Optimal Screening, Monitoring, and Prevention Strategies in Infectious Disease Management
2021-01
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Optimal Screening, Monitoring, and Prevention Strategies in Infectious Disease Management
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2021-01
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Abstract
Mathematical models of infectious disease such as Markov models, dynamic compartmentalmodels have been increasingly utilized in medical decision making. Most studies
primarily focus on assessing the effectiveness, cost-effectiveness of policies, interventions by
balancing costs and direct health benets (often in qualify-adjusted life-years gained, or
disability-adjusted life-years averted). There are challenges with this classical approach.
First, it may overlook the future impact of the current decision. For example, in treating
bacterial infections, antibiotic over-prescription is an increasingly urgent healthcare issue
to be addressed. Second, previous works focus less on incorporating individual
response and heterogeneity effect into an infectious disease control policy optimization
setting. In Chapter 2, we address the antibiotic over-prescription in febrile illness management,by formulating the problem of minimizing the weighted average of antibiotic
underuse and overuse to inform the optimal diagnostic test and antibiotic treatment
options for given occurrence probabilities of several bacterial and viral infections. The model accounted for multiple infections simultaneously and incorporated test, treatment,
and other direct and indirect costs, as well as the effect of delays in seeking
care and test turnaround times. We used the Markov models to numerically estimate
disability-adjusted life years (DALYs), pre-penalty costs, and the likelihood of antibiotics
overuse per patient for fifteen different strategies in Thailand settings (a typical
viral and bacterial endemic setting). In Chapter 3, we formulate a Markov decision process to address patient adherenceheterogeneity by optimizing viral load monitoring strategies for HIV-infected patients. In Chapter 4, we provide a framework to optimize public health control policies inresponding to an infectious disease outbreak like the COVID-19 pandemic. We use a multinomial
discrete choice model to characterize an individual activity level and integrate it into a repeated game-theoretical model with a SIR disease transmission dynamics. We
derive a few insightful structural properties from these models and conduct numerical
studies based on representative data for COVID-19 in Minnesota. We conclude with a discussion of the work and directions for future research inChapter 5.
Description
University of Minnesota Ph.D. dissertation. January 2021. Major: Industrial Engineering. Advisors: Ying Cui, Eva Enns. 1 computer file (PDF); x, 193 pages.
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Zhang, Zhenhuan. (2021). Optimal Screening, Monitoring, and Prevention Strategies in Infectious Disease Management. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/220592.
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