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GSMA started the Big Data for Social Good initiative. The aim of this initiative is to use big data for epidemic and disaster response. Positium was chosen to supply the data analytics for the initiative that covers 19 operators with over 2 billion connections in over 100 countries. To respond effectively and efficiently to the spread of infectious diseases, pollution, earthquakes and other disasters, governments and global relief agencies need to know where the affected people are, in which direction they are moving and how the environment is changing. Mobile operators are cooperating through the GSMA to establish a common framework and ecosystem approach that can support strategic planning, decision-making and support preparedness and response to help people recover from a disaster, contain an epidemic and contend with environmental pollution.
As a result of our work with GSMA, a report was released, which presents analysis carried out with the goal of investigating whether patterns of commuting and other habitual movements, derived from mobile positioning data (MPD), are related to incidence rates of tuberculosis. If such a relationship is established, MPD could be exploited to improve epidemiological models of tuberculosis spread and to inform medical prevention practices towards improved courses of action, for example by predicting the occurrence of tuberculosis in unexplored areas, or identifying areas in need of enhanced diagnostics and treatment efforts. The data analysed in this report are collected from the Indian State of Uttar Pradesh.
Tuberculosis (TB) has been the most lethal infectious agent in the world for the past five years, causing an estimated 1.7 million deaths in 2016. Global efforts to combat the disease are enshrined in the United Nations Sustainable Development Goals. India is the country with the highest burden of the disease, accounting for 27-35% of total infections and an estimated 26% of total deaths. The Government of India has set a target of ending the national TB epidemic, amounting to a 90% reduction incidence rate, by 2025, necessitating the use of innovative strategies, technologies, and datasets (Dowdy et al., 2014, Shrestha et al., 2016).
The analysis in the report seeks, more specifically, to provide a proof-of-concept of validity of MPD for the intended TB epidemiology purposes by exploring the statistical relationship between MPD and TB data. MPD belong to the category of “organic data” (Groves, 2011). This means that the data have not been created with the explicit purpose of data analysis (like survey data are) but instead are produced and recorded for the purpose of carrying out a business process – in this case, for provision and charging of mobile network operator services. Their use for analytic purposes comes as a byproduct.
Read the full report: Investigating the Potential of Mobile
Positioning Data to Improve Epidemiological Models of
Tuberculosis in India.
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