15. Using remote sensing and GIS data for establishing malaria risk zoning maps
Article Sidebar
-
Viễn thám Remote sensing
Abstract
Malaria is one of the most populated tropical diseases in Vietnam. Based on statistical data, the number of people infected malaria in Vietnam was 1 million people in 1991, reduced to 6780 people in 2018. The government's effort is to eliminate malaria from the community by 2030. However, the Central and Central Highlands provinces are still hot spots for malaria due to the characteristics of topography, population and people's living habits. Forecasting and risk zoning for the preparation of response plans are very important for malaria elimination. In this paper, GIS and Artificial Neuron Network (ANN) are integrated to process the remote sensing and observation data in order to create the malaria risk zoning map in Dak Nong province. The input data include 15 criteria and observational data from fields. The results showed that the forecasting malaria map is highly appropriate for field observation data. This means that GIS and ANN application has high potential in malaria forecast mapping and can be applied to other tropical diseases in Vietnam.
Full text article
References
[2]. Abiodun Morakinyo Adeola, and et al (2015). Application of geographical information system and remote sensing in malaria research and control in South Africa: a review. Southern African Journal of Infectious Diseases, Volume 30, Issue 4.
[3]. Jacklin F Mosha, and et al (2014). Hot spot or not: a comparison of spatial statistical methods to predict prospective malaria infections. Malaria Journal 2014. https://malariajournal.biomedcentral.com/articles/10.1186/1475-2875-13-53.
[4]. Maru Aregawi et al (2013). Marc Coosemans Time Series Analysis of Trends in Malaria Cases and Deaths at Hospitals and the Effect of Antimalarial Interventions, 2001 - 2011, Ethiopia. Journal of PLOS ONE. https://doi.org/10.1371/journal.pone.0106359.
[5]. Sudheer Ch et al (2014). A Support Vector Machine-Firefly Algorithm based forecasting model to determine malaria transmission. Journal ofNeurocomputing https://doi.org/10.1016/j.neucom.2013.09.030.
[6]. Palaniyandi M (2014). Red and Infrared remote sensing data for mapping and assessing the malaria and JE vectors. J Geophys Remote Sensing. 3(3):1 – 4.
[7]. Anna L Buczak, and et al (2015). Fuzzy association rule mining and classificationfor the prediction of malaria in South Korea. BMC Medical Informatics and Decision Making 15(1):47, DOI: 10.1186/s12911-015-0170-6.
[8]. Orlando Zacarias, Henrik Boström (2013). Comparing Support Vector Regression and Random Forests Modeling for Predicting Malaria Incidence in Mozambique. International Journal on Advances in ICT for Emerging Regions (ICTer), DOI: 10.1109/ICTer.2013.6761181.
[9]. Hồ Đắc Thoàn (2018). Nghiên cứu một số đặc điểm dịch tễ và biện pháp phòng chống sốt rét cho người dân ngủ rẫy ở hai huyện của tỉnh Khánh Hòa và Gia Lai (2014 - 2017). Luận án Tiến sĩ Y tế công cộng, Hà Nội.
[10]. Nguyễn Đức Hảo (2010). Xác định tỷ lệ mắc và thực trạng sử dụng thuốc tự điều trị sốt rét cho người ngủ rẫy tại xã Đắk R Măng, huyện Đắk Glong, tỉnh Đắk Nông năm 2010. Y tế công cộng.
[11]. Nguyễn Quang Mỹ (2002), Tăng cường năng lực đào tạo về Viễn thám và GIS trong lĩnh vực môi trường và sức khoẻ ở Việt Nam, Đại học Khoa học tự nhiên, Đại học Quốc gia Hà Nội
[12]. Đào Văn Dũng và nnk (2009). Ứng dụng viễn thám và hệ thống thông tin địa lý trong dự báo dịch sốt rét ở Gia Lai. Tạp chí Y học Việt Nam.
[13]. Quang-Thanh Bui and et al (2018). Understanding spatial variations of malaria in Vietnam using remotely sensed data integrated into GIS and machine learning classifiers. Geocarto International.
[14]. Dieu Tien Bui, and et al (2016). Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibility modeling in a high-frequency tropical. Journal of Hydrology, Vol 540, pp 317 – 330.
[15]. Dieu Tien Bui and et al (2017). A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Journal of Agricultural and forest meteorology, Vol 233, pp 32 - 44.
