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- Sumaila, U. Rashid, et al.
(author)
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WTO must ban harmful fisheries subsidies
- 2021
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In: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 374:6567, s. 544-544
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Journal article (other academic/artistic)
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| 3. |
- Beck, Anna Severine, et al.
(author)
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Will there be a next Nordic TAG? Reflections on theoretical archaeology in the Nordic countries today
- 2019
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In: Arkæologisk Forum. - 1399-5545. ; :41, s. 17-19
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Journal article (other academic/artistic)abstract
- The first meeting in Nordic Theoretical Archaeology Group (Nordic TAG) was held in 1985. The – so far – last meeting in Nordic TAG was held in Copenhagen in 2015. At this meeting, the theme was “the Next 30 years in Theoretical Archaeology” – or in other words the aim wasto discussin which direction theories in the archaeological discipline will develop and especially what new theories, methodologies and perspectives might influence the field in the future. Tragicomically – or prophetically – no meetings have been organized since then. Now almost five years later we must ask: what is the future of Nordic TAG, and what does the lack of it tell us about the development of theoretical archaeology in the Nordic countries today?
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- Dehasque, Marianne, et al.
(author)
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Inference of natural selection from ancient DNA
- 2020
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In: Evolution Letters. - : JOHN WILEY & SONS LTD. - 2056-3744. ; 4:2, s. 94-108
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Journal article (other academic/artistic)abstract
- Evolutionary processes, including selection, can be indirectly inferred based on patterns of genomic variation among contemporary populations or species. However, this often requires unrealistic assumptions of ancestral demography and selective regimes. Sequencing ancient DNA from temporally spaced samples can inform about past selection processes, as time series data allow direct quantification of population parameters collected before, during, and after genetic changes driven by selection. In this Comment and Opinion, we advocate for the inclusion of temporal sampling and the generation of paleogenomic datasets in evolutionary biology, and highlight some of the recent advances that have yet to be broadly applied by evolutionary biologists. In doing so, we consider the expected signatures of balancing, purifying, and positive selection in time series data, and detail how this can advance our understanding of the chronology and tempo of genomic change driven by selection. However, we also recognize the limitations of such data, which can suffer from postmortem damage, fragmentation, low coverage, and typically low sample size. We therefore highlight the many assumptions and considerations associated with analyzing paleogenomic data and the assumptions associated with analytical methods.
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| 6. |
- Ssegonja, R., et al.
(author)
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Cost-effectiveness of an indicated preventive intervention for depression in adolescents
- 2020
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In: European Journal of Public Health. - : Oxford University Press. - 1101-1262 .- 1464-360X. ; 30:Suppl. 5, s. V914-V914
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Journal article (other academic/artistic)abstract
- Background: Adolescent depression has negative health and economic outcomes in the short- and long-term. Indicated preventive interventions, in particular group based cognitive behavioural therapy (GB-CBT), are effective in preventing depression in adolescents with subsyndromal depression. However, little is known about the cost-effectiveness of these interventions.Methods: A Markov cohort model was used to conduct cost-effectiveness analyses comparing a GB-CBT indicated preventive intervention for depression, to a no-intervention option. Taking a time horizon of 5- and 10 years, incremental differences in societal costs and health benefits expressed as cases of depression prevented, and as quality adjusted life years (QALYs) gained were estimated. Through univariate and probabilistic sensitivity analyses, the robustness of the results was explored. Costs, presented in 2018 USD, and effects were discounted at a yearly rate of 3%.Results: The base-case analysis showed that GB-CBT indicated preventive intervention incurred lower costs, prevented more cases of depression and generated higher QALYs compared to the no-intervention option for both time horizons. Offering the intervention was even a cost saving strategy and demonstrated a probability of being cost-effective of over 95%. In the sensitivity analyses, these results were robust to the modelling assumptions.Limitations: The study considered a homogeneous cohort and assumed a constant annual decay rate of the relative treatment effect.Conclusions: GB-CBT indicated preventive interventions for depression in adolescence can generate good value for money compared to leaving adolescents with subsyndromal depression untreated.Key messages:Indicated preventive interventions for depression are cost-saving and can generate substantial health benefits.Indicated preventive interventions can be adopted as cost-effective preventive strategies for depression.
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| 9. |
- Wigren, Anna, 1991-
(author)
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Sequential Monte Carlo methods for conjugate state-space models
- 2022
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Doctoral thesis (other academic/artistic)abstract
- Bayesian inference in state-space models requires the solution of high-dimensional integrals, which is intractable in general. A viable alternative is to use sample-based methods, like sequential Monte Carlo, but this introduces variance into the inferred quantities that can sometimes render the estimates useless. This thesis explores how conjugacy relations that allow for replacing numerical integration with analytic updates can be used to reduce the variance in sequential Monte Carlo based methods for both state and parameter inference.In the context of state inference, a new type of proposal distribution for sequential Monte Carlo tailored for moderately high-dimensional systems with intractable transition densities is suggested. It combines the standard and the locally optimal proposal by adding conjugate artificial process noise to the model. The resulting bias-variance trade-off allows for a reduced Monte Carlo variance in exchange for model bias.In Bayesian inference using particle Gibbs samplers, conjugacy relations between parameter and state updates can be exploited to reduce the inherent correlation between consecutive samples by eliminating parameters from the state update. Despite the resulting non-Markovian model dependencies that arise from the marginalization, the computational complexity of the marginalized particle Gibbs samplers is shown to scale linearly with the number of observations.Furthermore, the marginalized framework is extended to the case of multiple state-space models with shared parameters to reduce Monte Carlo variance while simultaneously aggregating information from all datasets. For these models, multiple update structures are possible for the marginalized particle Gibbs sampler. Two distinct structures with complementary attributes are described and strategies for combining them to form more efficient samplers are discussed. The improved performance is illustrated for a compartmental model describing multiple outbreaks of mosquito-borne diseases that share either disease- or location-dependent parameters. The thesis also contributes to making sequential Monte Carlo methods available to a wider range of users through a tutorial-style paper aimed for the control community and an implementation of marginalized particle Gibbs samplers in a probabilistic programming language that automatically exploits conjugacy relations in the models.
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