Evidence map›Paper›PMID 40700458›Full record

ArticlePLoS neglected tropical diseases2025

Spatial occurrence-intensity modeling of dengue incidence in southernmost provinces of Thailand.

Apiradee Lim, Lumpoo Ammatawiyanon, Haris Khurram, Phattrawan Tongkumchum, Don McNeil

Abstract read
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Article in PLoS neglected tropical diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Mapping landscape changes to address dengue fever risk in Laos and Thailand.Current research in parasitology & vector-borne diseases · 2026
    Article
4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Apiradee LimDepartment of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Pattani, Thailand.
Lumpoo AmmatawiyanonDepartment of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Pattani, Thailand.
Haris KhurramDepartment of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Pattani, Thailand.ORCID 0000-0003-1814-4742
Phattrawan TongkumchumDepartment of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Pattani, Thailand.
Don McNeilDepartment of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Pattani, Thailand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDengue is currently spreading and is considered a hyperendemic in Thailand. Outbreaks happen almost every year in various provinces of Thailand, especially the four southernmost provinces consistently reporting for more than a decade. This study aimed to explore the spatial distribution and high-risk areas using an occurrence-intensity model at a sub-district level in the four southernmost provinces of Thailand.

methodsThe record of the cases admitted to the hospital and diagnosed as dengue in the 377 sub-districts of four southernmost provinces, Songkhla, Pattani, Yala, and Narathiwat from 2008 to 2020, gender-age wise, were taken from the Office of Disease Prevention and Control, Ministry of Public Health, Thailand. We suggested a methodology based on the occurrence-intensity model to analyze the dengue cases in two steps. At first, the occurrence is determined by using the logistic regression model and considering the variable of interest as a case or not. While at second, the intensity is determined by fitting a log-linear regression model for disease intensity after excluding zeros.

resultsThe results from 78,416 observations revealed that a total number of 68,526 dengue cases were registered from 2008-2020 in all four southernmost provinces. The overall average occurrence rate was 28.3% while the average intensity was 419 per 100,000 population. The occurrence-intensity model gives a much better fit to the data and highlights that the gender-age patterns of occurrence and intensity are different. Occurrence is higher among young adult ages and then declines with age for each gender, whereas intensity is higher in children, young adults, and the elderly for each gender. The sub-districts are in the suburbs of the Songkhla province and sporadically, areas on the border of the Narathiwat province had high occurrence and intensity.

conclusionsThe spatial occurrence and intensity of dengue in sub-districts can provide valuable guidance to identify high-risk areas and monitor the intensity of dengue cases in these areas. This will be useful for healthcare departments in developing effective public health strategies for dengue control.

Indexed as

DengueAdolescentAdultAgedChildChild, PreschoolFemaleHumansIncidenceInfantInfant, NewbornMaleMiddle AgedSpatial AnalysisThailandYoung Adult

Identifiers

PMID40700458
PMCPMC12306782

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