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Now showing 1 - 5 of 15
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    Annex 19 : predicting population-level socio-economic characteristics using Call Detail Records (CDRs) in Sri Lanka
    (2018) Surendra, Aparna; Lokanathan, Sriganesh; Fernando, Lasantha; Perera-Gomez, Thavisha
    National census information is time-consuming and expensive to collect. This research helps determine whether mobile phone data can provide a reliable, cheap proxy for census data within Sri Lanka, especially where post-conflict regions need more frequent data collection. Study findings suggest that socio-economic levels (SEL) can affect call detail records (CDR) data in a post-conflict, Sri Lankan setting. Analysis demonstrates the potential for telecom data to predict census features. The results correspond to assumptions about the population under study, which includes a high percentage of vulnerable, highly mobile groups displaced due to conflict.
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    Annex 19 : predictive model for the dengue incidences in Sri Lanka using mobile network big data
    (2018) Dharmawardana, K.G.S.; Lokuge, K. S.; Dassanayake, P. S. B.; Sirisena, M. L.; Fernando, Lasantha; Perera, Amal Shehan; Lokanathan, Sriganesh
    The study constructs a usable predictive model for any given Medical Officer of Health (MOH) division, which is the smallest medical administrative district in Sri Lanka, by taking human mobility into account. It includes the importation of dengue into immunologically ’naive’ regions. Derived mobility values for each region of the country are weighted using reported past dengue cases. The study introduces a generalizable methodology to fuse big data sources with traditional data sources, using machine learning techniques. Mobile Network Big Data (MNBD) consists of data categories such as Call Detail Records (CDR), Internet access usage records, and airtime recharge records.
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    Final technical report : leveraging mobile network big data for developmental policy
    (2017) LIRNEasia
    The research addresses how big data can provide evidence to better inform public policy and allow for greater use of evidence in the policy making process. In addition to more detailed research in the area of transportation and urban planning (commuting patterns), this research articulates and answers questions in other domains such as health (modeling the spread of diseases) and official statistics (mapping poverty for instance). Guidelines were translated into legal language so that mobile operators can responsibly share data. Traditional survey methods that provide enough detail to accurately assess conditions are costly and can rarely reach a representative portion of the population, especially in poorer areas.
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    Leveraging mobile network big data for developmental policy : final technical report
    This final report reviews research conducted around the potential uses of big data towards better informed public policy, and to expand the use of evidence-based research in the policy making process. Through this project, LIRNEasia extended its pioneering research based on Mobile Network Big Data (MNBD). New sources of data were leveraged in addition to MNBD, including Closed Circuit Television (CCTV), satellite imagery, electricity data, crowd-sourced data, as well as “small” data from government agencies and others. In addition to the generation of actionable insights, LIRNEasia is developing capacity in local universities and among new researchers.
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    Improving disease outbreak forecasting models for efficient targeting of public health resources
    (2018-03) Fernando, Lasantha; Perera, Amal; Lokanathan, Sriganesh
    The forecasting models developed in this work can be utilized to effect better resource mobilisation for combatting dengue. For understanding human mobility in disease propagation, Mobile Network Big Data (MNBD) is a low cost data exhaust that provides rich insight into human mobility patterns, including better spatial and temporal granularity. Research focuses on the development of a human mobility model, using MNBD that can accurately depict aggregate human population movements in Sri Lanka, and from this determine which machine learning technique provides the best disease forecasting model.