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Browsing by Author "Kao, Szu-Yu"

Now showing 1 - 3 of 3
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    A Bi-Level Model for State and County Aquatic Invasive Species Prevention Decisions, Minnesota, 2022
    (2022-03-04) Haight, Robert, G.; Yemshanov, Denys; Kao, Szu-Yu; Phelps, Nicholas, B.D.; Kinsley, Amy, C; robert.haight@usda.gov; Haight, Robert, G.; University of Minnesota, Minnesota Aquatic Invasive Species Research Center; University of Minnesota, School of Public Health; University of Minnesota, Department of Veterinary Population Medicine; Canadian Forest Service, Great Lakes Forestry Centre, Sault Ste. Marie, Ontario, Canada; USDA Forest Service, Northern Research Station, St. Paul, MN, USA
    These data contain a bi-level model for determining how a state planner can efficiently allocate inspection resources to county managers, who independently decide where to locate inspection stations. We apply the model to a hypothetical allocation problem for the state of Minnesota. The model includes the infestation status of 9,182 lakes, of which 471 are infested with zebra mussel, starry stonewort, or Eurasian watermilfoil, and estimates of annual numbers of high-risk boat movements from infested to uninfested lakes.
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    Network connectivity patterns of Minnesota waterbodies and implications for aquatic invasive species prevention
    (2020-10-28) Kao, Szu-Yu; Enns, Eva A; Tomamichel, Megan; Doll, Adam; Escobar, Luis E; Qiao, Huijie; Craft, Meggan E; Phelps, Nicholas B D; phelp083@umn.edu; Phelps, Nicholas B D; Minnesota Aquatic Invasive Species Research Center, University of Minnesota; Division of Health Policy and Management, School of Public Health, University of Minnesota; Odum School of Ecology, University of Georgia; Department of Fish and Wildlife Conservation, Virginia Polytechnic Institute and State University; Key Laboratory of Animal Ecology and Conservation Biology, Chinese Academy of Sciences; Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota
    The data contains simulated boater movements across lakes in the state of Minnesota (MN). The data were simulated based on the boater inspection program conducted by the Minnesota Department of Natural Resources in 2014-2017. Using the inspection survey, we employed machine learning technique, XGBoost, to construct three predictive boater movement models. First, we predicted the number of boater traffic on a lake for a year. Second, we predicted the boater connection between any pair of lakes in MN. Third, we predicted the number of boaters between two lakes that were predicted to have connection.
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    Treat your partners right: Implication of sexual contact networks in partner management for sexually transmitted infections
    (2020-06) Kao, Szu-Yu
    Sexually transmitted infections (STIs) have continued to increase among the heterosexual population and men who have sex with men (MSM) in the last five years. To better inform STI control strategies, the factors that influence the disease dynamics of STIs can be important to incorporate in developing infectious disease modeling for cost-effectiveness analysis. The spread of STIs depends on the macrostructure (e.g., random, clustered, scale-free networks) and microstructure (e.g., relationship dynamics) of the contact networks, and the sexual behaviors (e.g., condom use) commonly adopted in the population. In this thesis, we investigated different aspects that could influence STI transmission and the cost and effectiveness of STI control strategies in the population of interest. First, we evaluated how the structure of sexual contact network influences the cost and effectiveness of partner management strategies controlling for bacterial STIs in MSM. We found that the network structure, the compliance to intervention, and the resource constraint matter in determining the optimal partner management strategy. Second, we evaluated how relationship dynamics affects the cost-effectiveness of partner management strategies and quantified the value of key measures (concurrency and the average relationship duration) that inform relationship dynamics. We found that modeling sexual contact networks without measures informing relationship dynamics might lead to recommendation of a less cost-effective partner management strategy to control bacterial STIs, resulting societal loss. The value of collecting concurrency information is higher than the value of relationship duration. Third, we explored how HIV status disclosure, partner selection and condom use behavior changed with HIV prevalence in MSM using evolutionary game theory. We found that these behaviors varied with HIV prevalence. In particular, HIV-positive individuals were more likely to disclose their status and less likely to use condom at a high HIV prevalence than at a low HIV prevalence. These behavior changes should be considered in cost-effectiveness analysis to better inform interventions of HIV/STIs.