Browsing by Author "Xu, Ran"

Now showing 1 - 6 of 6
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    Dell's SupportAssist customer adoption model: enhancing the next generation of data-intensive support services
    Ghaffarzadegan, Navid; Rad, Armin A.; Xu, Ran; Middlebrooks, Sam E.; Mostafavi, Sarah; Shepherd, Michael; Chambers, Landon; Boyum, Todd (2017-12)
    We developed a decision support system to model, analyze, and improve market adoption of Dell's SupportAssist program. SupportAssist is a proactive and preventive support service capability that can monitor system operations data from all connected Dell devices around the world and predict impending failures in those devices. Performance of such data-intensive services is highly interconnected with market adoption: service performance depends on the richness of the customer database, which is influenced by customer adoption that in turn depends on customer satisfaction and service performancea reinforcing feedback loop. We developed the SupportAssist adoption model (SAAM). SAAM utilizes various data sources and modeling techniques, particularly system dynamics, to analyze market response under different strategies. Dell anticipates improving market adoption of SupportAssist and revenue from support services, as results of using this analytical tool. Copyright (c) 2018 The Authors System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society
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    Enhancing long-term forecasting: Learning from COVID-19 models
    Rahmandad, Hazhir; Xu, Ran; Ghaffarzadegan, Navid (PLOS, 2022-05-01)
    While much effort has gone into building predictive models of the COVID-19 pandemic, some have argued that early exponential growth combined with the stochastic nature of epidemics make the long-term prediction of contagion trajectories impossible. We conduct two complementary studies to assess model features supporting better long-term predictions. First, we leverage the diverse models contributing to the CDC repository of COVID-19 USA death projections to identify factors associated with prediction accuracy across different projection horizons. We find that better long-term predictions correlate with: (1) capturing the physics of transmission (instead of using black-box models); (2) projecting human behavioral reactions to an evolving pandemic; and (3) resetting state variables to account for randomness not captured in the model before starting projection. Second, we introduce a very simple model, SEIRb, that incorporates these features, and few other nuances, offers informative predictions for as far as 20-weeks ahead, with accuracy comparable with the best models in the CDC set. Key to the long-term predictive power of multi-wave COVID-19 trajectories is capturing behavioral responses endogenously: balancing feedbacks where the perceived risk of death continuously changes transmission rates through the adoption and relaxation of various Non-Pharmaceutical Interventions (NPIs).
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    Late retirement, early careers, and the aging of U.S. science and engineering professors
    Ghaffarzadegan, Navid; Xu, Ran (PLOS, 2018-12-26)
    Studies of rescuing early-career scientists often take narrow approaches and focus on PhD students or postdoc populations. In a multi-method systems approach, we examine the inter-relations between the two ends of the pipeline and ask: what are the effects of late retirement on aging and hiring in academia? With a simulation model, we postulate that the decline in the retirement rate in academia contributes to the aging pattern through two mechanisms: (a) direct effect: longer stay of established professors, and (b) indirect effect: a hiring decline in tenure-track positions. Late retirement explains more than half of the growth in average age and brings about 20% decline in hiring. We provide empirical evidence based on the natural experimental set-up of the removal of mandatory retirement in the 1990s.
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    A missing behavioural feedback in COVID-19 models is the key to several puzzles
    Rahmandad, Hazhir; Xu, Ran; Ghaffarzadegan, Navid (BMJ, 2022-10-25)
    Summary: ⇒ Human actions have played a key role in shaping the COVID-19 pandemic patterns. While theoretically recognised, existing models of epidemics often do not endogenously capture many of the feedback loops connecting people’s choices and epidemic dynamics, for example, adoption of non-pharmaceutical interventions (NPIs) by individuals and governments shapes disease transmission, which in turn alters perceived risks and future NPI adoption. ⇒ Such ‘risk-driven response’ feedback is central to explaining important empirical puzzles of the COVID-19 pandemic, including the convergence of reproduction number to 1 across nations, multiple waves of pandemic, mortality variance and limited trade-off between economic and health outcomes in adoption of NPIs. Capturing that feedback also enhances pandemic forecasting and offers distinct and more effective vaccination strategies. ⇒ Much remains to be explored in modelling diverse behavioural feedbacks, from endogenous testing and vaccination choices to the building of infrastructure for various responses. Integrating those with epidemiological models offers promising new discoveries and enhanced policy design.
