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| 1 | Measurements of electron-impact ionization cross sections of Ar,Kr,and Xe by comparison with photoionization显示文摘 | Sorokin A A Shmaenok L A Bobashev S V | 2000 | Physical Review A2000，61，02： | 1 |
| 2 | Deceleration of supersonic plasma flow by an applied magnetic field 显示文摘 | Bobashev S V Golovachov Y P Van Wie D M | 2003 | Journal of Propulsion and Power2003，19，4： | 1 |
| 3 | Influence of electric and magnetic fields on shock-wave configuration in diffuser显示文摘 | Lapushkina T A Bobashev S V Vasil' eva R V Erofeev A V | 2002 | Technical Physics2002，72，4： | 1 |
| 4 | High dead-space syringes and the risk of HIV and HCV infection among injecting drug users显示文摘 | Zule WA Bobashev G | 2009 | Drug Al- cohol Depend2009，100，： | 1 |
| 5 | Neurocognitive characterizations of Russian heroin addicts without a significant history of other drug use显示文摘 | Fishbein D.H Krupitsky E Flannery B.A Langevin D.J Bobashev G Verbitskaya E Tsoy M | | 0，，01： | 1 |
| 6 | Researching a local heroin market as a complex adaptive system显示文摘 | Hoffer LD Bobashev G Morris RJ | 2009 | Am J Community Psychol2009，44，34： | 1 |
| 7 | Impacts of timing,length,and intensity of behavioral interventions to COVID-19 dynamics:North Carolina countylevel examples显示文摘We sought to examine how the impact of revocable behavioral interventions,e.g.,shelterin-place,varies throughout an epidemic,as well as the role that the proportion of susceptible individuals had on an intervention's impact.We estimated the theoretical impacts of start day,length,and intensity of interventions on disease transmission and illustrated them on COVID-19 dynamics inWake County,North Carolina,to inform how interventions can be most effective.We used a Susceptible,Exposed,Infectious,and Recovered(SEIR)model to estimate epidemic curves with modifications to the disease transmission parameter(β).We designed modifications to simulate events likely to increase transmission(e.g.,long weekends,holiday seasons)or behavioral interventions likely to decrease it(e.g.,shelter-in-place,masking).We compared the resultant curves'shape,timing,and cumulative case count to baseline and across other modified curves.Interventions led to changes in COVID-19 dynamics,including moving the peak's location,height,and width.The proportion susceptible,at the start day,strongly influenced their impact.Early interventions shifted the curve,while interventions near the peak modified shape and case count.For some scenarios,in which the transmission parameter was decreased,the final cumulative count increased over baseline.We showed that the timing of revocable interventions has a strong impact on their effect.The same intervention applied at different time points,corresponding to different proportions of susceptibility,resulted in qualitatively differential effects.Accurate estimation of the proportion susceptible is critical for understanding an intervention's impact.The findings presented here provide evidence of the importance of estimating the proportion of the population that is susceptible when predicting the impact of behavioral infection control interventions.Greater emphasis should be placed on the estimation of this epidemic component in intervention design and decision-making.Our results are generic and are applicable to other infectious disease epidemics,as well as to future waves of the current COVID-19 epidemic.Developed into a publicly available tool that allows users to modify the parameters to estimate impacts of different interventions,these models could aid in evaluating behavioral intervention options prior to their use and in predicting case increases from specific events. | Claire Quiner Kasey Jones Georgiy Bobashev | 2022 | Infectious Disease Modelling2022，7，3： | 0 |
| 8 | Incorporation of near-real-time hospital occupancy data to improve hospitalization forecast accuracy during the COVID19 pandemic显示文摘Public health decision makers rely on hospitalization forecasts to inform COVID-19 pandemic planning and resource allocation.Hospitalization forecasts are most relevant when they are accurate,made available quickly,and updated frequently.We rapidly adapted an agent-based model(ABM)to provide weekly 30-day hospitalization forecasts(i.e.,demand for intensive care unit[ICU]beds and non-ICU beds)by state and region in North Carolina for public health decision makers.The ABM was based on a synthetic population of North Carolina residents and included movement of agents(i.e.,patients)among North Carolina hospitals,nursing homes,and the community.We assigned SARSCoV-2 infection to agents using county-level compartmental models and determined agents’COVID-19 severity and probability of hospitalization using synthetic population characteristics(e.g.,age,comorbidities).We generated weekly 30-day hospitalization forecasts during MayeDecember 2020 and evaluated the impact of major model updates on statewide forecast accuracy under a SARS-CoV-2 effective reproduction number range of 1.0e1.2.Of the 21 forecasts included in the assessment,the average mean absolute percentage error(MAPE)was 7.8%for non-ICU beds and 23.6%for ICU beds.Among the major model updates,integration of near-real-time hospital occupancy data into the model had the largest impact on improving forecast accuracy,reducing the average MAPE for non-ICU beds from 6.6%to 3.9%and for ICU beds from 33.4%to 6.5%.Our results suggest that future pandemic hospitalization forecasting efforts should prioritize early inclusion of hospital occupancy data to maximize accuracy. | Alexander Preiss Emily Hadley Kasey Jones Marie C.D.Stoner Caroline Kery Peter Baumgartner Georgiy Bobashev Jessica Tenenbaum Charles Carter Kimberly Clement Sarah Rhea | 2022 | Infectious Disease Modelling2022，7，1： | 0 |
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