Evidence map›Paper›PMID 41274877›Full record

ArticleNature communications2025

Climate variation and serotype competition drive dengue outbreak dynamics in Singapore.

Emilie Finch, Chia-Chen Chang, Adam Kucharski, Shuzhen Sim, Lee-Ching Ng, Rachel Lowe

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
  4. Review
  5. Review
  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

6 authors.

Emilie FinchCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK. ef507@cam.ac.uk.ORCID http://orcid.org/0000-0002-8225-7533
Chia-Chen ChangNational Environment Agency, Singapore, Singapore.
Adam KucharskiCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
Shuzhen SimNational Environment Agency, Singapore, Singapore.
Lee-Ching NgNational Environment Agency, Singapore, Singapore.ORCID http://orcid.org/0000-0003-2413-3863
Rachel LoweCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK. rachel.lowe@bsc.es.ORCID http://orcid.org/0000-0003-3939-7343

Funding

RCUK | Medical Research Council (MRC) MR/N013638/1Wellcome Trust
6 · The paper itself

Abstract

Dengue poses a rapidly increasing threat to global health, with Southeast Asia among the most affected regions. Climate-informed early warning systems can help mitigate outbreaks; however, prediction of large outbreaks with sufficient lead time remains a challenge. In this study, we quantified the role of climatic variation and serotype competition in shaping dengue risk in Singapore using over 20 years of weekly case data. We integrated these insights into a forecasting framework capable of predicting dengue outbreaks up to two months ahead and generated counterfactual projections to assess the impact of novel interventions, such as Wolbachia. While a climate-informed model improved predictive power by 54% compared to a seasonal baseline, including serotype information increased this to 60%, better explaining interannual variation in dengue incidence. By incorporating serotype competition as a proxy for population immunity, this work advances the field of climate-informed dengue prediction and demonstrates the value of long-term virus surveillance.

Indexed as

ClimateDengueDengue VirusDisease OutbreaksAedesAnimalsClimate ChangeHumansIncidenceSeasonsSerogroupSingaporeWolbachia

Identifiers

PMID41274877
PMCPMC12727700

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.