Remote sensing & vector-borne diseases
Remote sensing and disease control in China
Since 1985, the use of remote sensing (RS) techniques in China has steadily grown in the area of understanding of human health. In the beginning, the ideas of RS techniques for disease control were introduced. In 1996–2003, Exploration of various RS analysis techniques were carried out for disease control activities. From2003 to present time, two major research directions are being developed. These include: identifying appropriate environments for the intermediate hosts or vectors of diseases through in-depth study based on image classification techniques of remotely sensed images and predicting appropriate environments for the occurrence of a disease of concern and related intermediate hosts and vectors through spatial modeling techniques with RS-extracted environmental factors.
Credit: Zhang et al., 2013
MODIS and Vector-Borne Diseases
The risk associated with globally distributed vector-borne diseases demands an urgent need to gain better understanding of spatio-temporal patterns in disease transmission and diffusion. Disease vectors depend on climatic and environmental conditions which in turn can be observed using Earth Observation (EO) instruments. Such methods include the use of high-resolution time series from satellites in spatial models enable researchers to assess potential health risks. MODIS (Moderate Resolution Imaging Spectroradiometer), which is flown on board the satellites Terra and Aqua has a very high importance in this regards. More than a decade of high quality data sets and operational products are freely available which can be used to assess the spread of disease vectors in order to assist decision-makers and public health authorities to develop surveillance plans and vector control.
Credit: Neteler and Metz, 2014
Vector borne disease transmission in India
The study conducted in India using remote sensing and GIS found out that spatial agreement was existed between the environmental variables and the vector borne disease epidemic transmission. The study found out that geographical distribution and the seasonal abundance of vector abundance and vector borne disease transmission have been controlled by the climate, landscape and the environmental variables. The prevalence of vector borne diseases (filariasis, malaria, JE, dengue, chikungunya, and visceral leishmaniasis) caused by the vector mosquitoes namely Culex quinquefasciatus and mansonia, Anopheles genus mosquitoes, Culex genus mosquitoes and Aedes genus mosquitoes respectively, and which has been spread over most of the regions in the country
Credit: Palaniyandi et al. 2014
Predicting Risk of Vector-borne Disease in Colorado
The study for predicting risk of vector-borne disease were conducted in Larimer and Weld Counties of Colorado along the Front Range of the Rocky Mountains. Two satellite images incorporating urban, suburban, riparian and agricultural areas were being used for a base map. The purpose of the study was to find out whether researchers can correctly determine mosquito habitats within the study area. The researchers trapped mosquitoes at a number of sample sites within the study area and the data were statistically analyzed in relation to an unsupervised classification of the satellite images covering the study area
Credit: Anna Maki, 2005
Malaria in Chiapas, Mexico
U. S. and Mexican researchers collaborated in a study to map habitats of the vector Anopheles albimanus, using Landsat Thematic Mapper (TM) imagery and extensive ecological and epidemiological data (Beck et al. 1994). Pixel categories were associated with landscape type both by ground surveys and by comparison to color infrared photography of the study area. Using the relationship between landscape type and suitability as larval habitat for An. albimanus, it was possible to correctly distinguish between villages with high and low vector abundance with an overall accuracy of 90% (Beck et al. 1994).
Tsetse in Africa
Robinson et al. (1997) mapped the distribution of tsetse (Glossina spp.) habitat by using climate and remotely sensed vegetation data. Coarse-resolution (7.6 km) Advanced Very-High Resolution Radiometer (AVHRR) images were combined with smoothed climate surfaces, derived from continent-wide, long-term weather station records. Predictions were improved by subdividing habitats prior to classification. This system might be improved by using satellite-derived weather data and by using finer grain imagery (e.g., 1.1 km AVHRR).
Lyme disease (Lyme borreliosis) in Maryland
Lyme disease (Lyme borreliosis): Several groups have used remotely sensed data to improve our understanding of Lyme disease. Glass et al. (1992), for example, conducted a case-control study of Lyme disease in Baltimore, County, Maryland, USA. Land use/land cover maps (derived from Landsat TM imagery) were combined with soils, elevation, geology, and watershed maps to evaluate risk of exposure to Lyme disease and its vector, Ixodes scapularis. The risk of disease was significantly lower in highly developed areas, and risk decreased with increasing distance from forests.
Tick-borne encephalitis in Europe
Daniel and KoláÍ (1990) analyzed a 25-year data base on the distribution and abundance of Ixodes ricinus, the vector of tick-borne encephalitis in Europe, in relation to land cover types derived from a 41-km x 41-km section of a Landsat Multispectral Scanner (MSS) scene. They showed that I. ricinus is associated with specific land cover types, which allowed them to generate risk maps that could be used in public education and other prevention programs.
Cholera in Bangladesh
Cholera, a water-borne disease, has been responsible for hundreds of thousands of cases and tens of thousands of deaths in each of six or seven pandemics. The cholera organism, Vibrio cholerae, may live as a commensal or symbiont associated with zooplankton and phytoplankton. Colwell (1996) and colleagues found a correlation between sea surface temperature (from satellite data) and cholera cases in Bangladesh. These authors postulate that rising temperatures produce algal blooms that lead to an increase in the marine copepods that harbor V. cholerae. The copepods are carried into the water systems of coastal communities, where epidemics of classical cholera begin.