Elsevier

One Health

Volume 13, December 2021, 100358
One Health

Mapping the spatial distribution of the dengue vector Aedes aegypti and predicting its abundance in northeastern Thailand using machine-learning approach

https://doi.org/10.1016/j.onehlt.2021.100358Get rights and content
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open access

Highlights

  • Aedes (Ae.) aegypti is the primary vector for dengue globally, including in Thailand where this study was done.

  • Understanding the spatial distribution of Ae. aegypti abundance (high/low) can help design control methods for preventing dengue and other arboviral diseases.

  • Socio-economic, KAPs on climate change and dengue, and landscape factors can be helpful in predicting the local abundance of Ae. aegypti.

  • Accurate prediction of disease vectors is of great importance in designing effective vector control strategies and allocating resources effectively.

  • Random Forest models give the best prediction of Ae. aegypti abundance in Northeastern Thailand.

Abstract

Background

Mapping the spatial distribution of the dengue vector Aedes (Ae.) aegypti and accurately predicting its abundance are crucial for designing effective vector control strategies and early warning tools for dengue epidemic prevention. Socio-ecological and landscape factors influence Ae. aegypti abundance. Therefore, we aimed to map the spatial distribution of female adult Ae. aegypti and predict its abundance in northeastern Thailand based on socioeconomic, climate change, and dengue knowledge, attitude and practices (KAP) and/or landscape factors using machine learning (ML)-based system.

Method

A total of 1066 females adult Ae. aegypti were collected from four villages in northeastern Thailand during January–December 2019. Information on household socioeconomics, KAP regarding climate change and dengue, and satellite-based landscape data were also acquired. Geographic information systems (GIS) were used to map the household-based spatial distribution of female adult Ae. aegypti abundance (high/low). Five popular supervised learning models, logistic regression (LR), support vector machine (SVM), k-nearest neighbor (kNN), artificial neural network (ANN), and random forest (RF), were used to predict females adult Ae. aegypti abundance (high/low). The predictive accuracy of each modeling technique was calculated and evaluated. Important variables for predicting female adult Ae. aegypti abundance were also identified using the best-fitted model.

Results

Urban areas had higher abundance of female adult Ae. aegypti compared to rural areas. Overall, study respondents in both urban and rural areas had inadequate KAP regarding climate change and dengue. The average landscape factors per household in urban areas were rice crop (47.4%), natural tree cover (17.8%), built-up area (13.2%), permanent wetlands (21.2%), and rubber plantation (0%), and the corresponding figures for rural areas were 12.1, 2.0, 38.7, 40.1 and 0.1% respectively. Among all assessed models, RF showed the best prediction performance (socioeconomics: area under curve, AUC = 0.93, classification accuracy, CA = 0.86, F1 score = 0.85; KAP: AUC = 0.95, CA = 0.92, F1 = 0.90; landscape: AUC = 0.96, CA = 0.89, F1 = 0.87) for female adult Ae. aegypti abundance. The combined influences of all factors further improved the predictive accuracy in RF model (socioeconomics + KAP + landscape: AUC = 0.99, CA = 0.96 and F1 = 0.95). Dengue prevention practices were shown to be the most important predictor in the RF model for female adult Ae. aegypti abundance in northeastern Thailand.

Conclusion

The RF model is more suitable for the prediction of Ae. aegypti abundance in northeastern Thailand. Our study exemplifies that the application of GIS and machine learning systems has significant potential for understanding the spatial distribution of dengue vectors and predicting its abundance. The study findings might help optimize vector control strategies, future mosquito suppression, prediction and control strategies of epidemic arboviral diseases (dengue, chikungunya, and Zika). Such strategies can be incorporated into One Health approaches applying transdisciplinary approaches considering human-vector and agro-environmental interrelationships.

Keywords

Supervised learning
Aedes aegypti
Prediction
Dengue
Early warning

Abbreviations

DENV
Dengue virus
GIS
Geographic information systems
ML
Machine learning
KAP
Knowledge, attitude, and practice
SES
Socioeconomic status
PCI
Premise condition index
HCI
Household crowding index
LR
logistic regression
SVM
Support vector machine
kNN
k-nearest neighbor
ANN
Artificial neural network
RF
Random forest
AUC
Area under curve
CA
Classification accuracy.

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