Browsing by Author "Chang, Joshua C"

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    A mathematical model for persistent post-CSD vasoconstriction.
    (PLoS computational biology, 2020-07-15) Xu, Shixin; Chang, Joshua C; Chang, Joshua C; Chow, Carson C; Brennan, KC; Huang, Huaxiong
    Cortical spreading depression (CSD) is the propagation of a relatively slow wave in cortical brain tissue that is linked to a number of pathological conditions such as stroke and migraine. Most of the existing literature investigates the dynamics of short term phenomena such as the depolarization and repolarization of membrane potentials or large ion shifts. Here, we focus on the clinically-relevant hour-long state of neurovascular malfunction in the wake of CSDs. This dysfunctional state involves widespread vasoconstriction and a general disruption of neurovascular coupling. We demonstrate, using a mathematical model, that dissolution of calcium that has aggregated within the mitochondria of vascular smooth muscle cells can drive an hour-long disruption. We model the rate of calcium clearance as well as the dynamical implications on overall blood flow. Based on reaction stoichiometry, we quantify a possible impact of calcium phosphate dissolution on the maintenance of F0F1-ATP synthase activity.
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    Multiple models for outbreak decision support in the face of uncertainty.
    (Proceedings of the National Academy of Sciences of the United States of America, 2023-05) Shea, Katriona; Borchering, Rebecca K; Probert, William JM; Howerton, Emily; Bogich, Tiffany L; Li, Shou-Li; van Panhuis, Willem G; Viboud, Cecile; Aguás, Ricardo; Belov, Artur A; Bhargava, Sanjana H; Cavany, Sean M; Chang, Joshua C; Chen, Cynthia; Chen, Jinghui; Chen, Shi; Chen, YangQuan; Childs, Lauren M; Chow, Carson C; Crooker, Isabel; Del Valle, Sara Y; España, Guido; Fairchild, Geoffrey; Gerkin, Richard C; Germann, Timothy C; Gu, Quanquan; Guan, Xiangyang; Guo, Lihong; Hart, Gregory R; Hladish, Thomas J; Hupert, Nathaniel; Janies, Daniel; Kerr, Cliff C; Klein, Daniel J; Klein, Eili Y; Lin, Gary; Manore, Carrie; Meyers, Lauren Ancel; Mittler, John E; Mu, Kunpeng; Núñez, Rafael C; Oidtman, Rachel J; Pasco, Remy; Pastore Y Piontti, Ana; Paul, Rajib; Pearson, Carl AB; Perdomo, Dianela R; Perkins, T Alex; Pierce, Kelly; Pillai, Alexander N; Rael, Rosalyn Cherie; Rosenfeld, Katherine; Ross, Chrysm Watson; Spencer, Julie A; Stoltzfus, Arlin B; Toh, Kok Ben; Vattikuti, Shashaank; Vespignani, Alessandro; Wang, Lingxiao; White, Lisa J; Xu, Pan; Yang, Yupeng; Yogurtcu, Osman N; Zhang, Weitong; Zhao, Yanting; Zou, Difan; Ferrari, Matthew J; Pannell, David; Tildesley, Michael J; Seifarth, Jack; Johnson, Elyse; Biggerstaff, Matthew; Johansson, Michael A; Slayton, Rachel B; Levander, John D; Stazer, Jeff; Kerr, Jessica; Runge, Michael C
    Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.