Related Experiment Video
Updated: Aug 23, 2025
Vector Competence Analyses on Aedes aegypti Mosquitoes using Zika Virus
Published on: May 31, 2020
Spatio-temporal dynamics of three diseases caused by Aedes-borne arboviruses in Mexico
Bo Dong1, Latifur Khan1, Madison Smith2
1Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080 USA.
Background:
The intensity of transmission of Aedes-borne viruses is heterogeneous, and multiple factors can contribute to variation at small spatial scales. Illuminating drivers of heterogeneity in prevalence over time and space would provide information for public health authorities. The objective of this study is to detect the spatiotemporal clusters and determine the risk factors of three major Aedes-borne diseases, Chikungunya virus (CHIKV), Dengue virus (DENV), and Zika virus (ZIKV) clusters in Mexico.
Methods:
We present an integrated analysis of Aedes-borne diseases (ABDs), the local climate, and the socio-demographic profiles of 2469 municipalities in Mexico. We used SaTScan to detect spatial clusters and utilize the Pearson correlation coefficient, Randomized Dependence Coefficient, and SHapley Additive exPlanations to analyze the influence of socio-demographic and climatic factors on the prevalence of ABDs. We also compare six machine learning techniques, including XGBoost, decision tree, Support Vector Machine with Radial Basis Function kernel, K nearest neighbors, random forest, and neural network to predict risk factors of ABDs clusters.
Results:
DENV is the most prevalent of the three diseases throughout Mexico, with nearly 60.6% of the municipalities reported having DENV cases. For some spatiotemporal clusters, the influence of socio-economic attributes is larger than the influence of climate attributes for predicting the prevalence of ABDs. XGBoost performs the best in terms of precision-measure for ABDs prevalence.
Conclusions:
Both socio-demographic and climatic factors influence ABDs transmission in different regions of Mexico. Future studies should build predictive models supporting early warning systems to anticipate the time and location of ABDs outbreaks and determine the stand-alone influence of individual risk factors and establish causal mechanisms.
Insights
This study analyzed Dengue, Chikungunya, and Zika virus clusters in Mexico, finding that socio-economic and climate factors drive disease spread. Understanding these drivers is key for public health interventions against Aedes-borne diseases.
Area of Science:
- Epidemiology
- Public Health
- Environmental Science
Background:
- Aedes-borne diseases (ABDs) like Dengue virus (DENV), Chikungunya virus (CHIKV), and Zika virus (ZIKV) exhibit heterogeneous transmission patterns influenced by various factors at local scales.
- Understanding the drivers of ABD prevalence variability is crucial for effective public health strategies.
- This study focuses on identifying spatiotemporal clusters and risk factors for CHIKV, DENV, and ZIKV in Mexico.
Purpose of the Study:
- To detect spatiotemporal disease clusters of major Aedes-borne diseases in Mexico.
- To determine the influence of socio-demographic and climatic factors on the prevalence of these diseases.
- To compare machine learning models for predicting ABD risk factors.
Main Methods:
- Integrated analysis of ABDs, local climate, and socio-demographic data across 2469 Mexican municipalities.
- Spatial cluster detection using SaTScan.
- Analysis of factor influence using Pearson correlation, Randomized Dependence Coefficient, and SHapley Additive exPlanations.
- Comparison of six machine learning models (XGBoost, decision tree, SVM, kNN, random forest, neural network) for risk prediction.
Main Results:
- Dengue virus (DENV) is the most widespread ABD in Mexico, affecting 60.6% of municipalities.
- Socio-economic factors were found to have a greater influence than climate factors in some spatiotemporal ABD clusters.
- XGBoost demonstrated superior performance in predicting ABD prevalence.
Conclusions:
- Both socio-demographic and climatic factors significantly impact ABD transmission across different Mexican regions.
- Future research should focus on developing predictive models for early warning systems to anticipate ABD outbreaks.
- Further studies are needed to elucidate the independent influence of individual risk factors and establish causal mechanisms for ABDs.
Related Concept Videos
01:21Causality in Epidemiology
01:26Principles of Disease Surveillance