ArticlePLoS neglected tropical diseases2023
Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques.
Article in PLoS neglected tropical diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Advances in mosquito-borne disease surveillance using machine learning.New microbes and new infections · 2026Article
- Spatio-temporal modelling of dengue counts in the Central Valley of Costa Rica.Epidemiology and infection · 2026Article
- Rural dengue dynamics: the interplay of climate, built environment, and agriculture in Costa Rica.medRxiv : the preprint server for health sciences · 2026Article
- High-resolution climate-dengue modeling and mid-century projections under SSP5-8.5 in Costa Rica.Science in One Health · 2026Article
- Time series analysis (2015-2022): dengue risk scenario in Goiás, Brazil.Revista brasileira de epidemiologia = Brazilian journal of epidemiology · 2026Article
- Impact of Climate Change on the Global Dynamics of Vector-Borne Infectious Diseases: A Narrative Review.Cureus · 2025Review
- An Integrative Explainable Artificial Intelligence Approach to Analyze Fine-Scale Land-Cover and Land-Use Factors Associated with Spatial Distributions of Place of Residence of Reported Dengue Cases.Tropical medicine and infectious disease · 2023Article
- The Potential of Surveillance Data for Dengue Risk Mapping: An Evaluation of Different Approaches in Cuba.Tropical medicine and infectious disease · 2023Article
- Common patterns between dengue cases, climate, and local environmental variables in Costa Rica: A wavelet approach.PLOS global public health · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dengue fever is a vector-borne disease affecting millions yearly, mostly in tropical and subtropical countries. Driven mainly by social and environmental factors, dengue incidence and geographical expansion have increased in recent decades. Therefore, understanding how climate variables drive dengue outbreaks is challenging and a problem of interest for decision-makers that could aid in improving surveillance and resource allocation. Here, we explore the effect of climate variables on relative dengue risk in 32 cantons of interest for public health authorities in Costa Rica. Relative dengue risk is forecast using a Generalized Additive Model for location, scale, and shape and a Random Forest approach. Models use a training period from 2000 to 2020 and predicted climatic variables obtained with a vector auto-regressive model. Results show reliable projections, and climate variables predictions allow for a prospective instead of a retrospective study.
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Registered trials
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.