Diseases spread prediction in tropical areas by machine learning methods ensembling and spatial analysis techniques

A. A. Kolesnikov, P. M. Kikin, A. M. Portnov

Результат исследований: Научные публикации в периодических изданияхстатья в журнале по материалам конференциирецензирование

1 Цитирования (Scopus)


Infection with tropical parasitic diseases has a great economic and social impact and is currently one of the most pressing health problem. These diseases, according to WHO, have a huge impact on the health of more than 40 million people worldwide and are the second leading cause of immunodeficiency. Developing countries may be providers of statistical data, but need help with forecasting and preventing epidemics. The number of infections is influenced by many factors-climatic, demographic, vegetation cover, land use, geomorphology. The purpose of the research is to investigate the space-time patterns, the relationship between diseases and environmental factors, assess the degree of influence of each of the factors, compare the quality of forecasting of individual techniques of geo-information analysis and machine learning and the way they are ensembled. Also we attempt to create a generalized mathematical model for predicting several types of diseases. The following resources were used as a data source: International Society for Infectious Diseases, Landsat, Sentinel. The paper concludes with the summary table containing the importance of individual climatic, social and spatial aspects affecting the incidence. The most effective predictions were given by a mathematical model based on a combination of spatial analysis techniques (MGWR) and neural networks based on the LSTM architecture.

Язык оригиналаанглийский
Страницы (с-по)221-226
Число страниц6
ЖурналInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Номер выпуска3/W8
СостояниеОпубликовано - 20 авг 2019
Событие2019 GeoInformation for Disaster Management, Gi4DM 2019 - Prague, Чехия
Продолжительность: 3 сен 20196 сен 2019

Предметные области Scopus

  • Информационные системы
  • География, планирование и развитие


Подробные сведения о темах исследования «Diseases spread prediction in tropical areas by machine learning methods ensembling and spatial analysis techniques». Вместе они формируют уникальный семантический отпечаток (fingerprint).