Evidence map›Paper›PMID 32883213›Full record

ArticleBMC infectious diseases2020

Modeling the relative role of human mobility, land-use and climate factors on dengue outbreak emergence in Sri Lanka.

Ying Zhang, Jefferson Riera, Kayla Ostrow, Sauleh Siddiqui, Harendra de Silva, Sahotra Sarkar, Lakkumar Fernando, Lauren Gardner

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In one paragraph

Article in BMC infectious diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

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

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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

8 authors.

Ying ZhangDepartment of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
Jefferson RieraDepartment of Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA.
Kayla OstrowDepartment of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
Sauleh SiddiquiDepartment of Environmental Science, American University, Washington, DC, 20016, USA.
Harendra de SilvaDepartment of Pediatrics, University of Colombo, Colombo, 00900, Sri Lanka.
Sahotra SarkarDepartment of Philosophy, Department of Integrative Biology, University of Texas at Austin, Austin, TX, 78712, USA.
Lakkumar FernandoCentre for Clinical Management of Dengue and Dengue Haemorrhagic Fever, Negombo, 11500, Sri Lanka.
Lauren GardnerDepartment of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA. l.gardner@jhu.edu.ORCID http://orcid.org/0000-0003-1083-3850

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMore than 80,000 dengue cases including 215 deaths were reported nationally in less than 7 months between 2016 and 2017, a fourfold increase in the number of reported cases compared to the average number over 2010-2016. The region of Negombo, located in the Western province, experienced the greatest number of dengue cases in the country and is the focus area of our study, where we aim to capture the spatial-temporal dynamics of dengue transmission.

methodsWe present a statistical modeling framework to evaluate the spatial-temporal dynamics of the 2016-2017 dengue outbreak in the Negombo region of Sri Lanka as a function of human mobility, land-use, and climate patterns. The analysis was conducted at a 1 km × 1 km spatial resolution and a weekly temporal resolution.

resultsOur results indicate human mobility to be a stronger indicator for local outbreak clusters than land-use or climate variables. The minimum daily temperature was identified as the most influential climate variable on dengue cases in the region; while among the set of land-use patterns considered, urban areas were found to be most prone to dengue outbreak, followed by areas with stagnant water and then coastal areas. The results are shown to be robust across spatial resolutions.

conclusionsOur study highlights the potential value of using travel data to target vector control within a region. In addition to illustrating the relative relationship between various potential risk factors for dengue outbreaks, the results of our study can be used to inform where and when new cases of dengue are likely to occur within a region, and thus help more effectively and innovatively, plan for disease surveillance and vector control.

Indexed as

ClimateDengueDisease OutbreaksHumansModels, StatisticalRisk FactorsSri LankaTemperatureTravelClimateDengueHuman mobilityLand-useOutbreaksRisk factorsSpatial-temporal dynamicsStatistical modeling, Sri Lanka

Identifiers

PMID32883213
PMCPMC7469426

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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.