ORCID Profile
0000-0002-6047-9709
Current Organisation
Hong Kong University of Science and Technology
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Publisher: eLife Sciences Publications, Ltd
Date: 02-04-2019
DOI: 10.7554/ELIFE.43481
Abstract: For countries aiming for malaria elimination, travel of infected in iduals between endemic areas undermines local interventions. Quantifying parasite importation has therefore become a priority for national control programs. We analyzed epidemiological surveillance data, travel surveys, parasite genetic data, and anonymized mobile phone data to measure the spatial spread of malaria parasites in southeast Bangladesh. We developed a genetic mixing index to estimate the likelihood of s les being local or imported from parasite genetic data and inferred the direction and intensity of parasite flow between locations using an epidemiological model integrating the travel survey and mobile phone calling data. Our approach indicates that, contrary to dogma, frequent mixing occurs in low transmission regions in the southwest, and elimination will require interventions in addition to reducing imported infections from forested regions. Unlike risk maps generated from clinical case counts alone, therefore, our approach distinguishes areas of frequent importation as well as high transmission.
Publisher: IEEE
Date: 04-2014
Publisher: MDPI AG
Date: 02-09-2013
DOI: 10.3390/EN6094551
Publisher: Association for Computing Machinery (ACM)
Date: 17-02-2015
DOI: 10.1145/2700265
Abstract: We study the water quality in an urban district, where the surface wind distribution is an essential input but undergoes high spatial and temporal variations due to the impact of surrounding buildings. In this work, we develop an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir using a limited number of wind sensors. Unlike existing solutions that assume Gaussian process of target phenomena, this study measures the wind that inherently exhibits strong non-Gaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons that follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind. The output of sensor placement is a set of the most informative locations to deploy the wind sensors, based on the readings of which we can accurately predict the wind over the entire reservoir in real time. Ten wind sensors are deployed. The in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction scheme provides accurate wind measurement that outperforms the state-of-the-art Gaussian model based on interpolation-based approaches.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2013
Location: No location found
No related grants have been discovered for Mo Li.