Dengue


Dharmawardana, K. G. S., Lokuge, J. N.
Fernando, L., Perera, A. S., Lokanathan, S., Ghouse, A.
Ideally, we would have had findings. But we are in the middle of research, so what we can present is work in progress: problems that have been faced; those that have been solved; those we’re still working on; etc. Hopefully, once we get our hands on the needed epidemiological data we will present findings in a few months. We are grateful to the incoming President of the Commonwealth Medical Association, Professor Vajira Dissanayake, for creating this opportunity for us. The presentation was made at a session chaired by Dr Hasitha Tissera, the Head of the Epidemiology Unit of the Ministry of Health.
LIRNEasia has been at the forefront of big data analysis for development in Sri Lanka, conducting in-house analysis to generate actionable insights across a range of policy domains. On 6th May 2016, LIRNEasia and the Health Informatics Society of Sri Lanka jointly convened a planning meeting on building better models for forecasting the propagation of infectious disease such as dengue in Sri Lanka. The meeting was intended to lay the foundation for a multi-disciplinary collaboration engaging health informatics specialists, epidemiologists, and data scientists to identify research priorities and opportunities. The participants included the following: Madhushi Bandara, Junior Researcher, LIRNEasia Prof Vajira Dissanayake (Health Informatics Society of Sri Lanka, Biomedical Informatics Programme – Postgraduate Institute of Medicine) Dr. M.
“Leptospirosis is out and Dengue is in” – these are the words of the Sarvodaya Research Assistant – Pubudini weerakoon – working in Kurunegala District of Sri Lanka on the real-time biosurveillance program (RTBP). This report on Leptospirosis in Sri Lanka gives a full account of the past events. The aim of the RTBP is to gather patient case information through the m-Healthsurvey mobile application and subject that data to real time analysis for rapid detection of emerging health events. The automated analytic and detection is driven by the T-Cube software, developed by Carnegie Mellon Universities Auton Lab, based on data mining principles. We took the weekly epidemiological reports (WER) from the past two years and put T-Cube to the test.