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In this paper we present the first systematic analysis of the impact of the populated vs. intrinsic spin distribution on the nuclear level density and γ-ray strength function retrieved through the Oslo Method. We illustrate the effect of the spin distribution on the recently performed239Pu(d,pγ)240Pu experiment using a 12 MeV deuteron beam performed at the Oslo Cyclotron Lab. In the analysis we couple state-of-the-art calculations for the populated spin-distributions with the Monte-Carlo nuclear decay code RAINIER to compare Oslo Method results to the known input. We find that good knowledge of the populated spin distribution is crucial and show that the populated distribution has a significant impact on the extracted nuclear level density and γ-ray strength function for the239Pu(d,pγ)240Pu case.

The Oslo method has been applied to particle-γ coincidences following the Pu239(d,p) reaction to obtain the nuclear level density (NLD) and γ-ray strength function (γSF) of Pu240. The experiment was conducted with a 12 MeV deuteron beam at the Oslo Cyclotron Laboratory. The low spin transfer of this reaction leads to a spin-parity mismatch between populated and intrinsic levels. This is a challenge for the Oslo method as it can have a significant impact on the extracted NLD and γSF. We have developed an iterative approach to ensure consistent results even for cases with a large spin-parity mismatch, in which we couple Green's function transfer calculations of the spin-parity dependent population cross-section to the nuclear decay code rainier. The resulting γSF shows a pronounced enhancement between 2-4 MeV that is consistent with the location of the low-energy orbital M1 scissors mode.

The nuclear level densities and γ-ray strength functions of La138,139,140 were measured using the La139(He3,α), La139(He3,He3′), and La139(d,p) reactions. The particle-γ coincidences were recorded with the silicon particle telescope (SiRi) and NaI(Tl) (CACTUS) arrays. In the context of these experimental results, the low-energy enhancement in the A∼140 region is discussed. The La137,138,139(n,γ) cross sections were calculated at s- and p-process temperatures using the experimentally measured nuclear level densities and γ-ray strength functions. Good agreement is found between La139(n,γ) calculated cross sections and previous measurements.

The nuclear level densities and γ-ray strength functions of La138,139,140 were measured using the La139(He3,α), La139(He3,He3′), and La139(d,p) reactions. The particle-γ coincidences were recorded with the silicon particle telescope (SiRi) and NaI(Tl) (CACTUS) arrays. In the context of these experimental results, the low-energy enhancement in the A∼140 region is discussed. The La137,138,139(n,γ) cross sections were calculated at s- and p-process temperatures using the experimentally measured nuclear level densities and γ-ray strength functions. Good agreement is found between La139(n,γ) calculated cross sections and previous measurements.

Massive stars are a major source of chemical elements in the cosmos, ejecting freshly produced nuclei through winds and core-collapse supernova explosions into the interstellar medium. Among the material ejected, long-lived radioisotopes, such as 60 Fe (iron) and 26 Al (aluminum), offer unique signs of active nucleosynthesis in our galaxy. There is a long-standing discrepancy between the observed 60 Fe/ 26 Al ratio by γ-ray telescopes and predictions from supernova models. This discrepancy has been attributed to uncertainties in the nuclear reaction networks producing 60 Fe, and one reaction in particular, the neutron-capture on 59 Fe. Here we present experimental results that provide a strong constraint on this reaction. We use these results to show that the production of 60 Fe in massive stars is higher than previously thought, further increasing the discrepancy between observed and predicted 60 Fe/ 26 Al ratios. The persisting discrepancy can therefore not be attributed to nuclear uncertainties, and points to issues in massive-star models., (© 2024. The Author(s).)

