Institutt for datavitenskap og analyse

Journal of Infection and Public Health 19(9) s. 1-12 Doi: https://doi.org/10.1016/j.jiph.2026.103328

Background: Urban areas often act as early epicentres of pandemics, yet transmission dynamics across regions with different demographic profiles remains poorly understood. We aimed to characterise COVID-19 transmission in Norway and assess how it interacted with infection control measures across urban and rural settings. Methods: We analysed daily COVID-19 cases across three pandemic waves (Alpha, Delta, Omicron). For local trends, 36 sites in the Greater Oslo Region were grouped by infection trajectories using group-based trajectory modelling. For national trends, we examined the cities of Oslo, Bergen, Trondheim, Stavanger, Tromsø, and Akershus county. Infection trends were analysed using linear mixed models and lagged regression analyses and related to demographic characteristics. Results: In the Greater Oslo Region, three distinct site groups-high, moderate, and low infection-were identified. The high infection group had the highest population density, largest proportion of crowded housing and immigrant population and preceded the moderate and low groups by 5-13 days during the Alpha wave. Nationally, Oslo's infection rates peaked 11-24 days before other cities during the Alpha wave, with shorter lags during the Delta and Omicron waves. