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

Rahman, Siddikur; Pientong, Chamsai; Zafar, Sumaira; Ekalaksananan, Tipaya; Rácz, Paul; Haque, Ubydul; Rocklöv, Joacim; Overgaard, Hans J.

doi:10.1016/j.onehlt.2021.100358

Cited by 21 publications

(14 citation statements)

References 40 publications

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“…The level of education can be a major determinant of knowledge of the disease and its transmission, as well as attitudes and practices, especially those involving the integration of community efforts for dengue control [15]. People with a higher level of education and maturity according to age are important factors to increase public awareness about dengue prevention [16], [17].…”

Section: Resultsmentioning

confidence: 99%

Factors associated with dengue fever prevention practices in endemic area

Arfan

Rizky

Hernawan

2022

IJPHS

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Prevention practices and the factors that influence them are essential for controlling the spread of dengue hemorrhagic fever (DHF) and effective strategic planning. This study aimed to determine the factors associated with the practice of preventing dengue fever in communities in endemic areas of Kubu Raya Regency, West Kalimantan Province, Indonesia. This study was an observational study with a cross-sectional design. The research sample was 379 households in endemic areas using a questionnaire. Collecting data was using a questionnaire with interviews. Bivariate analysis employed Chi-square test, multivariate analysis using logistic regression. Most of the respondents practiced fewer prevention; variables related to dengue fever prevention practices were income, education, knowledge about dengue prevention and health education (mass media). Regression analysis showed that the variables of education, knowledge about dengue prevention, and mass media education were related to the practice of preventing dengue fever. This study indicated that in preventing dengue fever, strategies must be developed to cultivate prevention practices and increase prevention campaigns using mass media and focus on areas with low education and knowledge in dengue prevention.

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Section: Resultsmentioning

confidence: 99%

Factors associated with dengue fever prevention practices in endemic area

Arfan

Rizky

Hernawan

2022

IJPHS

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“…The XGBoost model is a supervised machine learning technique and an emerging machine learning method for time series forecasting in recent years [ 39 , 40 ]. It uses an improved generalized gradient boosting library that can rapidly assess the value of all input attributes [ 41 – 43 ]. Boosting is a technique that combines hundreds of low-accuracy prediction models into a single high-accuracy model by frequently integrating the models under tolerable parameter values [ 44 – 46 ].…”

Section: Methodsmentioning

confidence: 99%

Section: Xgboost Modelmentioning

confidence: 99%

A data-driven eXtreme gradient boosting machine learning model to predict COVID-19 transmission with meteorological drivers

Rahman

Chowdhury

2022

PLoS ONE

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COVID-19 pandemic has become a global major public health concern. Examining the meteorological risk factors and accurately predicting the incidence of the COVID-19 pandemic is an extremely important challenge. Therefore, in this study, we analyzed the relationship between meteorological factors and COVID-19 transmission in SAARC countries. We also compared the predictive accuracy of Autoregressive Integrated Moving Average (ARIMAX) and eXtreme Gradient Boosting (XGBoost) methods for precise modelling of COVID-19 incidence. We compiled a daily dataset including confirmed COVID-19 case counts, minimum and maximum temperature (°C), relative humidity (%), surface pressure (kPa), precipitation (mm/day) and maximum wind speed (m/s) from the onset of the disease to January 29, 2022, in each country. The data were divided into training and test sets. The training data were used to fit ARIMAX model for examining significant meteorological risk factors. All significant factors were then used as covariates in ARIMAX and XGBoost models to predict the COVID-19 confirmed cases. We found that maximum temperature had a positive impact on the COVID-19 transmission in Afghanistan (β = 11.91, 95% CI: 4.77, 19.05) and India (β = 0.18, 95% CI: 0.01, 0.35). Surface pressure had a positive influence in Pakistan (β = 25.77, 95% CI: 7.85, 43.69) and Sri Lanka (β = 411.63, 95% CI: 49.04, 774.23). We also found that the XGBoost model can help improve prediction of COVID-19 cases in SAARC countries over the ARIMAX model. The study findings will help the scientific communities and policymakers to establish a more accurate early warning system to control the spread of the pandemic.

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“…The factors used in the research [ 35 ] conducted in northeastern Thailand on the spatial distribution and prediction of abundance (classified as high and low) dengue vector were household-based socioeconomic and urban–rural residence, education status, household income, socioeconomic status, dengue knowledge, attitude and practices, and land use/land cover (built-up area, permanent wetlands, natural tree cover, rubber plantation, rice crop). The authors compared the results of all these different types of data factors and also used machine learning models on all these different data factors collectively: Logistic Regression, SVM, RF, k-Nearest Neighbor (kNN), and Artificial Neural Network (ANN).…”

Section: Related Workmentioning

confidence: 99%

WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases

Javaid

Sarfraz

Aftab

et al. 2023

IJERPH

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The diseases transmitted through vectors such as mosquitoes are named vector-borne diseases (VBDs), such as malaria, dengue, and leishmaniasis. Malaria spreads by a vector named Anopheles mosquitos. Dengue is transmitted through the bite of the female vector Aedes aegypti or Aedes albopictus mosquito. The female Phlebotomine sandfly is the vector that transmits leishmaniasis. The best way to control VBDs is to identify breeding sites for their vectors. This can be efficiently accomplished by the Geographical Information System (GIS). The objective was to find the relation between climatic factors (temperature, humidity, and precipitation) to identify breeding sites for these vectors. Our data contained imbalance classes, so data oversampling of different sizes was created. The machine learning models used were Light Gradient Boosting Machine, Random Forest, Decision Tree, Support Vector Machine, and Multi-Layer Perceptron for model training. Their results were compared and analyzed to select the best model for disease prediction in Punjab, Pakistan. Random Forest was the selected model with 93.97% accuracy. Accuracy was measured using an F score, precision, or recall. Temperature, precipitation, and specific humidity significantly affect the spread of dengue, malaria, and leishmaniasis. A user-friendly web-based GIS platform was also developed for concerned citizens and policymakers.

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

Cited by 21 publications

References 40 publications

Factors associated with dengue fever prevention practices in endemic area

Factors associated with dengue fever prevention practices in endemic area

A data-driven eXtreme gradient boosting machine learning model to predict COVID-19 transmission with meteorological drivers

WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases

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