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<div><span style="font-family: Arial, sans-serif;"><img alt="Dissertation Defense Announcement at the Cullen College of Engineering" width="600" height="171" style="width: 600px; height: 171px;" src="https://www.egr.uh.edu/sites/www.egr.uh.edu/files/enews/2022/images/dissertation1.png"></span></div>
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<b>Data-driven optimization approaches to improve healthcare access</b></div>
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<b>Bilal Majeed</b></div>
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<span style="font-size: 14px;">April 16, 2024; 10:00 AM - 11:00 AM (CST)<br>
Location: E214 Eng Bld 2<br>
Zoom: </span><span style="font-size: 14px; color: rgb(200, 16, 46);"><a href="https://urldefense.com/v3/__https://www.google.com/url?q=https:**Aus06web.zoom.us*j*87628881755&sa=D&source=calendar&usd=2&usg=AOvVaw3RA-w7RkjP-Of0oyJ6mHEC__;Ly8vLw!!LkSTlj0I!FeHgwonB2kGutFjS28clJlGQAU-bIYLQdjr-QWF8NIAIYMzNNbnGO7y-ZCRnzsnbg0QWZicYE4q5k2LjJ_U6K-Q$" data-auth="NotApplicable" style="color: rgb(200, 16, 46); margin-top: 0px; margin-bottom: 0px;" id="OWAfe5fe4ae-9c8b-975f-5de5-b5d02c635193" class="OWAAutoLink" data-loopstyle="linkonly">https://us06web.zoom.us/j/87628881755</a></span></p>
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<span style="font-size: 14px;"><b>Committee Chair:</b><br>
Jiming Peng, Ph.D.</span></p>
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<span style="font-size: 14px;"><b>Committee Members:</b></span><br>
<span style="font-size: 10px;">Gino Lim, Ph.D. | Ying Lin, Ph.D. | Kathryn Freeman Anderson, Ph.D. | Rigoberto I Delgado, Ph.D.</span></p>
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<b>Abstract</b></p>
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<div style="direction: ltr; font-family: Arial, sans-serif;">The use of data has become crucial in informing healthcare decision-making processes, with data-driven optimization and techniques playing a key role in improving healthcare accessibility and efficiency.
These methods, which range from predicting disease spread to enhancing operational processes, are widely used across various healthcare sectors. This dissertation focuses on using data-driven optimization and techniques to improve the service coverage of Mobile
Health Clinics (MHCs). The dissertation begins by discussing the methods used to collect, clean, and integrate data necessary for the research, followed by a brief analysis. One of the challenges addressed in this research is predicting vaccination rates in
schools and census tracts. This is difficult because information on vaccination rates is limited in schools, while there is more data available for census tracts. To tackle this challenge, a hierarchical forecasting method and a non-convex quadratic optimization
model are proposed, along with their solution methods. Additionally, an integer programming model and a vehicle routing problem are introduced to optimize the deployment and efficiency of MHCs. These models are implemented for a case study in the Houston Independent
School District and surrounding communities. Additionally, a multi-period curve fitting model is outlined to predict the short-term spread of COVID-19 in metropolitan areas of the U.S, with possible extensions to other infectious diseases.<br>
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<td style="direction: ltr;"><span style="font-family: Arial, sans-serif;"><img alt="Engineered For What's Next" width="600" height="82" style="width: 600px; height: 82px;" src="https://www.egr.uh.edu/sites/www.egr.uh.edu/files/enews/2022/images/dissertation2.png"></span></td>
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