Remote sensing—the phrase conjures up images of high-tech satellites scanning the earth’s surface and sending data back to either arctic scientists, forestry managers, or military intelligence personnel. For a time, the public may have regarded remote sensing technology as being rather exclusive and—well, remote!
But with the increasing availability of satellite data for public use, capability of desktop computers to handle massive datasets, and the imaginative power of people in putting two and two together; remote sensing applications are now booming in all kinds of endeavour—some for commercial gain, and some for actually making the world a better place.
Perhaps one of the most exciting remote sensing applications of the latter kind is happening in the field of epidemiology, specifically, in addressing neglected tropical diseases (NTDs) such as malaria and snail fever. Click here for more information on NTDs on the WHO website.
NTDs pose heavy socio-economic costs in developing countries every year. Snail fever (Schistosomiasis) alone affects an estimated 250 million people worldwide (CDC 2012), mostly occurring in poverty-stricken areas where communities do not have adequate access to sanitation. So how does remote sensing fit in the picture?
How schistosomiasis is transmitted
Most NTDs are contracted through parasites. In the case of schistosomiasis, the pathogen is mediated by parasitic flatworms called schistosomes. These live in freshwater snails before they take on their free-living swimming form as cercariae. Cercariae are able to penetrate through human skin, and persist into adulthood in the blood vessels that support the liver (Roach 2012).
While community-wide administration of drugs that kill the parasites inside the body is the main tactic for combating this disease, another strategy that is regaining attention is the control of snail populations that harbour the parasites while in their earlier life stages.
Where Remote Sensing fits in the picture
As mentioned earlier, remote sensing is normally associated with earth observation endeavours; its most practical applications being in agriculture and forestry—which are quite far off from epidemiology. It is then quite serendipitous that these disparate fields would lay the groundwork for remote sensing applications that would lend themselves so well in helping to mitigate one of the most prioritised diseases in the world.
Just as environmental factors of rainfall, temperature, and vegetation health are significant in forestry and agriculture, so too are they for identifying the preferred habitats of snail populations that carry schistosomes (Brooker 2002).
Some of the most commonly used remote sensing metrics for determining such environmental factors are LST (Land surface temperature) and NDVI (Normalised Difference Vegetation Index). Watch video below for a brief explanation of how NDVI works, or click here for a refresher video on remote sensing basics.
Through the use of these remote sensing metrics, researchers have been able to make broad predictions as to which areas would be more prone to have infected snail populations. This information is valuable for decision-makers given that considerable financial resources as well as teams of public and environmental health workers are needed to carry out disease intervention campaigns for communities at risk. Information derived from remotely sensed data proves highly useful in identifying and prioritising those areas/communities that face higher risks of disease spread.
Remote sensing is a great tool, but it’s not a catch-all solution
While there is indeed an important role for remote sensing in the field of epidemiology, it is not without its own limitations and margins of error;
It must be acknowledged that remotely sensed data need to be verified with what “ground truths” – that is, data collected from the field, which can give statistical meaning to the data collected from remote sensing; i.e. How sure can you be that the predictions made by remotely sensed data about the prevalence of infected snail population in a given area is accurate to real-life figures? There is still a need for future studies to verify the statistical accuracy of predictions derived from remotely sensed data.
Moreover, there are other factors at play in the spread of diseases like schistosomiasis—human factors, which may not be readily measured by commonly used remote sensing metrics. In light of this, there is a need to explore and seek the integration of other applications of remote sensing, particularly those used in the social sciences.
While it may not be a catch-all solution for predicting phenomena as complex as epidemics, remote sensing can be a powerful tool when used in concert with other tools and datasets. Data collected from the field can help verify and substantiate remotely sensed data, while integrating data derived from other fields of inquiry could help reveal interconnecting factors that improve future predictive models of epidemics.
Brooker, S 2002, “Schistosomes, snails and satellites”, in Acta Tropica, vol. 82, pp. 207-214
CDC 2012, Parasites-Schistosomiasis, Centers for Disease Control and Prevention, viewed 29 May 2016, <http://www.cdc.gov/parasites/schistosomiasis/>
Roach, R 2012, “Schistosomiasis”, in International Public Health Journal, vol. 4, no. 2, pp. 159-165.
Beltran, S, Cézilly, F, and Boissier, J 2008, A schistosome pair with the thin female located within the male gynaecophorical canal, photograph, accessed 31 May 2016, <http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2553268/?tool=pubmed>
Wikimedia commons 2010, Life cycle trematode, diagram, accessed 31 May 2016, <https://commons.wikimedia.org/wiki/File:Trematode_lifecycle_stages.png>