Predicting Aedes Aegypti Eggs Count Using Remote Sensing Data and a Generalized Linear Model

Mudele, Oladimeji; Andreo, Verónica; Porcasi, Ximena; Scavuzzo, Carlos M.; López, Laura; Gamba, Paolo

doi:10.1109/igarss47720.2021.9554002

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…In O. Mudele's work, a Poisson GLM is used to model and extrapolate Ae. aegypti eggs count based on relevant environmental variables (precipitation, temperature, humidity, and vegetation condition) extracted from RS data [10]. D A. Focks also employs dynamic life table methods to predict the dynamic changes of Ae.…”

Section: Introductionmentioning

confidence: 99%

Three machine-learning models to predict dengue vectors Breteau Index based on meteorology and biotope in Fujian China

Ke,

Liu,

Yang

et al. 2024

Preprint

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Background: Dengue fever's rising prevalence in China underscores the need for improved surveillance tools. The Breteau Index (BI), critical for tracking dengue transmission, currently lacks timely and precise predictions, hindering effective response strategies. This study proposes a machine learning-enhanced BI predictive model to refine dengue forecasting in Fujian China. Methods: Data Collection: In this study, data about the Breteau Index (BI), meteorological conditions, and biotope characteristics from 2015 to 2022 were systematically gathered from the Siming District in Xiamen. Model Development: A range of machine learning algorithms was employed for BI prediction, including Support Vector Regression (SVR), Step-down Linear Regression, Random Forest (RF), Decision Trees (DT), Logistic Regression Models, Deep Neural Networks (DNN), LASSO Logistic Models, and Generalized Additive Models (GAM). Evaluation Metrics: The effectiveness and fit of the predictive models were quantitatively evaluated using the Akaike Information Criterion (AIC)/Bayesian Information Criterion (BIC) and R-squared values, alongside autocorrelation analyses of residuals to ensure statistical integrity. External Validation: Models were further validated with BI data from other Fujian cities to test their generalizability. Results: This study highlights the Deep Neural Network (DNN) model's superiority over alternative forecasting methods for the Breteau Index (BI), with significant determinants being stagnant water sources like unused containers and aquatic plant bonsais. Meteorological conditions, including precipitation(7-day lag), wind speed(3, 5, 7-day lags), sunshine(2-day lag), barometric pressure(3, 4-day lags), humidity, and temperature(7-day lag), were identified as critical influencers of BI. The analysis underscores the necessity of prompt waterlogged area management post-rainfall to mitigate dengue vector breeding, aligning with findings on the ecological preferences of Aedes albopictus. Conclusions: In conclusion, the validated Deep Neural Network (DNN) model efficiently predicts the Breteau Index (BI) in Xiamen, indicating its utility in Fujian. Key strategies include controlling waterlogged containers to reduce Aedes aegypti habitats and promptly addressing weather-related BI fluctuations within three days post-rainstorm to lower dengue transmission risk.

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Section: Introductionmentioning

confidence: 99%

Three machine-learning models to predict dengue vectors Breteau Index based on meteorology and biotope in Fujian China

Ke,

Liu,

Yang

et al. 2024

Preprint

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“…is determined by the nature of the outcome variable to be predicted (binary, count, continuous, etc. )(Mudele et al, 2021b;Mudele et al, 2021c).…”

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confidence: 99%

Application of satellite imagery for vector-borne disease monitoring in sub-Saharan Africa: An overview

Oluwole Olakunle Ajayi,

Busayo Wright-Ajayi,

Lateefat Abiodun Mosaku

et al. 2024

GSC Adv. Res. Rev.

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This work presents an overview of the application of satellite imagery or remote sensing (RS) data for vector-borne disease (VBD) monitoring in sub-Saharan Africa. We discussed the different vector-borne disease conditions that are prevalent and endemic in parts of sub-Saharan Africa and how satellite imagery (RS data) can be used in monitoring these conditions. Key disease conditions considered include malaria, human African trypanosomiasis, leishmaniasis, lymphatic filariasis, loa loa filariasis, rift valley fever, dengue, yellow fever, and rickettsioses. Furthermore, we explored some of the current ways remote sensing data and geographical information systems (GIS) are being applied to monitoring these diseases. We discuss the efficacy of using strong spatial modelling techniques combined with RS data to enhance our comprehension of the role that environmental conditions play in influencing VBD vectors and transmission alongside the utilization of GIS/RS technologies for vector-borne illness surveillance, prevention, and control. Finally, we discussed how drone technology and new remote sensing platforms can provide improved monitoring and the impact of such improvements from a population health standpoint.

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A 3D Dynamical Hybrid Model: Coupling Statistical And Machine Learning Techniques To SAR Time Series Prediction

Guerra,

Bayer,

Gamba

2023

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

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Predicting Aedes Aegypti Eggs Count Using Remote Sensing Data and a Generalized Linear Model

Cited by 3 publications

References 16 publications

Three machine-learning models to predict dengue vectors Breteau Index based on meteorology and biotope in Fujian China

Three machine-learning models to predict dengue vectors Breteau Index based on meteorology and biotope in Fujian China

Application of satellite imagery for vector-borne disease monitoring in sub-Saharan Africa: An overview

A 3D Dynamical Hybrid Model: Coupling Statistical And Machine Learning Techniques To SAR Time Series Prediction

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