| 1. |
- Engblom, Stefan, et al.
(författare)
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Bayesian epidemiological modeling over high-resolution network data
- 2020
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Ingår i: Epidemics. - : Elsevier BV. - 1755-4365 .- 1878-0067. ; 32
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Tidskriftsartikel (refereegranskat)abstract
- Mathematical epidemiological models have a broad use, including both qualitative and quantitative applications. With the increasing availability of data, large-scale quantitative disease spread models can nowadays be formulated. Such models have a great potential, e.g., in risk assessments in public health. Their main challenge is model parameterization given surveillance data, a problem which often limits their practical usage. We offer a solution to this problem by developing a Bayesian methodology suitable to epidemiological models driven by network data. The greatest difficulty in obtaining a concentrated parameter posterior is the quality of surveillance data; disease measurements are often scarce and carry little information about the parameters. The often overlooked problem of the model's identifiability therefore needs to be addressed, and we do so using a hierarchy of increasingly realistic known truth experiments. Our proposed Bayesian approach performs convincingly across all our synthetic tests. From pathogen measurements of shiga toxin-producing Escherichia coli O157 in Swedish cattle, we are able to produce an accurate statistical model of first-principles confronted with data. Within this model we explore the potential of a Bayesian public health framework by assessing the efficiency of disease detection and -intervention scenarios.
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| 2. |
- Bronstein, Samuel, et al.
(författare)
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Bayesian inference in epidemics : linear noise analysis
- 2023
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Ingår i: Mathematical Biosciences and Engineering. - : American Institute of Mathematical Sciences. - 1547-1063 .- 1551-0018. ; 20:2, s. 4128-4152
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Tidskriftsartikel (refereegranskat)abstract
- This paper offers a qualitative insight into the convergence of bayesian parameter inference in a setup which mimics the modeling of the spread of a disease with associated disease measurements. Specifically, we are interested in the Bayesian model’s convergence with increasing amounts of data under measurement limitations. Depending on how weakly informative the disease measurements are, we offer a kind of ‘best case’ as well as a ‘worst case’ analysis where, in the former case, we assume that the prevalence is directly accessible, while in the latter that only a binary signal corresponding toa prevalence detection threshold is available. Both cases are studied under an assumed so-called linear noise approximation as to the true dynamics. Numerical experiments test the sharpness of our results when confronted with more realistic situations for which analytical results are unavailable.
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| 3. |
- Bull, Jonathan R., et al.
(författare)
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Distributed and Adaptive Fast Multipole Method in Three Dimensions
- 2021
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Ingår i: Communications in Computational Physics. - : Global Science Press. - 1815-2406 .- 1991-7120. ; 30:4, s. 959-984
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Tidskriftsartikel (refereegranskat)abstract
- We develop a general distributed implementation of an adaptive fast multipole method in three space dimensions. We rely on a balanced type of adaptive space discretization which supports a highly transparent and fully distributed implementation. A complexity analysis indicates favorable scaling properties and numerical experiments on up to 512 cores and 1 billion source points verify them. The parameters controlling the algorithm are subject to in-depth experiments and the performance response to the input parameters implies that the overall implementation is well-suited to automated tuning.
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| 4. |
- Börjesson, Stefan, 1979-, et al.
