Evidence map›Paper›PMID 38585938›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Unraveling varying spatiotemporal patterns of dengue and associated exposure-response relationships with environmental variables in Southeast Asian countries before and during COVID-19.

Wei Luo, Zhihao Liu, Yiding Ran, Mengqi Li, Yuxuan Zhou, Weitao Hou, Shengjie Lai, Sabrina L Li, Ling Yin

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 8 institutions in 6 countries.

Wei LuoGeoSpatialX Lab, Department of Geography, National University of Singapore, Singapore, Singapore.ORCID 0000-0002-8465-5607
Zhihao LiuSchool of Geosciences, Yangtze University, Wuhan, China.ORCID 0009-0004-4187-406X
Yiding RanGeoSpatialX Lab, Department of Geography, National University of Singapore, Singapore, Singapore.
Mengqi LiDepartment of Geography, University of Zurich, Zurich, Switzerland.
Yuxuan ZhouDepartment of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong Special Administrative Region, China.
Weitao HouSchool of Design and the Built Environment, Curtin University, Perth, Australia.
Shengjie LaiWorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom.
Sabrina L LiSchool of Geography, University of Nottingham, Nottingham, United Kingdom.
Ling YinShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
National University of Singapore · SGChinese Academy of Sciences · CNCity University of Hong Kong · HKCurtin University · AUUniversity of Nottingham · GBUniversity of Southampton · GBUniversity of Zurich · CHYangtze University · CN

Funding

Human mobility models to forecast disease dynamics and the effectiveness of public health interventionsR01AI160780 · NIAID · JOHNS HOPKINS UNIVERSITY · PI CUMMINGS, DEREK A, WESOLOWSKI, AMY · 2021 to 2025
$3.3M
NIAID NIH HHS R01 AI160780
6 · The paper itself

Abstract

The enforcement of COVID-19 interventions by diverse governmental bodies, coupled with the indirect impact of COVID-19 on short-term environmental changes (e.g. plant shutdowns lead to lower greenhouse gas emissions), influences the dengue vector. This provides a unique opportunity to investigate the impact of COVID-19 on dengue transmission and generate insights to guide more targeted prevention measures. We aim to compare dengue transmission patterns and the exposure-response relationship of environmental variables and dengue incidence in the pre- and during-COVID-19 to identify variations and assess the impact of COVID-19 on dengue transmission. We initially visualized the overall trend of dengue transmission from 2012-2022, then conducted two quantitative analyses to compare dengue transmission pre-COVID-19 (2017-2019) and during-COVID-19 (2020-2022). These analyses included time series analysis to assess dengue seasonality, and a Distributed Lag Non-linear Model (DLNM) to quantify the exposure-response relationship between environmental variables and dengue incidence. We observed that all subregions in Thailand exhibited remarkable synchrony with a similar annual trend except 2021. Cyclic and seasonal patterns of dengue remained consistent pre- and during-COVID-19. Monthly dengue incidence in three countries varied significantly. Singapore witnessed a notable surge during-COVID-19, particularly from May to August, with cases multiplying several times compared to pre-COVID-19, while seasonality of Malaysia weakened. Exposure-response relationships of dengue and environmental variables show varying degrees of change, notably in Northern Thailand, where the peak relative risk for the maximum temperature-dengue relationship rose from about 3 to 17, and the max RR of overall cumulative association 0-3 months of relative humidity increased from around 5 to 55. Our study is the first to compare dengue transmission patterns and their relationship with environmental variables before and during COVID-19, showing that COVID-19 has affected dengue transmission at both the national and regional level, and has altered the exposure-response relationship between dengue and the environment.

Indexed as

COVID-19DengueDLNMExposure-Response RelationshipSoutheast Asia(SEA)Time-series analysis

Identifiers

PMID38585938
PMCPMC10996745
OpenAlexW4393188541

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.