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    Weather, air pollution, and SARS-CoV-2 transmission: a global analysis
    Xu, Ran; Rahmandad, Hazhir; Gupta, Marichi; DiGennaro, Catherine; Ghaffarzadegan, Navid; Amini, Heresh; Jalali, Mohammad S. (Elsevier, 2021-10-01)
    BACKGROUND: Understanding how environmental factors affect SARS-CoV-2 transmission could inform global containment efforts. Despite high scientific and public interest and multiple research reports, there is currently no consensus on the association of environmental factors and SARS-CoV-2 transmission. To address this research gap, we aimed to assess the relative risk of transmission associated with weather conditions and ambient air pollution. METHODS: In this global analysis, we adjusted for the delay between infection and detection, estimated the daily reproduction number at 3739 global locations during the COVID-19 pandemic up until late April, 2020, and investigated its associations with daily local weather conditions (ie, temperature, humidity, precipitation, snowfall, moon illumination, sunlight hours, ultraviolet index, cloud cover, wind speed and direction, and pressure data) and ambient air pollution (ie, PM2·5, nitrogen dioxide, ozone, and sulphur dioxide). To account for other confounding factors, we included both location-specific fixed effects and trends, controlling for between-location differences and heterogeneities in locations' responses over time. We built confidence in our estimations through synthetic data, robustness, and sensitivity analyses, and provided year-round global projections for weather-related risk of global SARS-CoV-2 transmission. FINDINGS: Our dataset included data collected between Dec 12, 2019, and April 22, 2020. Several weather variables and ambient air pollution were associated with the spread of SARS-CoV-2 across 3739 global locations. We found a moderate, negative relationship between the estimated reproduction number and temperatures warmer than 25°C (a decrease of 3·7% [95% CI 1·9-5·4] per additional degree), a U-shaped relationship with outdoor ultraviolet exposure, and weaker positive associations with air pressure, wind speed, precipitation, diurnal temperature, sulphur dioxide, and ozone. Results were robust to multiple assumptions. Independent research building on our estimates provides strong support for the resulting projections across nations. INTERPRETATION: Warmer temperature and moderate outdoor ultraviolet exposure result in a slight reduction in the transmission of SARS-CoV-2; however, changes in weather or air pollution alone are not enough to contain the spread of SARS-CoV-2 with other factors having greater effects.
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    Why Similar Policies Resulted In Different COVID-19 Outcomes: How Responsiveness And Culture Influenced Mortality Rates
    Lim, Tse Yang; Xu, Ran; Ruktanonchai, Nick; Saucedo, Omar; Childs, Lauren M.; Jalali, Mohammad S.; Rahmandad, Hazhir; Ghaffarzadegan, Navid (Health Affairs, 2023-12)
    In the first two years of the COVID-19 pandemic, per capita mortality varied by more than a hundredfold across countries, despite most implementing similar nonpharmaceutical interventions. Factors such as policy stringency, gross domestic product, and age distribution explain only a small fraction of mortality variation. To address this puzzle, we built on a previously validated pandemic model in which perceived risk altered societal responses affecting SARS-CoV-2 transmission. Using data from more than 100 countries, we found that a key factor explaining heterogeneous death rates was not the policy responses themselves but rather variation in responsiveness. Responsiveness measures how sensitive communities are to evolving mortality risks and how readily they adopt nonpharmaceutical interventions in response, to curb transmission.We further found that responsiveness correlated with two cultural constructs across countries: uncertainty avoidance and power distance. Our findings show that more responsive adoption of similar policies saves many lives, with important implications for the design and implementation of responses to future outbreaks.