Evidence map›Paper›PMID 31931886›Full record

ArticleParasites & vectors2020

Does Bangkok have a central role in the dengue dynamics of Thailand?

Zhiwei Xu, Hilary Bambrick, Puntani Pongsumpun, I Ming Tang, Laith Yakob, Gregor Devine, Francesca D Frentiu, Gail Williams, Wenbiao Hu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Parasites & vectors, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Observational
  4. Article
  5. Article
  6. Article
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

9 authors.

Zhiwei XuSchool of Public Health and Social Work, Queensland University of Technology, Brisbane, 4059, Australia.
Hilary BambrickSchool of Public Health and Social Work, Queensland University of Technology, Brisbane, 4059, Australia.
Puntani PongsumpunDepartment of Mathematics, Faculty of Science, King Mongkut's Institute of Technology Ladkrabang, Bangkok, 10520, Thailand.
I Ming TangComputational & Applied Science for Smart Innovation Cluster (CLASSIC), Faculty of Science, King Mongkut's University of Technology Thonburi, Bangkok, 10140, Thailand.
Laith YakobDepartment of Disease Control, London School of Hygiene and Tropical Medicine, London, WC1H 9SH, UK.
Gregor DevineMosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, 4006, Australia.
Francesca D FrentiuInstitute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, 4059, Australia.
Gail WilliamsSchool of Public Health, Faculty of Medicine, University of Queensland, Brisbane, 4006, Australia.
Wenbiao HuSchool of Public Health and Social Work, Queensland University of Technology, Brisbane, 4059, Australia. w2.hu@qut.edu.au.

Funding

National Health and Medical Research Council, Australia APP 1138622
6 · The paper itself

Abstract

backgroundBangkok plays a central role in the commerce of Thailand. This study aimed to characterize the district-level spatial-temporal patterns of dengue in Thailand and explore if a dengue peak in Bangkok led the peaks of dengue in other Thai provinces.

methodsMonthly dengue data at district level in Thailand from January 2004 to December 2017 were obtained and used to assess the spatial and seasonal patterns of dengue in Thailand. As our seasonal decomposition and cross-correlation analyses showed that dengue in Bangkok peaked in November, which was a few months after the dengue peak in most other provinces, we used a time-series generalized linear model to explore if there was another province in which the dengue case number was most predictive of dengue case numbers in other Thai provinces.

resultsThe highest district-level annual dengue incidence rates (per 10,000) in the three time periods (i.e. 2004-2008, 2009-2013 and 2014-2017) were 58.08 (Samphanthawong), 85.93 (Mueang Krabi), and 66.60 (Mae Sariang), respectively. Dengue incidence rates in the western part of Northern Thailand, southern part of Central Thailand, southern part of Eastern Thailand, and Southern Thailand were higher than in other regions. Dengue in most districts of Thailand peaked in June, July or August, but dengue peaks in all districts of Bangkok occurred in November. The number of dengue cases in Nakhon Ratchasima was most predictive of the number of dengue cases in other provinces in Thailand by a one-month lag.

conclusionsOur results suggest that the dengue peak in Bangkok did not lead the peaks of dengue in other Thai provinces. Future research exploring how changes in socio-ecological factors (e.g. road network and climate factors) in Nakhon Ratchasima have affected the transmission of dengue in Thailand might shed some new light on the prevention and control of dengue.

Indexed as

AnimalsDengueHumansIncidenceSeasonsSociological FactorsTemperatureThailandBangkokDengueNakhon RatchasimaThailand

Identifiers

PMID31931886
PMCPMC6958813

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.