Using AI to contain COVID-19 and future epidemics in Malaysia and Sri Lanka with a focus on women, children, and underprivileged groups
The COVID-19 crisis is being called a “data-driven pandemic” – that is, massive amounts of information and data are being released and shared at a scale that has never been seen before. Across the world, Artificial Intelligence (AI) and data science research is showing promise for early COVID detection, timely communications with the public, new diagnostic tools; and informed policy and public health responses that can be automated, implemented and scaled affordably. AI and data science methodologies are particularly well suited to pattern recognition, forecasting, and automation. Dashboards can help to relay risk and hotspots to policy makers, help support at-home self-testing and advice, as well as supporting care practitioners with medical diagnosis and patient triage. AI and data science research should call into consideration the needs of women and other vulnerable groups or may risk exacerbating existing inequalities.
This project from the University of Peradeniya in Sri Lanka will use an Artificial Intelligence (AI) framework to assess and contain COVID-19 and future epidemics while mitigating the socio-economic impact to women, children, and underprivileged groups in Malaysia and Sri Lanka. Based on generated behaviour and movements, the project will develop AI to conduct contact tracing and socioeconomic impact mitigation actions in a more informed, socially conscious and responsible manner in the case of the next wave of COVID-19 infections or a different future infectious disease. The project will develop a set of recommendations that policy makers and medical practitioners can access.
This work will be carried out as part of the COVID-19 Global South Artificial Intelligence and Data Innovation Program, a program funded by Canada’s International Development Research Centre and the Swedish International Development Cooperation Agency.
Project ID
109586
Project Status
Active
Duration
24 months
IDRC Officer
Anindya Chaudhuri
Total Funding
CA$ 846,700.00
Location
Malaysia
Sri Lanka
Programs
Education and Science
Education and Science
Networked Economies
Institution Country
Sri Lanka
Project Leader
Janaka Ekanayake
Institution
University of Peradeniya
Institution Website
Outputs
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Artificial intelligence framework for threat assessment and containment for covid-19 and future epidemics while mitigating the socioeconomic impact to women, children, and underprivileged groups Article
Author(s): Ilangarathna, G., Weligampola, H., Ranasinghe, Y., Attygalla, E., Godaliyaddha, R. Language: English |
Prerequisite for COVID-19 prediction : a review on factors affecting the infection rate Article
Author(s): Tang, Shirley Gee Hoon, Hadi, Muhamad Haziq Hasnul, Arsad, Siti Rosilah, Ker, Pin Jern, Ramanathan, Santhi Language: English |
Comprehensive overview of education during three COVID-19 pandemic periods : impact on engineering students in Sri Lanka Article
The study provided an overview of changes in the educational system due to the COVID-19 pandemic among engineering undergraduates of Sri Lanka. Results show that students’ attendance in online classes improved over time compared to the initial pandemic period. Nearly 50% of students’ family income was impacted- either stopped or reduced due to the pandemic. Most students have issues regarding computing devices, internet connectivity, and the home environment, which are not conducive to learning at home. Under normal circumstances, engineering undergraduates in Sri Lanka have high exposure to modern technology and a diversity of instructional delivery, hence this student cohort was chosen for the study. Author(s): Ilangarathna, Gayanthi A., Ranasinghe, Yasiru, Weligampola, Harshana, Attygalla, Erandi, Ekanayake, Janaka Language: English |
Hands off : a handshake interaction detection and localization model for COVID-19 threat control Paper
A handshake interaction localization model in real-time that may help mitigate the threat for transmitting COVID-19, is presented using computer vision in a non-intrusive technique. A real-time detection model (using YOLO/you only look once) is proposed to identify handshake interactions in realistic scenarios. YOLO can detect multiple interactions in a single frame. The model can be applied to public spaces to identify handshake interactions. The study is the first to use a human interaction localization model in a multi-person setting. YOLO is a convolutional neural network (CNN) for object detection in real-time. Author(s): Jameel Hassan, A. S., Sritharan, Suren, Jayatilaka, Gihan, Godaliyadda, Roshan I., Ekanayake, Parakrama B., Herath, Vijitha, Ekanayake, Janaka B. Language: English |
Spatial analysis of COVID-19 and socio-economic factors in Sri Lanka Paper
Using data from the Epidemiological Department of Sri Lanka, a cluster analysis was carried out based on COVID-19 data and demographic data of districts, towards developing a mathematical model that can identify and describe socio-economic factors related to pandemic measures. Population and population density, monthly expenditure, and education level are suggested as main factors for policy makers consideration. Findings can support future evidence-based COVID-19 policies, and further utilized as a foundation for other epidemiological models. A challenge in the study was the presumed disparity between actual COVID-19 cases and observed COVID-19 cases, thereby depicting an inaccurate measure of COVID-19 severity. Author(s): Perera, Rumali, Weligampola, Harshana, Marikkar, Umar, Sritharan, Suren, Godaliyadda, Roshan, Ekanayake, Parakrama, Herath, Vijitha, Rathnayake, Anuruddhika, Dharmaratne, Samath Language: English |