Posts by Collection

Impact of Human Mobility on Spread of Dengue in Sri Lanka

Published in Milan, Italy, 2017

Human mobility plays a significant role in spatio-temporal propagation of infectious diseases. But how much of an impact does human mobility have on propagating a dengue outbreak in a dengue endemic country such as Sri Lanka?

Recommended citation: Fernando, L., Perera, A. S., Lokanathan, S., Ghouse, A., & Tissera, H. (2017). Impact of Human Mobility on Spread of Dengue in Sri Lanka. In Book of Abstracts (Oral) of 5th International Conference on Scientific Analysis of Mobile Phone datasets (NetMob) (pp. 75–77). Milan, Italy http://lasanthafdo.github.io/files/netmob-cdr-dengue.pdf

Predictive model for the dengue incidences in Sri Lanka using mobile network big data

Published in Kandy, Sri Lanka, 2017

Multiple studies in recent literature have shown that human mobility has a considerable effect on the importation of dengue to an immunologically dengue ‘naive’ regions. The purpose of this study is to validate whether this fact is applicable to dengue endemic regions as well as assess the impact of human mobility when developing predictive models for dengue incidence of a particular geographical region.

Recommended citation: Dharmawardana, K. G. S., Lokuge, J. N., Dassanayake, P. S. B., Sirisena, M. L., Fernando, M. L., Perera, A. S., & Lokanathan, S. (2017, December). Predictive model for the dengue incidences in Sri Lanka using mobile network big data. In Industrial and Information Systems (ICIIS), 2017 IEEE International Conference on (pp. 1-6). IEEE http://lasanthafdo.github.io/files/iciis-dengue-predictive-models.pdf

Predicting population-level socio-economic characteristics using Call Detail Records (CDRs) in Sri Lanka

Published in Houston, TX, USA, 2018

Prior work has shown that mobile network big data can be used as a high-frequency alternative data source to derive proxy measures that have strong predictive capacity to estimate census and poverty data in developing countries. Given that the observations from these studies can be dependent on local context and regional characteristics, we replicate this work targeting two regions in Sri Lanka

Recommended citation: Fernando, L., Surendra, A., Lokanathan, S., & Gomez, T. (2018, June). Predicting population-level socio-economic characteristics using Call Detail Records (CDRs) in Sri Lanka. In Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets (p. 1). ACM. http://lasanthafdo.github.io/files/dsmm-socio-economic.pdf

Spatio-temporal forecasting of dengue outbreaks

Published: September 01, 2016

In this project, we use mobile network big data to model human movement patterns. Then using these human mobility models, we make use of other related data sources as well to develop machine learning models to predict dengue outbreaks for a spatial administrative district of Sri Lanka, 2 weeks ahead of time.

Predicting population level socio-economic characteristics using call detail records (CDRs)

Published: June 01, 2018

Usage patterns in calling behaviour as well as movement patterns have been shown to correlate with socio-economic levels. In this project, we use Call Detail Records (CDRs) to derive proxy indicators on mobility, consumption and social behaviours of mobile phone subscribers at a large scale to investigate which indicators correlate most in a Sri Lankan regional context.

Privacy-preserving CDRs from mobile phone subscriber profiles

Published: March 25, 2019

Generated fictional privacy-preserving CDRs that maintain population-level behavioral characteristics of the original data set for sharing purposes.

Predicting migration from the ego network of a mobile phone subscriber

Published: January 03, 2020

In this project, we developed novel measures of social and spatial entropy using large-scale pseudonymized CDRs in Sri Lanka by extending prior work. Currently exploring the results to derive possible migration and mobility behaviour related insights of an ego user.

Supervisor - Final Year Undergraduate Projects

Project Supervision, University of Moratuwa, Department of Computer Science & Engineering, 2016

Supervised multiple ftinal year undergradute group projects related to large scaled data processing frameworks and epidemic modeling.

Teaching Assistant - Undergradute Courses

Undergraduate courses, University of Moratuwa, Department of Computer Science & Engineering, 2016

Teaching assistant for multiple undergraduate computer science courses.

Visiting Lecturer - Undergraduate Courses

Undergraduate courses, University of Moratuwa, Department of Computer Science & Engineering, 1900

Conducted visiting lectures for the final year undergradute courses on Machine Learning in 2017 and 2018. I also conducted visiting lectures for the Distributed Systems course in 2019.

Guest Lecturer - Graduate Courses

Graduate courses, University of Moratuwa, Department of Computer Science & Engineering, 2017

Conducted guest lectures for master’s level gradute course on Big Data Analytics Technologies.