(författare)
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Detection of an IMI-2 carbapenemase-producing Enterobacter asburiae at a Swedish feed mill
- 2022
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Ingår i: Frontiers in Microbiology. - : Frontiers Media S.A.. - 1664-302X. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Occurrence of multidrug resistant Enterobacteriaceae in livestock is of concern as they can spread to humans. A potential introduction route for these bacteria to livestock could be animal feed. We therefore wanted to identify if Escherichia spp., Enterobacter spp., Klebsiella spp., or Raoutella spp. with transferable resistance to extended spectrum cephalosporins, carbapenems or colistin could be detected in the environment at feed mills in Sweden. A second aim was to compare detected isolates to previous described isolates from humans and animals in Sweden to establish relatedness which could indicate a potential transmission between sectors and feed mills as a source for antibiotic resistant bacteria. However, no isolates with transferable resistance to extended-cephalosporins or colistin could be identified, but one isolate belonging to the Enterobacter cloacae complex was shown to be carbapenem-resistant and showing carbapenemase-activity. Based on sequencing by both short-read Illumina and long-read Oxford Nanopore MinIon technologies it was shown that this isolate was an E. asburiae carrying a bla IMI-2 gene on a 216 Kbp plasmid, designated pSB89A/IMI-2, and contained the plasmid replicons IncFII, IncFIB, and a third replicon showing highest similarity to the IncFII(Yp). In addition, the plasmid contained genes for various functions such as plasmid segregation and stability, plasmid transfer and arsenical transport, but no additional antibiotic resistance genes. This isolate and the pSB89A/IMI-2 was compared to three human clinical isolates positive for bla IMI-2 available from the Swedish antibiotic monitoring program Swedres. It was shown that one of the human isolates carried a plasmid similar with regards to gene content to the pSB89A/IMI-2 except for the plasmid transfer system, but that the order of genes was different. The pSB89A/IMI-2 did however share the same transfer system as the bla IMI-2 carrying plasmids from the other two human isolates. The pSB89A/IMI-2 was also compared to previously published plasmids carrying bla IMI-2, but no identical plasmids could be identified. However, most shared part of the plasmid transfer system and DNA replication genes, and the bla IMI-2 gene was located next the transcription regulator imiR. The IS3-family insertion element downstream of imiR in the pSB89A was also related to the IS elements in other bla IMI-carrying plasmids.
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| 5. |
- Engblom, Stefan, et al.
(författare)
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Towards Confident Bayesian Parameter Estimation in Stochastic Chemical Kinetics
- 2021
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Ingår i: Numerical Mathematics and Advanced Applications ENUMATH 2019. - Cham : Springer Nature. - 9783030558734 - 9783030558741 ; , s. 373-380
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Konferensbidrag (refereegranskat)abstract
- We investigate the feasibility of Bayesian parameter inference for chemical reaction networks described in the low copy number regime. Here stochastic models are often favorable implying that the Bayesian approach becomes natural. Our discussion circles around a concrete oscillating system describing a circadian rhythm, and we ask if its parameters can be inferred from observational data. The main challenge is the lack of analytic likelihood and we circumvent this through the use of a synthetic likelihood based on summarizing statistics. We are particularly interested in the robustness and confidence of the inference procedure and therefore estimates a priori as well as a posteriori the information content available in the data. Our all-synthetic experiments are successful but also point out several challenges when it comes to real data sets.
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| 6. |
- Fall, Tove, et al.
(författare)
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CRUSH Covid - ett tvärvetenskapligt samarbetsprojekt
- 2023
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Annan publikation (populärvet., debatt m.m.)abstract
- CRUSH Covid är ett pågående tvärvetenskapligt projekt, i vilket forskare från Region Uppsala och Uppsala universitet under 2020–2022 samverkade intensivt för att ge en kontinuerlig lägesbild och tolkning av smittspridning, klusterutbrott, och vaccinationsgrad. CRUSH Covid publicerade realtidsdata och samlade också in data som nu används i forskningsprojekt om bland annat pandemisk beredskap.
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| 7. |
- Kennedy, Beatrice, 1982-, et al.
(författare)
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App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden
- 2022
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- The app-based COVID Symptom Study was launched in Sweden in April 2020 to contribute to real-time COVID-19 surveillance. We enrolled 143,531 study participants (≥18 years) who contributed 10.6 million daily symptom reports between April 29, 2020 and February 10, 2021. Here, we include data from 19,161 self-reported PCR tests to create a symptom-based model to estimate the individual probability of symptomatic COVID-19, with an AUC of 0.78 (95% CI 0.74-0.83) in an external dataset. These individual probabilities are employed to estimate daily regional COVID-19 prevalence, which are in turn used together with current hospital data to predict next week COVID-19 hospital admissions. We show that this hospital prediction model demonstrates a lower median absolute percentage error (MdAPE: 25.9%) across the five most populated regions in Sweden during the first pandemic wave than a model based on case notifications (MdAPE: 30.3%). During the second wave, the error rates are similar. When we apply the same model to an English dataset, not including local COVID-19 test data, we observe MdAPEs of 22.3% and 19.0% during the first and second pandemic waves, respectively, highlighting the transferability of the prediction model.
