Smartphone-based systems for mobile infectious disease detection and epidemiology
Chen, Weili
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https://hdl.handle.net/2142/97677
Description
Title
Smartphone-based systems for mobile infectious disease detection and epidemiology
Author(s)
Chen, Weili
Issue Date
2017-04-07
Director of Research (if dissertation) or Advisor (if thesis)
Cunningham, Brian T.
Doctoral Committee Chair(s)
Cunningham, Brian T.
Committee Member(s)
Bashir, Rashid
Eden, J. Gary
Brooks, Ian
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Mobile health
Genetic testing
Point-of-care
Infectious diseases
Abstract
Infectious diseases remain a serious public health challenge worldwide and are the leading cause of death in many developing countries. The rapid detection of pathogens is vital for the control and prevention of the infectious diseases. New tools are needed to enable rapid detection, identification, and reporting of infectious viral and microbial pathogens in a wide variety of point-of-care applications that impact human and animal health. With the rapid development of mobile technologies, mobile devices have provided a novel and effective approach to identify and report infectious diseases.
In this work, two types of smartphone-based detection platforms are developed for mobile infectious disease detection. The first one is for the detection of human immunodeficiency virus. The second one is for the multiplexed detection of nucleic acids of pathogens for equine respiratory infections. Both platforms utilize a smartphone camera as the sensor in conjunction with a handheld cradle that interfaces the phone with a microchip for the on-chip nucleic acid testing of infectious diseases.
This work provides a mobile, simple and inexpensive capability for clinicians to perform infectious disease diagnostics, and it represents a significant stride towards a practical solution to the infectious disease diagnostics at resource-limited settings.
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