Evidence map›Paper›PMID 31164093›Full record

ArticleBMC infectious diseases2019

Spatiotemporal analysis of the dengue outbreak in Guangdong Province, China.

Guanghu Zhu, Jianpeng Xiao, Tao Liu, Bing Zhang, Yuantao Hao, Wenjun Ma

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Article
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  7. Evolution of COVID-19 dynamics in Guangdong Province, China: an endemic-epidemic modeling study.Archives of public health = Archives belges de sante publique · 2024
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  14. Knockdown resistance mutations distribution and characteristics ofFrontiers in cellular and infection microbiology · 2022
    Article
  15. Article
  16. 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 · 2021
    Article
  17. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Guanghu ZhuSchool of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, 541004, China.
Jianpeng XiaoGuangdong Provincial Institution of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.
Tao LiuGuangdong Provincial Institution of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.
Bing ZhangGuangdong Provincial Institution of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.
Yuantao HaoDepartment of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China.
Wenjun MaGuangdong Provincial Institution of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China. mawj@gdiph.org.cn.ORCID http://orcid.org/0000-0002-1416-1476

Funding

Guangdong Provincial Medical Research Foundation A2017474National Natural Science Foundation of China 11661026National Natural Science Foundation of China 81773497Natural Science Foundation of Guangdong Province 2017A030313699Natural Science Foundation of Guangxi Province 2017GXNSFAA198235the National Key R\&D Program of Ministry of Science and Technology of China No. 2018YFB0505500 and 2018YFB0505503
6 · The paper itself

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.

Indexed as

Disease OutbreaksAnimalsChinaCluster AnalysisCulicidaeDengueHumansIncidenceInsect VectorsRetrospective StudiesSpatial AnalysisSpatio-Temporal AnalysisClusterDengueSpatiotemporal transmission

Identifiers

PMID31164093
PMCPMC6549279

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.