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  1. 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. 
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    Free, publicly-accessible full text available May 2, 2024
  2. Abstract

    The management of biological invasions is a worldwide conservation priority. Unfortunately, decision‐making on optimal invasion management can be impeded by lack of information about the biological processes that determine invader success (i.e. biological uncertainty) or by uncertainty about the effectiveness of candidate interventions (i.e. operational uncertainty). Concurrent assessment of both sources of uncertainty within the same framework can help to optimize control decisions.

    Here, we present a Value of Information (VoI) framework to simultaneously analyse the effects of biological and operational uncertainties on management outcomes. We demonstrate this approach with a case study: minimizing the long‐term population growth of musk thistleCarduus nutans, a widespread invasive plant, using several insects as biological control agents, includingTrichosirocalus horridus,Rhinocyllus conicusandUrophora solstitialis.

    The ranking of biocontrol agents was sensitive to differences in the target weed's demography and also to differences in the effectiveness of the different biocontrol agents. This finding suggests that accounting for both biological and operational uncertainties is valuable when making management recommendations for invasion control. Furthermore, our VoI analyses show that reduction of all uncertainties across all combinations of demographic model and biocontrol effectiveness explored in the current study would lead, on average, to a 15.6% reduction in musk thistle population growth rate. The specific growth reduction that would be observed in any instance would depend on how the uncertainties actually resolve. Resolving biological uncertainty (across demographic model combinations) or operational uncertainty (across biocontrol effectiveness combinations) alone would reduce expected population growth rate by 8.5% and 10.5% respectively.

    Synthesis and applications. Our study demonstrates that intervention rank is determined both by biological processes in the targeted invasive populations and by intervention effectiveness. Ignoring either biological uncertainty or operational uncertainty may result in a suboptimal recommendation. Therefore, it is important to simultaneously acknowledge both sources of uncertainty during the decision‐making process in invasion management. The framework presented here can accommodate diverse data sources and modelling approaches, and has wide applicability to guide invasive species management and conservation efforts.

     
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