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Spatial and temporal patterns of dengue infections in Timor-Leste, 2005–2013

Overview of attention for article published in Parasites & Vectors, January 2018
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Title
Spatial and temporal patterns of dengue infections in Timor-Leste, 2005–2013
Published in
Parasites & Vectors, January 2018
DOI 10.1186/s13071-017-2588-4
Pubmed ID
Authors

Kinley Wangdi, Archie C. A. Clements, Tai Du, Susana Vaz Nery

Abstract

Dengue remains an important public health problem in Timor-Leste, with several major epidemics occurring over the last 10 years. The aim of this study was to identify dengue clusters at high geographical resolution and to determine the association between local environmental characteristics and the distribution and transmission of the disease. Notifications of dengue cases that occurred from January 2005 to December 2013 were obtained from the Ministry of Health, Timor-Leste. The population of each suco (the third-level administrative subdivision) was obtained from the Population and Housing Census 2010. Spatial autocorrelation in dengue incidence was explored using Moran's I statistic, Local Indicators of Spatial Association (LISA), and the Getis-Ord statistics. A multivariate, Zero-Inflated, Poisson (ZIP) regression model was developed with a conditional autoregressive (CAR) prior structure, and with posterior parameters estimated using Bayesian Markov chain Monte Carlo (MCMC) simulation with Gibbs sampling. The analysis used data from 3206 cases. Dengue incidence was highly seasonal with a large peak in January. Patients ≥ 14 years were found to be 74% [95% credible interval (CrI): 72-76%] less likely to be infected than those < 14 years, and females were 12% (95% CrI: 4-21%) more likely to suffer from dengue as compared to males. Dengue incidence increased by 0.7% (95% CrI: 0.6-0.8%) for a 1 °C increase in mean temperature; and 47% (95% CrI: 29-59%) for a 1 mm increase in precipitation. There was no significant residual spatial clustering after accounting for climate and demographic variables. Dengue incidence was highly seasonal and spatially clustered, with positive associations with temperature, precipitation and demographic factors. These factors explained the observed spatial heterogeneity of infection.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 80 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 15 19%
Researcher 12 15%
Student > Bachelor 10 13%
Student > Ph. D. Student 6 8%
Student > Doctoral Student 4 5%
Other 11 14%
Unknown 22 28%
Readers by discipline Count As %
Medicine and Dentistry 14 18%
Nursing and Health Professions 8 10%
Social Sciences 6 8%
Agricultural and Biological Sciences 4 5%
Biochemistry, Genetics and Molecular Biology 4 5%
Other 19 24%
Unknown 25 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 July 2018.
All research outputs
#11,779,281
of 13,272,830 outputs
Outputs from Parasites & Vectors
#3,028
of 3,517 outputs
Outputs of similar age
#235,871
of 270,611 outputs
Outputs of similar age from Parasites & Vectors
#1
of 2 outputs
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