Understanding spatial variations of malaria in Vietnam using remotely sensed data integrated into GIS and machine learning classifiers

Bui, Quang‐Thanh; Nguyen, Quoc Hung; Pham, Van-Manh; Pham, Minh; Tran, Anh

doi:10.1080/10106049.2018.1478890

Cited by 35 publications

(37 citation statements)

References 20 publications

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“…Studies such as Diboulo et al [46] and Giardina et al [39] used the permanent or semi-permanent waters taking into account both natural and man-made containers. Other studies considered proximity to the running water, like rivers [76] or streams [31,77] and stagnant waters and lakes [45,78].…”

Section: Ruangudomsakul Et Al Used a Multitude Of Indices Derived Frmentioning

confidence: 99%

“…Topography is a significant factor in the transmission of MBDs as it affects the living conditions of the Anopheles and Aedes aegypti mosquitoes and indicates the best suited breeding sites [79,80]. Fourteen studies in this review [31][32][33]39,42,44,45,49,67,[76][77][78]81,82] utilized Digital Elevation Models (DEMs) to extract the topographic parameters of elevation, aspect and slope. Moreover, DEMs were used for calculating the Topographic Wetness Index (TWI) [41,42,78], an index that gives information about the wetness of an area, taking into account the topographic slope and the upstream area.…”

Section: Ruangudomsakul Et Al Used a Multitude Of Indices Derived Frmentioning

confidence: 99%

“…According to Stilianakis et al [30] low wind speed was associated with the existence of infected mosquitoes, while high speed reduced the chances for blood meals, and consequently human infections. Overview of studies that utilized EO derived environmental/climatic variables: LST [32,35,[37][38][39][40]43,45,46,[49][50][51][52][63][64][65][66][67]75,[83][84][85], Air temperature [31,41,44,48,53,59,60,76,81,82,86,87], Temperature Index [61], Soil Temperature [30], SST [59,60], SMT [71], SSTA [32], Precipitaiton (Sat) [32,35,40,43,45,50,51,…”

Section: Ruangudomsakul Et Al Used a Multitude Of Indices Derived Frmentioning

confidence: 99%

“…Moreover, the TRMM mission was the most important source to timely acquire precipitation data, while Meteosat-7 data were also used by Amek et al to estimate the rainfall at a 8 km wide cell [45]. Landsat-7 and Landsat-8, were both used to derive vegetation indices [33,76,77] as well as to generate LU/LC maps. Correspondingly Adde et al [48] and Yue et al [85] generated detailed LU/LC maps from using SPOT-5 and GF-1 data at the spatial resolution of 10 m and 16 m respectively.…”

Section: Satellite Eo Systems Used For Assessing the Environmental Prmentioning

confidence: 99%

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Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

et al. 2019

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Earth Observation (EO) data can be leveraged to estimate environmental variables that influence the transmission cycle of the pathogens that lead to mosquito-borne diseases (MBDs). The aim of this scoping review is to examine the state-of-the-art and identify knowledge gaps on the latest methods that used satellite EO data in their epidemiological models focusing on malaria, dengue and West Nile Virus (WNV). In total, 43 scientific papers met the inclusion criteria and were considered in this review. Researchers have examined a wide variety of methodologies ranging from statistical to machine learning algorithms. A number of studies used models and EO data that seemed promising and claimed to be easily replicated in different geographic contexts, enabling the realization of systems on regional and national scales. The need has emerged to leverage furthermore new powerful modeling approaches, like artificial intelligence and ensemble modeling and explore new and enhanced EO sensors towards the analysis of big satellite data, in order to develop accurate epidemiological models and contribute to the reduction of the burden of MBDs.

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Section: Ruangudomsakul Et Al Used a Multitude Of Indices Derived Frmentioning

confidence: 99%

Section: Ruangudomsakul Et Al Used a Multitude Of Indices Derived Frmentioning

confidence: 99%

Section: Ruangudomsakul Et Al Used a Multitude Of Indices Derived Frmentioning

confidence: 99%

Section: Satellite Eo Systems Used For Assessing the Environmental Prmentioning

confidence: 99%

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Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

et al. 2019

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“…Multilayer Perceptron (MPL) and J48 classifier techniques in the Weka tool recently being used in successfully predicting malaria incidents [32][33][34]. Researchers around the globe also used it for prediction of dengue [35][36][37][38] and other public health issues such as Cholera [39], diabetes [40][41][42], heart diseases [43,44].…”

Section: Data Preparationmentioning

confidence: 99%

Comparative Analysis of Machine Learning Classifiers for the Prediction of Malaria Incidence Attributed to Climatic Factors

Mohapatra¹,

Tripathi²,

Pal³

et al. 2020

Preprint

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Background: India has a rising rate of malaria as well as a high mortality rate despite awareness and efforts being focused on the issue. Some regions are profoundly affected than others, such as in Odisha, where the prevalence of malaria is nearly a third of the whole country. This study investigated the influence of climate factors on the incidence of malaria in the Sundargarh district in the state of Odisha, India. Methods: Block-wise observed station rainfall data was sourced from the Special Relief Commissioners' (SRC) web portal. Gridded surface maximum temperature and relative humidity data were accessed from the European Center for Medium-range Weather Forecast (ECMWF) reanalysis data archive. Malaria incident data were collected from the Directorate of Public Health, Government of Odisha. WEKA machine learning tool with two classifier techniques, Multi-Layer Perceptron (MLP) and J48 with 10-fold cross-validation, percentile split (66%), and supplied test options, were used for the Malaria prediction. A comparative analysis was carried out on both techniques to ascertain the superior model amongst the two, concerning the prediction accuracy of malaria in the context of a varying climate. Classifier accuracy, Root Mean Square Error (RMSE), Kappa, and ROC scores were the indicators used for the analysis. Results: The results suggested that J48 had exhibited a better skill to MLP and illustrated less error with a positive kappa. In particular, the 10-fold cross-validation method had better performance over the percentile Spilt (66%) and supplied test options. J48 demonstrated less error (RMSE = 0.6), better kappa = 0.63, and higher accuracy = 0.71), suggesting it as most suitable model. Further, seasonal temperature and humidity variation had shown a better association with malaria incidents in comparison to rainfall.Conclusion: The performance of the machine learning methods for Sundargarh was particularly better during the monsoon and post-monsoon when the events are at the peak. The results were encouraging for the utilization of climate forecast for prediction of malaria incidences. It is thus recommended that the J48 classifier machine learning technique could be adopted for the development of malaria early warning system.

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Machine Learning Techniques for Predicting Malaria: Unpacking Emerging Challenges and Opportunities for Tackling Malaria in Sub-saharan Africa

Mbunge,

Milham,

Sibiya

et al. 2023

Lecture Notes in Networks and Systems

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Understanding spatial variations of malaria in Vietnam using remotely sensed data integrated into GIS and machine learning classifiers

Cited by 35 publications

References 20 publications

Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

Comparative Analysis of Machine Learning Classifiers for the Prediction of Malaria Incidence Attributed to Climatic Factors

Machine Learning Techniques for Predicting Malaria: Unpacking Emerging Challenges and Opportunities for Tackling Malaria in Sub-saharan Africa

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