Effects of climate variability on the spatio-temporal distribution of Dengue in Valle del Cauca, Colombia, from 2001 to 2019

Dengue is a vector-borne disease that has increased over the past two decades, becoming a global public health emergency. The transmission of dengue is contingent upon various factors, among which climate variability plays a significant role. However, there remains substantial uncertainty regarding the underlying mechanisms. This study aims to investigate the spatial and temporal patterns of dengue risk and to quantify the associated risk factors in Valle del Cauca, Colombia, from 2001 to 2019. To achieve this, a spatio-temporal Bayesian hierarchical model was developed, integrating delayed and non-linear effects of climate variables, socio-economic factors, along with spatio-temporal random effects to account for unexplained variability. The results indicate that average temperature is positively associated with dengue risk 0-2 months later, showing a 35% increase in the risk. Similarly, high precipitation levels lead to increased risk approximately 2-3 months later, while relative humidity showed a constant risk within a 6 months-lag. These findings could be valuable for local health authorities interested in developing early warning systems to predict future risks in advance.

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2024-10-08
DOI
https://doi.org/10.1371/journal.pone.0311607
Citations
12
Primary Topic
Mosquito-borne diseases and control
Type
article
Field-Weighted Citation Impact
6.52

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Effects of climate variability on the spatio-temporal distribution of Dengue in Valle del Cauca, Colombia, from 2001 to 2019

Paula Moraga, Delia Ortega-Lenis, Freddy Hernández Barajas, David Arango-Londoño
12 citations
PLoS ONE
Mosquito-borne diseases and control
6.52
article

Effects of climate variability on the spatio-temporal distribution of Dengue in Valle del Cauca, Colombia, from 2001 to 2019

Paula Moraga, Delia Ortega-Lenis, Freddy Hernández Barajas, David Arango-Londoño
article en
12 citations

Abstract

Dengue is a vector-borne disease that has increased over the past two decades, becoming a global public health emergency. The transmission of dengue is contingent upon various factors, among which climate variability plays a significant role. However, there remains substantial uncertainty regarding the underlying mechanisms. This study aims to investigate the spatial and temporal patterns of dengue risk and to quantify the associated risk factors in Valle del Cauca, Colombia, from 2001 to 2019. To achieve this, a spatio-temporal Bayesian hierarchical model was developed, integrating delayed and non-linear effects of climate variables, socio-economic factors, along with spatio-temporal random effects to account for unexplained variability. The results indicate that average temperature is positively associated with dengue risk 0-2 months later, showing a 35% increase in the risk. Similarly, high precipitation levels lead to increased risk approximately 2-3 months later, while relative humidity showed a constant risk within a 6 months-lag. These findings could be valuable for local health authorities interested in developing early warning systems to predict future risks in advance.

PLoS ONEVol. 19(10)
Pontificia Universidad Javeriana (CO), Universidad Nacional de Colombia (CO), King Abdullah University of Science and Technology (SA)
Ministry of Science and Technology
Climate action
Openalex Percentile: Top 3%
Mosquito-borne diseases and control
6.52
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.