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- Downey, Harriet, et al.
(författare)
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Training future generations to deliver evidence-based conservation and ecosystem management
- 2021
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Ingår i: Ecological Solutions and Evidence. - : Wiley. - 2688-8319. ; 2:1
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Forskningsöversikt (refereegranskat)abstract
- 1. To be effective, the next generation of conservation practitioners and managers need to be critical thinkers with a deep understanding of how to make evidence-based decisions and of the value of evidence synthesis.2. If, as educators, we do not make these priorities a core part of what we teach, we are failing to prepare our students to make an effective contribution to conservation practice.3. To help overcome this problem we have created open access online teaching materials in multiple languages that are stored in Applied Ecology Resources. So far, 117 educators from 23 countries have acknowledged the importance of this and are already teaching or about to teach skills in appraising or using evidence in conservation decision-making. This includes 145 undergraduate, postgraduate or professional development courses.4. We call for wider teaching of the tools and skills that facilitate evidence-based conservation and also suggest that providing online teaching materials in multiple languages could be beneficial for improving global understanding of other subject areas.
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- Thompson, Robin N., et al.
(författare)
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Key questions for modelling COVID-19 exit strategies
- 2020
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Ingår i: Proceedings of the Royal Society of London. Biological Sciences. - : The Royal Society. - 0962-8452 .- 1471-2954. ; 287:1932
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Tidskriftsartikel (refereegranskat)abstract
- Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.
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- Marin, Robin
(författare)
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Computational Modeling, Parameterization, and Evaluation of the Spread of Diseases
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Computer simulations play a vital role in the modeling of infectious diseases. Different modeling regimes fit specific purposes, from ordinary differential equations to probabilistic formulations. Throughout the COVID-19 pandemic, we have seen how the results from these computational models can come to dictate our daily lives and the importance of reliable results. This thesis aims to address the challenge of exploiting the increase in available computational power to build accurate models with well-understood uncertainties. The latter is essential when basing decisions on any model predictions.Data collection relevant to epidemiology is expanding, and methods to incorporate models in data fitting need to follow suit. This thesis applies the Bayesian framework connecting data with models in a probabilistic setting. We propose simulation-based inference methods that allow for the use of complex models otherwise excluded due to their intractable likelihoods. Our computational set-up exemplifies how modelers can deploy Bayesian inference in large-scale, real-world data environments.The thesis includes four papers relevant for modelers considering dynamic systems, approximate Bayesian inference, or epidemics. Paper I finds the approximate posterior of a complex chemical reaction network and estimates the prior and posterior uncertainties using the pathwise Fisher information matrix, thus framing our methodology in a fully synthetic setting. Paper II constructs a disease spread model for the spread of a verotoxigenic E. coli prevalent in the Swedish cattle population. The data includes a high-resolution transport network and actual bacterial-swab observations from selected farms. The results show that even if the data is sparse in space and time, it is still possible to recover a posterior that replicates the data and is viable for mitigation evaluations. Paper III studies a form of meta-models, the Ornstein-Uhlenbeck process, and how they approximate epidemiological models and enable broad analysis. We state an analytical limit of what is possible to learn from data subject to binary filters with confirming numerical examples. Finally, Paper IV finds a posterior model of the COVID-19 pandemic in Sweden and the 21 regions using a Kalman filter approximation. The findings result in a probabilistic regional surveillance tool for an epidemic at a national scale with considerable cost-cutting potential independent of large-scale testing of individuals.In conclusion, the thesis examines how reasonably realistic and computationally expensive epidemic models can be adapted to data using a Bayesian framework without compromising model complexity and estimating uncertainties that further support decision-making.
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