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| 8. |
- Kumlien, Christine, et al.
(författare)
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Research priorities to prevent and treat diabetic foot ulcers-A digital James Lind Alliance Priority Setting Partnership
- 2022
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Ingår i: Diabetic Medicine. - : John Wiley & Sons. - 0742-3071 .- 1464-5491. ; 39:11
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Tidskriftsartikel (refereegranskat)abstract
- Aim To establish outcomes of a priority setting partnership between participants with diabetes mellitus and clinicians to identify the top 10 research priorities for preventing and treating diabetic foot ulcers (DFUs). Methods Due to the COVID-19 pandemic, the James Lind Alliance Priority Setting Partnership process was adapted into a digital format which involved a pilot survey to identify understandable uncertainties with high relevance for participants tested by calculating the content validity index; a main survey answered by 53 participants living with diabetes and 49 clinicians; and a final digital workshop to process and prioritise the final top 10 research priorities. Results The content validity index was satisfactory for 20 out of 25 uncertainties followed by minor changes and one additional uncertainty. After we processed the 26 uncertainties from the main survey and seven current guidelines, a list of 28 research uncertainties remained for review and discussion in the digital workshop. The final top 10 research priorities included the organisation of diabetes care; screening of diabetes, impaired blood circulation, neuropathy, and skin properties; vascular surgical treatment; importance of self-care; help from significant others; pressure relief; and prevention of infection. Conclusion The top 10 research priorities for preventing and treating DFUs represent consensus areas from persons living with diabetes and clinicians to guide future research. These research priorities can justify and inform strategic allocation of research funding. The digitalisation of James Lind Alliance methodology was feasible.
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| 9. |
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| 10. |
- Liu, Jing
(författare)
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Towards Fast and Robust Algorithms in Flash X-ray single-particle Imaging
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Modern X-ray Free Electron Laser (XFEL) technology provides the possibility to acquire a large number of diffraction patterns from individual biological nano-particles, including proteins, viruses, and DNA. Ideally, the collected data frames are high-quality single-particle diffraction patterns. However, unfortunately, the raw dataset is noisy and also contains patterns with scatterings from multiple particles, contaminated particles, etc. The data complexity and the massive volumes of raw data make pattern selection a time-consuming and challenge task. Further, X-rays interact with particles at random and the captured patterns are the 2D intensities of the scattered waves, i.e. we cannot observe the particle orientations and the phase information from the 2D diffraction patterns. To reconstruct 2D diffraction patterns into 3D structures of the desired particle, we need a sufficiently large single-particle-pattern dataset. The computational methodology for this reconstruction task is still under development and in need of an improved understanding of the algorithmic uncertainties.In this thesis, we tackle some of the challenges to obtain 3D structures of sample molecules from single-particle diffraction patterns. First, we have developed two classification methods to select single-particle diffraction patterns that are similar to provided templates. Second, we have accelerated the 3D reconstruction procedures by distributing the computations among Graphics Processing Units (GPUs) and by proposing an adaptive discretization of 3D space. Third, to better understand the uncertainties of the 3D reconstruction procedure, we have evaluated the impact of the different sources of resolution-limiting factors and introduced a practically applicable computational methodology in the form of bootstrap procedures for assessing the reconstruction uncertainty. These technologies form a data-analysis pipeline for recovering 3D structures from the raw X-ray single-particle data, which also analyzes the uncertainties. With the experimental developments of the X-ray single-particle technology, we expect that the data volumes will be increasing sharply, and hence, we believe such a computational pipeline will be critical to retrieve particle structures in the achievable resolution.
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