Infectious Diseases Outbreak Prediction using Geolocation Data with Machine Learning
Thematic Area: ICTs including big data and artiﬁcial intelligence
University: University of Rwanda (UR)
Project Leader: Dr. Kizito Mkurikeyeyezu
Collaborating Partners: LocationMind Inc, Tokyo, Japan ; Université Gaston Berger, Senegal
Duration: 2 years
Although the recent COVID-19 pandemic is arguably the most disastrous pandemic of the 21st century, disease outbreaks regularly occur, albeit on a geographically limited scope. Likewise, humanity is not safe with known communicable diseases such as Ebola, Zika virus, Cholera, Tuberculosis, and Measles. For the least developed countries, outbreaks of communicable diseases pose particular challenges because of a lack of financial resources to trace, identify, test, and quarantine suspect cases and put pressure on the already overburdened and underfunded healthcare
This research leverages mobile money (MOMO) payment systems widely used in all East African countries. The researchers will develop software that collects geolocation data from MOMO payment transactions. The MOMO data will help estimate the activities and movement patterns of an entire population. Such data, together with live data of infection test results, will allow the researchers to develop contact tracing algorithms and models of infectious disease transmission and identify clusters of infected people and gauge the disease’s super-spreading potential. The proposed approach is low-cost, pragmatic, and is applicable to the context of developing countries.
The proposed research provides a pragmatic, sustainable, and low-cost solution for rapid contact tracing and disease spread modeling that leapfrogs on existing mobile payments. The research will develop tools to collect data and reliable evidence to support public health decision-making by epidemic disease researchers, government authorities, and international organizations.
For more information, please contact the Project Leader.