Multiple models for outbreak decision support in the face of uncertainty.

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.

Vote-processing rules for combining control recommendations from multiple models.

Mathematical modelling is used during disease outbreaks to compare control interventions. Using multiple models, the best method to combine model recommendations is unclear. Existing methods weight model projections, then rank control interventions using the combined projections, presuming model outputs are directly comparable. However, the way each model represents the epidemiological system will vary. We apply electoral vote-processing rules to combine model-generated rankings of interventions. Combining rankings of interventions, instead of combining model projections, avoids assuming that projections are comparable as all comparisons of projections are made within each model. We investigate four rules: First-past-the-post, Alternative Vote (AV), Coombs Method and Borda Count. We investigate rule sensitivity by including models that favour only one action or including those that rank interventions randomly. We investigate two case studies: the 2014 Ebola outbreak in West Africa (37 compartmental models) and a hypothetical foot-and-mouth disease outbreak in UK (four individual-based models). The Coombs Method was least susceptible to adding models that favoured a single action, Borda Count and AV were most susceptible to adding models that ranked interventions randomly. Each rule chose the same intervention as when ranking interventions by mean projections, suggesting that combining rankings provides similar recommendations with fewer assumptions about model comparability. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

The role of demographic compensation in stabilising marginal tree populations in North America.

Demographic compensation-the opposing responses of vital rates along environmental gradients-potentially delays anticipated species' range contraction under climate change, but no consensus exists on its actual contribution. We calculated population growth rate (λ) and demographic compensation across the distributional ranges of 81 North American tree species and examined their responses to simulated warming and tree competition. We found that 43% of species showed stable population size at both northern and southern edges. Demographic compensation was detected in 25 species, yet 15 of them still showed a potential retraction from southern edges, indicating that compensation alone cannot maintain range stability. Simulated climatic warming caused larger decreases in λ for most species and weakened the effectiveness of demographic compensation in stabilising ranges. These findings suggest that climate stress may surpass the limited capacity of demographic compensation and pose a threat to the viability of North American tree populations.

Weighing the unknowns: Value of Information for biological and operational uncertainty in invasion management.

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 thistle Carduus nutans, a widespread invasive plant, using several insects as biological control agents, including Trichosirocalus horridus, Rhinocyllus conicus and Urophora 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.

Concurrent assessment of epidemiological and operational uncertainties for optimal outbreak control: Ebola as a case study.

Determining how best to manage an infectious disease outbreak may be hindered by both epidemiological uncertainty (i.e. about epidemiological processes) and operational uncertainty (i.e. about the effectiveness of candidate interventions). However, these two uncertainties are rarely addressed concurrently in epidemic studies. We present an approach to simultaneously address both sources of uncertainty, to elucidate which source most impedes decision-making. In the case of the 2014 West African Ebola outbreak, epidemiological uncertainty is represented by a large ensemble of published models. Operational uncertainty about three classes of interventions is assessed for a wide range of potential intervention effectiveness. We ranked each intervention by caseload reduction in each model, initially assuming an unlimited budget as a counterfactual. We then assessed the influence of three candidate cost functions relating intervention effectiveness and cost for different budget levels. The improvement in management outcomes to be gained by resolving uncertainty is generally high in this study; appropriate information gain could reduce expected caseload by more than 50%. The ranking of interventions is jointly determined by the underlying epidemiological process, the effectiveness of the interventions and the size of the budget. An epidemiologically effective intervention might not be optimal if its costs outweigh its epidemiological benefit. Under higher-budget conditions, resolution of epidemiological uncertainty is most valuable. When budgets are tight, however, operational and epidemiological uncertainty are equally important. Overall, our study demonstrates that significant reductions in caseload could result from a careful examination of both epidemiological and operational uncertainties within the same modelling structure. This approach can be applied to decision-making for the management of other diseases for which multiple models and multiple interventions are available.

Essential information: Uncertainty and optimal control of Ebola outbreaks.

Early resolution of uncertainty during an epidemic outbreak can lead to rapid and efficient decision making, provided that the uncertainty affects prioritization of actions. The wide range in caseload projections for the 2014 Ebola outbreak caused great concern and debate about the utility of models. By coding and running 37 published Ebola models with five candidate interventions, we found that, despite this large variation in caseload projection, the ranking of management options was relatively consistent. Reducing funeral transmission and reducing community transmission were generally ranked as the two best options. Value of information (VoI) analyses show that caseloads could be reduced by 11% by resolving all model-specific uncertainties, with information about model structure accounting for 82% of this reduction and uncertainty about caseload only accounting for 12%. Our study shows that the uncertainty that is of most interest epidemiologically may not be the same as the uncertainty that is most relevant for management. If the goal is to improve management outcomes, then the focus of study should be to identify and resolve those uncertainties that most hinder the choice of an optimal intervention. Our study further shows that simplifying multiple alternative models into a smaller number of relevant groups (here, with shared structure) could streamline the decision-making process and may allow for a better integration of epidemiological modeling and decision making for policy.

