in Guagnzhou and Foshan were prone to local autoregressive transmission, and most region in southern and central GD exhibited higher endemic components. Moreover, nearly all of districts/counties (especially the urban area) have cases that were infected in adjacent regions.
conclusionsThe study can help to clarify the heterogeneity and the associations of dengue transmission in space and time, and thus provide useful information for public health authorities to plan dengue control strategies.
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