new article: ecological modelling to improve remote sensing disease risk analysis

new article: ecological modelling to improve remote sensing disease risk analysis

November 30, 2015

A new article by our former PhD student Dr. Yvonne Walz just got published. The article “Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling” aims at the advantages of using spatial modelling approaches for disease risk analysis using remote sensing. Schistosomiasis is a widespread water-based disease that puts close to 800 million people at risk of infection with more than 250 million infected, mainly in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and the frequency, duration and extent of human bodies exposed to infested water sources during human water contact. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. Since schistosomiasis risk profiling based on remote sensing data inherits a conceptual drawback if school-based disease prevalence data are directly related to the remote sensing measurements extracted at the location of the school, because the disease transmission usually does not exactly occur at the school, we took the local environment around the schools into account by explicitly linking ecologically relevant environmental information of potential disease transmission sites to survey measurements of disease prevalence. Our models were validated at two sites with different landscapes in Côte d’Ivoire using high- and moderateresolution remote sensing data based on random forest and partial least squares regression. We found that the ecologically relevant modelling approach explained up to 70% of the variation in Schistosoma infection prevalence and performed better compared to a purely pixelbased modelling approach. Furthermore, our study showed that model performance increased as a function of enlarging the school catchment area, confirming the hypothesis that suitable environments for schistosomiasis transmission rarely occur at the location of survey measurements.

http://geospatialhealth.net/index.php/gh/article/view/398

follow us and share it on:

you may also like:

MainPro remote sensing analysis products are now available online

MainPro remote sensing analysis products are now available online

Within our interdisciplinary research project MainPro, we aim to analyse potential climate change induced geohazards in the Main valley and its tributaries and develop nature-based solutions for them.  This project involves a large-scale analysis of potential...

Successful MSc Defense by Laura Obrecht

Successful MSc Defense by Laura Obrecht

At the recent EAGLE MSc defenses, Laura Obrecht presented her thesis on the detection of grassland mowing events using Sentinel-1 InSAR coherence and deep learning approaches. Her work, titled “Detektion von Grünlandmahd mit Sentinel-1 InSAR Coherence und einem Deep...

Interdisciplinary project MONID HABITRACK – press release

Interdisciplinary project MONID HABITRACK – press release

Tick-borne diseases such as Lyme disease and tick-borne encephalitis (TBE/FSME) are becoming an increasing concern in many regions of Germany. A new interdisciplinary research project, MONID HABITRACK (Habitat Prediction and Surveillance of Tick-borne Diseases using...

Share This