Introduction video

  A video introducing the AI4COVID project in Sri Lanka.

Overview of research by Prof. Janaka Ekanayake (at FITI 2020 keynote).

We conduct research in the following thrusts

Artificial Intelligence to model and forecast the spread of COVID-19:
We are developing algorithms to predict the COVID-19 case counts, outbreaks and severity on different scenarios. We also attemp to quantify the differential impact of COVID-19 on different communities and individuals..

Computer vision for combatting COVID Pandemic

We are developing computer vision pipelines to asses the risk and suggest preventive measures for COVID-19 transmission in different scenarios


Covid-19 simulator to identify optimal strategies to overcome the pandemic successfully.

We are developing a simulator to simulate the mobility of a population in a predefined virtual environment. This simulator resembles the behavior of a real-world pandemic. Thus, enabling us to identify the dissimilarities between observed vs true infection cases. Also, we can simulate different containment strategies and reopening strategies to find the optimal method that minimizes risks.


Data science for combatting Dengue Pandemic:

We are developing predictive algorithms to tackle the Dengue outbreaks.


Mobile sensing

Developing systems and algorithms to sense human movement/interaction from noisy mobile phone signals.


Spatial analysis of COVID-19 and socio-economic factors in Sri Lanka

The spread of the global Covid-19 pandemic affected Sri Lanka similar to how it affected other countries across the globe. The Sri Lankan government took many preventive measures to suppress the pandemic spread. To aid policy makers in taking these preventive measures, we propose a novel district-wise clustering based approach. Using freely available data from the Epidemiological Department of Sri Lanka, a cluster analysis was carried out based on the Covid-19 data and the demographic data of districts. K-Means clustering and spectral clustering models were the selected clustering techniques in this study. From the many district-wise socio-economic factors, population, population density, monthly expenditure and the education level were identified as the demographic variables that exhibit a high similarity with Covid-19 clusters. This approach will positively impact the preventive measures suggested by the relevant policy making parties of the Sri Lankan government.