Genetic variation facilitates seedling establishment but not population growth rate of a perennial invader.

BACKGROUND AND AIMS: Assessing the demographic consequences of genetic variation is fundamental to invasion biology. However, genetic and demographic approaches are rarely combined to explore the effects of genetic variation on invasive populations in natural environments. This study combined population genetics, demographic data and a greenhouse experiment to investigate the consequences of genetic variation for the population fitness of the perennial, invasive herb Lupinus polyphyllus. METHODS: Genetic and demographic data were collected from 37 L. polyphyllus populations representing different latitudes in Finland, and genetic variation was characterized based on 13 microsatellite loci. Associations between genetic variation and population size, population density, latitude and habitat were investigated. Genetic variation was then explored in relation to four fitness components (establishment, survival, growth, fecundity) measured at the population level, and the long-term population growth rate (λ). For a subset of populations genetic variation was also examined in relation to the temporal variability of λ. A further assessment was made of the role of natural selection in the observed variation of certain fitness components among populations under greenhouse conditions. KEY RESULTS: It was found that genetic variation correlated positively with population size, particularly at higher latitudes, and differed among habitat types. Average seedling establishment per population increased with genetic variation in the field, but not under greenhouse conditions. Quantitative genetic divergence (Q(ST)) based on seedling establishment in the greenhouse was smaller than allelic genetic divergence (F'(ST)), indicating that unifying selection has a prominent role in this fitness component. Genetic variation was not associated with average survival, growth or fecundity measured at the population level, λ or its variability. CONCLUSIONS: The study suggests that although genetic variation may facilitate plant invasions by increasing seedling establishment, it may not necessarily affect the long-term population growth rate. Therefore, established invasions may be able to grow equally well regardless of their genetic diversity.

Mobile dune fixation by a fast-growing clonal plant: a full life-cycle analysis.

Desertification is a global environmental problem, and arid dunes with sparse vegetation are especially vulnerable to desertification. One way to combat desertification is to increase vegetation cover by planting plant species that can realize fast population expansion, even in harsh environments. To evaluate the success of planted species and provide guidance for selecting proper species to stabilize active dunes, demographic studies in natural habitats are essential. We studied the life history traits and population dynamics of a dominant clonal shrub Hedysarum laeve in Inner-Mongolia, northern China. Vital rates of 19057 ramets were recorded during three annual censuses (2007-2009) and used to parameterize Integral Projection Models to analyse population dynamics. The life history of H. laeve was characterized by high ramet turnover and population recruitment entirely depended on clonal propagation. Stochastic population growth rate was 1.32, suggesting that the populations were experiencing rapid expansion. Elasticity analysis revealed that clonal propagation was the key contributor to population growth. The capacity of high clonal propagation and rapid population expansion in mobile dunes makes H. laeve a suitable species to combat desertification. Species with similar life-history traits to H. laeve are likely to offer good opportunities for stabilizing active dunes in arid inland ecosystems.

Permanent genetic resources added to Molecular Ecology Resources Database 1 February 2013-31 March 2013.

This article documents the addition of 142 microsatellite marker loci to the Molecular Ecology Resources database. Loci were developed for the following species: Agriophyllum squarrosum, Amazilia cyanocephala, Batillaria attramentaria, Fungal strain CTeY1 (Ascomycota), Gadopsis marmoratus, Juniperus phoenicea subsp. turbinata, Liriomyza sativae, Lupinus polyphyllus, Metschnikowia reukaufii, Puccinia striiformis and Xylocopa grisescens. These loci were cross-tested on the following species: Amazilia beryllina, Amazilia candida, Amazilia rutila, Amazilia tzacatl, Amazilia violiceps, Amazilia yucatanensis, Campylopterus curvipennis, Cynanthus sordidus, Hylocharis leucotis, Juniperus brevifolia, Juniperus cedrus, Juniperus osteosperma, Juniperus oxycedrus, Juniperus thurifera, Liriomyza bryoniae, Liriomyza chinensis, Liriomyza huidobrensis and Liriomyza trifolii.