Evidence map›Paper›PMID 39251932›Full record

ArticleBMC infectious diseases2024

Determining dengue infection risk in the Colombo district of Sri Lanka by inferencing the genetic parameters of Aedes mosquitoes.

Piyumi Chathurangika, Lakmini S Premadasa, S S N Perera, Kushani De Silva

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Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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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

4 authors.

Piyumi ChathurangikaResearch & Development Centre for Mathematical Modeling, Department of Mathematics, Faculty of Science, University of Colombo, 00030, Colombo, Sri Lanka.
Lakmini S PremadasaInternational Center for the Advancement of Research and Education (I·CARE), Texas Biomedical Research Institute, San Antonio, 78227, TX, USA.
S S N PereraResearch & Development Centre for Mathematical Modeling, Department of Mathematics, Faculty of Science, University of Colombo, 00030, Colombo, Sri Lanka.
Kushani De SilvaResearch & Development Centre for Mathematical Modeling, Department of Mathematics, Faculty of Science, University of Colombo, 00030, Colombo, Sri Lanka. kdesilva@maths.cmb.ac.lk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFor decades, dengue has posed a significant threat as a viral infectious disease, affecting numerous human lives globally, particularly in tropical regions, yet no cure has been discovered. The genetic trait of vector competence in Aedes mosquitoes, which facilitates dengue transmission, is difficult to measure and highly sensitive to environmental changes.

methodsIn this study we attempt, for the first time in a non-laboratory setting, to quantify the vector competence of Aedes mosquitoes assuming its homogeneity across both species; aegypti and albopictus and across the four Dengue serotypes. Estimating vector competence in relation to varying rainfall patterns was focused in this study to showcase the changes in this vector trait with respect to environmental variables. We quantify it using an existing mathematical model originally developed for malaria in a Bayesian inferencing setup. We conducted this study in the Colombo district of Sri Lanka where the highest number of human populations are threatened with dengue. Colombo district experiences continuous favorable temperature and humidity levels throughout the year creating ideal conditions for Aedes mosquitoes to thrive and transmit the Dengue disease. Therefore we only used the highly variable and seasonal rainfall as the primary environmental variable as it significantly influences the number of breeding sites and thereby impacting the population dynamics of Aedes.

resultsOur research successfully deduced vector competence values for the four identified seasons based on Monsoon rainfalls experienced in Colombo within a year. We used dengue data from 2009 - 2022 to infer the estimates. These estimated values have been corroborated through experimental studies documented in the literature, thereby validating the malaria model to estimate vector competence for dengue disease.

conclusionOur research findings conclude that environmental conditions can amplify vector competence within specific seasons, categorized by their environmental attributes. Additionally, the deduced vector competence offers compelling evidence that it impacts disease transmission, irrespective of geographical location, climate, or environmental factors.

Indexed as

AedesDengueDengue VirusMosquito VectorsAnimalsHumansRainSri LankaBayesianParameter estimationRainfallVector competenceVectorial capacity

Identifiers

PMID39251932
PMCPMC11385510

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LicenceCC BY-NC-ND
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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.