Evidence map›Paper›PMID 40555387›Full record

ArticleJournal of the Royal Society, Interface2025

A novel approach to quantify the optimal range and causal effect of rainfall on vector-borne diseases: a case study of dengue epidemics.

Yan Liu, Xia Wang, Biao Tang

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

3 authors.

Yan LiuSchool of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China.ORCID 0009-0001-5754-5904
Xia WangSchool of Mathematics and Statistics, Shaanxi Normal University, Xi'an, Shaanxi 710119, People's Republic of China.ORCID 0000-0001-9317-4824
Biao TangSchool of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China.ORCID 0000-0002-0418-5773

Funding

National Natural Science Foundation of China
6 · The paper itself

Abstract

The quantitative relationship between rainfall and the frequency of dengue outbreaks remains poorly understood, with rainfall's contribution often overlooked or mischaracterized. Taking Guangzhou as the case, we develop a dynamic model to identify the optimal rainfall range for mosquito population development. Using mosquito surveillance and meteorological data, we estimate the optimal weekly rainfall range as 131.2-212.8 mm. Additionally, we use the distributed lag nonlinear model to analyse the correlation between rainfall and local cases, providing cross-validation. We consequently introduce a novel rainfall index to quantify its causal effects on dengue burden and use a hurdle regularization model to assess the interplay between imported cases, rainfall and temperature in shaping dengue outbreaks. The cases in 2014 and 2015 are predicted by fitting the model to epidemic data between 2006 and 2013. Our proposed rainfall index outperforms existing indices in both model fitting and prediction accuracy. Additionally, switching 2014 and 2015 index values shows a significantly larger 2015 outbreak and smaller 2014 wave, unlike adjustments to temperature or imported case data, highlighting rainfall's dominant role in shaping Guangzhou dengue outbreaks. Although the parameters and the results are restricted to Guangzhou, the fundamental framework can be widely applied to any other region by including the specific data.

Indexed as

AedesDengueEpidemicsModels, BiologicalMosquito VectorsRainAnimalsChinaDisease OutbreaksHumanscausal effectsdenguemosquitoesnonlinear and non-monotonic patternrainfall

Identifiers

PMID40555387
PMCPMC12187404

What OpenQuestion holds

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Registered trials

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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.