Meteorological, environmental remote sensing and neural network analysis of the epidemiology of malaria transmission in Thailand

Kiang, Richard and Adimi, Farida and Soika, Valerii and Nigro, Joseph and Singhasivanon, Pratap and Sirichaisinthop, Jeeraphat and Leemingsawat, Somjai and Apiwathnasorn, Chamnarn and Looareesuwan, Sornchai (2006) Meteorological, environmental remote sensing and neural network analysis of the epidemiology of malaria transmission in Thailand. Geospatial health , 1 (1). pp. 71-84. ISSN 1970-7096

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In many malarious regions malaria transmission roughly coincides with rainy seasons, which provide for more abundant larval habitats. In addition to precipitation, other meteorological and environmental factors may also influence malaria transmission. These factors can be remotely sensed using earth observing environmental satellites and estimated with seasonal climate forecasts. The use of remote sensing usage as an early warning tool for malaria epidemics have been broadly studied in recent years, especially for Africa, where the majority of the world’s malaria occurs. Although the Greater Mekong Subregion (GMS), which includes Thailand and the surrounding countries, is an epicenter of multidrug resistant falciparum malaria, the meteorological and environmental factors affecting malaria transmissions in the GMS have not been examined in detail. In this study, the parasitological data used consisted of the monthly malaria epidemiology data at the provincial level compiled by the Thai Ministry of Public Health. Precipitation, temperature, relative humidity, and vegetation index obtained from both climate time series and satellite measurements were used as independent variables to model malaria. We used neural network methods, an artificial-intelligence technique, to model the dependency of malaria transmission on these variables. The average training accuracy of the neural network analysis for three provinces (Kanchanaburi, Mae Hong Son, and Tak) which are among the provinces most endemic for malaria, is 72.8% and the average testing accuracy is 62.9% based on the 1994-1999 data. A more complex neural network architecture resulted in higher training accuracy but also lower testing accuracy. Taking into account of the uncertainty regarding reported malaria cases, we divided the malaria cases into bands (classes) to compute training accuracy. Using the same neural network architecture on the 19 most endemic provinces for years 1994 to 2000, the mean training accuracy weighted by provincial malaria cases was 73%. Prediction of malaria cases for 2001 using neural networks trained for 1994-2000 gave a weighted accuracy of 53%. Because there was a significant decrease (31%) in the number of malaria cases in the 19 provinces from 2000 to 2001, the networks overestimated malaria transmissions. The decrease in transmission was not due to climatic or environmental changes. Thailand is a country with long borders. Migrant populations from the neighboring countries enlarge the human malaria reservoir because these populations have more limited access to health care. This issue also confounds the complexity of modeling malaria based on meteorological and environmental variables alone. In spite of the relatively low resolution of the data and the impact of migrant populations, we have uncovered a reasonably clear dependency of malaria on meteorological and environmental remote sensing variables. When other contextual determinants do not vary significantly, using neural network analysis along with remote sensing variables to predict malaria endemicity should be feasible.

Item Type: Article
Uncontrolled Keywords: malaria, Thailand, remote sensing, neural networks
Subjects: 000 Scienza degli elaboratori - Scienze dell’informazione - Scienze archivistiche, librarie e dell'informazione documentaria – opere generali > 006 Metodi speciali di elaborazione > 006.3 Intelligenza artificiale (classificare qui le opere d'insieme su intelligenza artificiale e scienza cognitiva) > 006.32 Reti neurali (Neural nets, Neural networks) (classificare qui l'apprendimento connessionista, i computer neurali)
600 Tecnologia - Scienze applicate > 610 Medicina e salute (Classificare qui la tecnologia dei servizi medici) > 616 Malattie (classificare qui la Clinica medica, la medicina basata sull'evidenza, la Medicina interna, la Medicina sperimentale) > 616.9 Altre malattie (altri rami della Medicina) > 616.93 Infezioni da clostridium, difterite, colera, dissenterie, protozoosi > 616.936 Protozoosi > 616.9362 Malaria
600 Tecnologia - Scienze applicate > 620 Ingegneria e attivita' affini > 621 Fisica applicata; Ingegneria meccanica > 621.3 Ingegneria elettrotecnica; Illuminazione; Superconduttivita'; Ingegneria magnetica; Ottica applicata; Tecnologia parafotica; Elettronica; Ingegneria delle comunicazioni; Elaboratori > 621.36 Ingegneria ottica (classificare qui l'Ottica applicata; le opere sulla tecnologia dell'infrarosso e dell'ultravioletto insieme) > 621.367 Fotografia tecnica e Foto-ottica (classificare qui l'elaborazione delle immagini, l'elaborazione ottica dei dati) > 621.3678 Tecnologia del rilevamento a distanza
Depositing User: biblioteca 7
Date Deposited: 15 Jun 2010 12:53
Last Modified: 15 Jun 2010 12:53

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