Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques

Barboza, Luis A.; Chou-Chen, Shu-Wei; Vásquez, Paola; García, Yury E.; Calvo, Juan G.; Hidalgo, H. G.; Sánchez, Fabio

doi:10.1371/journal.pntd.0011047

Cited by 7 publications

(5 citation statements)

References 31 publications

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“…Additional research demonstrates that incorporating the same climatic variables analyzed in our study to predict relative risk areas for dengue outbreaks in all cantons may result in model projections failing in certain locations. This observation, as demonstrated by [57][58][59], emphasizes the significance of comprehending the local correlations between dengue cases and external factors such as climate and socioeconomic drivers. Such understanding can lead to improved predictions of dengue locally, enabling better-informed decision-making regarding interventions and resource allocation.…”

Section: Environmental Variablesmentioning

confidence: 81%

Common patterns between dengue cases, climate, and local environmental variables in Costa Rica: A wavelet approach

García,

Chou-Chen,

Barboza

et al. 2023

PLOS Glob Public Health

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Dengue transmission poses significant challenges for public health authorities worldwide due to its susceptibility to various factors, including environmental and climate variability, affecting its incidence and geographic spread. This study focuses on Costa Rica, a country characterized by diverse microclimates nearby, where dengue has been endemic since its introduction in 1993. Using wavelet coherence and clustering analysis, we performed a time-series analysis to uncover the intricate connections between climate, local environmental factors, and dengue occurrences. The findings indicate that multiannual dengue frequency (3 yr) is correlated with the Oceanic Niño Index and the Tropical North Atlantic Index. This association is particularly prominent in cantons located along the North and South Pacific Coast, as well as in the Central cantons of the country. Furthermore, the time series of these climate indices exhibit a leading phase of approximately nine months ahead of dengue cases. Additionally, the clustering analysis uncovers non-contiguous groups of cantons that exhibit similar correlation patterns, irrespective of their proximity or adjacency. This highlights the significance of climate factors in influencing dengue dynamics across diverse regions, regardless of spatial closeness or distance between them. On the other hand, the annual dengue frequency was correlated with local environmental indices. A persistent correlation between dengue cases and local environmental variables is observed over time in the North Pacific and the Central Region of the country’s Northwest, with environmental factors leading by less than three months. These findings contribute to understanding dengue transmission’s spatial and temporal dynamics in Costa Rica, highlighting the importance of climate and local environmental factors in dengue surveillance and control efforts.

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Section: Environmental Variablesmentioning

confidence: 81%

Common patterns between dengue cases, climate, and local environmental variables in Costa Rica: A wavelet approach

García,

Chou-Chen,

Barboza

et al. 2023

PLOS Glob Public Health

Self Cite

View full text Add to dashboard Cite

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“…However, extreme climatic conditions were the leading cause of the unprecedented KC outbreaks in 2014 and 2015 [17]. Interannual variations in dengue transmission driven by temperatures, rainfall, relative humidity, or regional climate phenomena, such as ENSO and IOD, have been discussed elsewhere [9,11,14,38,39]. Moreover, suitability analyses have demonstrated critical associations between vector ecology, virus propagation, and climatic variations [15,25,40,41].…”

Section: Discussionmentioning

confidence: 99%

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

2023

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Dengue fever is a prevalent mosquito-borne disease that burdens communities in subtropical and tropical regions. Dengue transmission is ecologically complex; several environmental conditions are critical for the spatial and temporal distribution of dengue. Interannual variability and spatial distribution of dengue transmission are well-studied; however, the effects of land cover and use are yet to be investigated. Therefore, we applied an explainable artificial intelligence (AI) approach to integrate the EXtreme Gradient Boosting and Shapley Additive Explanation (SHAP) methods to evaluate spatial patterns of the residences of reported dengue cases based on various fine-scale land-cover land-use types, Shannon’s diversity index, and household density in Kaohsiung City, Taiwan, between 2014 and 2015. We found that the proportions of general roads and residential areas play essential roles in dengue case residences with nonlinear patterns. Agriculture-related features were negatively associated with dengue incidence. Additionally, Shannon’s diversity index showed a U-shaped relationship with dengue infection, and SHAP dependence plots showed different relationships between various land-use types and dengue incidence. Finally, landscape-based prediction maps were generated from the best-fit model and highlighted high-risk zones within the metropolitan region. The explainable AI approach delineated precise associations between spatial patterns of the residences of dengue cases and diverse land-use characteristics. This information is beneficial for resource allocation and control strategy modification.

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“…Previous studies [27,40] have shown that these climatic covariates are essential to predict dengue incidence. To begin the calibration, a training period is chosen to fit the model 1 using different combinations of covariates and spatio-temporal configurations of the model.…”

Section: Model Selection and Predictionmentioning

confidence: 99%

“…where U t and L t are the upper and lower limits of the prediction interval, respectively, the latter metric is more complete in evaluating the models' predictive capacity when the uncertainty is summarized through a predictive interval [42]. It has been used in previous predictive studies on dengue fever in Costa Rica (see [26,40]). We use the normalized version of RMSE and IS because we can compare different locations regardless of the scale of their relative risk.…”

Section: Model Selection and Predictionmentioning

confidence: 99%

Bayesian spatio-temporal model with INLA for dengue fever risk prediction in Costa Rica

Chou-Chen¹,

Barboza²,

Vásquez³

et al. 2023

Preprint

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Due to the rapid geographic spread of the Aedes mosquito and the increase in dengue incidence, dengue fever has been an increasing concern for public health authorities in tropical and subtropical countries worldwide. Significant challenges such as climate change, the burden on health systems, and the rise of insecticide resistance highlight the need to introduce new and cost-effective tools for developing public health interventions. Various and locally adapted statistical methods for developing climate-based early warning systems have increasingly been an area of interest and research worldwide. Costa Rica, a country with micro-climates and endemic circulation of the dengue virus (DENV) since 1993, provides ideal conditions for developing projection models with the potential to help guide public health efforts and interventions to control and monitor future dengue outbreaks.

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Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques

Cited by 7 publications

References 31 publications

Common patterns between dengue cases, climate, and local environmental variables in Costa Rica: A wavelet approach

Common patterns between dengue cases, climate, and local environmental variables in Costa Rica: A wavelet approach

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

Bayesian spatio-temporal model with INLA for dengue fever risk prediction in Costa Rica

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