Background: The global incidence target for the elimination of hepatitis C among people who inject drugs (PWID) is <2/100. In Norway, the hepatitis C epidemic is concentrated in PWID. Immigrants are the second most important risk group for chronic infection. We modelled the incidence of hepatitis C among active PWID, and the prevalence of chronic infection among active PWID, ex-PWID, and immigrants in Norway to 2022., Methods: We built a stochastic compartmental model, which was informed using data from national data sources, literature, and expert opinion. We report median values with 95% credible intervals (CrI)., Results: The model estimated 30 (95% Crl, 13-52) new infections among active PWID in 2022, or 0.37/100 (95% Crl, 0.17-0.65), down from a peak of 726 (95% Crl, 506-1067) in 2000. Across all groups, the model estimated 3202 (95% Crl, 1273-6601) chronically infected persons in 2022. Results were robust in sensitivity analyses., Conclusions: Norway provides an example of the feasibility of hepatitis C elimination in a setting with a concentrated epidemic, high coverage of harm reduction services, and no treatment restrictions. Continued momentum is needed to further reduce the transmission and burden of hepatitis C in Norway., Competing Interests: Potential conflicts of interest. All authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Restrictions of cross-border mobility are typically used to prevent an emerging disease from entering a country in order to slow down its spread. However, such interventions can come with a significant societal cost and should thus be based on careful analysis and quantitative understanding on their effects. To this end, we model the influence of cross-border mobility on the spread of COVID-19 during 2020 in the neighbouring Nordic countries of Denmark, Finland, Norway and Sweden. We investigate the immediate impact of cross-border travel on disease spread and employ counterfactual scenarios to explore the cumulative effects of introducing additional infected individuals into a population during the ongoing epidemic. Our results indicate that the effect of inter-country mobility on epidemic growth is non-negligible essentially when there is sizeable mobility from a high prevalence country or countries to a low prevalence one. Our findings underscore the critical importance of accurate data and models on both epidemic progression and travel patterns in informing decisions related to inter-country mobility restrictions., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Shubin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Vaccination was a key intervention in controlling the COVID-19 pandemic globally. In early 2021, Norway faced significant regional variations in COVID-19 incidence and prevalence, with large differences in population density, necessitating efficient vaccine allocation to reduce infections and severe outcomes. This study explored alternative vaccination strategies to minimize health outcomes (infections, hospitalizations, ICU admissions, deaths) by varying regions prioritized, extra doses prioritized, and implementation start time. Using two models (individual-based and meta-population), we simulated COVID-19 transmission during the primary vaccination period in Norway, covering the first 7 months of 2021. We investigated alternative strategies to allocate more vaccine doses to regions with a higher force of infection. We also examined the robustness of our results and highlighted potential structural differences between the two models. Our findings suggest that early vaccine prioritization could reduce COVID-19 related health outcomes by 8% to 20% compared to a baseline strategy without geographic prioritization. For minimizing infections, hospitalizations, or ICU admissions, the best strategy was to initially allocate all available vaccine doses to fewer high-risk municipalities, comprising approximately one-fourth of the population. For minimizing deaths, a moderate level of geographic prioritization, with approximately one-third of the population receiving doubled doses, gave the best outcomes by balancing the trade-off between vaccinating younger people in high-risk areas and older people in low-risk areas. The actual strategy implemented in Norway was a two-step moderate level aimed at maintaining the balance and ensuring ethical considerations and public trust. However, it did not offer significant advantages over the baseline strategy without geographic prioritization. Earlier implementation of geographic prioritization could have more effectively addressed the main wave of infections, substantially reducing the national burden of the pandemic., Competing Interests: The authors declare that they have no conflict of interest., (Copyright: © 2024 Chan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

BackgroundGiven the societal, economic and health costs of COVID-19 non-pharmaceutical interventions (NPI), it is important to assess their effects. Human mobility serves as a surrogate measure for human contacts and compliance with NPI. In Nordic countries, NPI have mostly been advised and sometimes made mandatory. It is unclear if making NPI mandatory further reduced mobility.AimWe investigated the effect of non-compulsory and follow-up mandatory measures in major cities and rural regions on human mobility in Norway. We identified NPI categories that most affected mobility.MethodsWe used mobile phone mobility data from the largest Norwegian operator. We analysed non-compulsory and mandatory measures with before-after and synthetic difference-in-differences approaches. By regression, we investigated the impact of different NPI on mobility.ResultsNationally and in less populated regions, time travelled, but not distance, decreased after follow-up mandatory measures. In urban areas, however, distance decreased after follow-up mandates, and the reduction exceeded the decrease after initial non-compulsory measures. Stricter metre rules, gyms reopening, and restaurants and shops reopening were significantly associated with changes in mobility.ConclusionOverall, distance travelled from home decreased after non-compulsory measures, and in urban areas, distance further decreased after follow-up mandates. Time travelled reduced more after mandates than after non-compulsory measures for all regions and interventions. Stricter distancing and reopening of gyms, restaurants and shops were associated with changes in mobility.

The COVID-19 pandemic is challenging nations with devastating health and economic consequences. The spread of the disease has revealed major geographical heterogeneity because of regionally varying individual behaviour and mobility patterns, unequal meteorological conditions, diverse viral variants, and locally implemented non-pharmaceutical interventions and vaccination roll-out. To support national and regional authorities in surveilling and controlling the pandemic in real-time as it unfolds, we here develop a new regional mathematical and statistical model. The model, which has been in use in Norway during the first two years of the pandemic, is informed by real-time mobility estimates from mobile phone data and laboratory-confirmed case and hospitalisation incidence. To estimate regional and time-varying transmissibility, case detection probabilities, and missed imported cases, we developed a novel sequential Approximate Bayesian Computation method allowing inference in useful time, despite the high parametric dimension. We test our approach on Norway and find that three-week-ahead predictions are precise and well-calibrated, enabling policy-relevant situational awareness at a local scale. By comparing the reproduction numbers before and after lockdowns, we identify spatially heterogeneous patterns in their effect on the transmissibility, with a stronger effect in the most populated regions compared to the national reduction estimated to be 85% (95% CI 78%-89%). Our approach is the first regional changepoint stochastic metapopulation model capable of real time spatially refined surveillance and forecasting during emergencies., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Engebretsen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)