GIS Health News Weekly: Animal and People Pathogens
Researchers at the University of Liverpool's Institute of Infection and Global Health are building the world's most comprehensive database describing human and animal pathogens. Called the Enhanced Infectious Diseases (EID2) database it maps the relationships between human and animal diseases and their hosts, disease-causing pathogens and the ways in which pathogens are transmitted can offer huge benefits when it comes to knowing what the disease risks are in a population or geographical area, and how best to manage and eliminate them.
Satellite Imagery and Big Data Tackle Disease in Senegal
An international research team combined high-resolution maps with advances in species distribution models to simulate the region’s riverine environment. That helped them locate the microhabitats where tsetse flies, which also cause sleeping sickness in humans, were most likely to be found. And that, in turn, allowed the Pan African Tsetse and Trypanosomiasis Eradication Campaign to decide where to place poisoned traps to kill the insects, which was followed up by the strategic release of sterilized males to disrupt surviving flies’ breeding cycles.
Cancer in Ireland Varies by...Geography
A map developed as part of the Irish Examiner’s county by county report on the state of Ireland’s health has revealed wide variations in both the incidence and mortality rates for all the main cancer types.
What did they expect? (Image at right)
Trachoma Mapping Project: Ahead of Schedule and Under Budget
The Global Trachoma Mapping Project (GTMP), funded by the UK government, just finished its second year. The news is good: 94 per cent of the identified districts surveyed nine months ahead of schedule and under budget. And, the work is done with smartphones.
This mapping project has involved the global rollout of a standardised assessment process which uses trained eye health workers and a smartphone. Survey teams visit and examine people living in a sample of communities within pre-identified districts and capture data on the presence of the disease. This is then used to update ministry of health trachoma action plans and the Trachoma Atlas – an ITI tool for tracking the global burden of trachoma. Essential data about water and sanitation access are also captured.
Wikipedia for Disease Prediction?
A post at Health Map suggest Wikipedia may be a better source that Twitter or Google searches for data used to predict the spread of disease. Why?
Wikipedia, unlike Twitter and Google, makes its complete data freely available for anyone to download. Every hour, access logs containing the number of views each Wikipedia page receives in each language are released. However, unlike Twitter and Google, Wikipedia data does not contain any explicit geo-location information.
Wikipedia data are released and aggregated at the language level. If the geographic distribution of language speakers is mostly clustered in a single location, then one can assume that most of those speakers are in that location. For example, most Thai speakers are in Thailand; therefore, we can assume that data from the Thai Wikipedia are coming mostly from Thailand. The same is true for Polish speakers (concentrated in Poland), and many other language-location pairings.
The data worked well in models for some diseases, but less well for others.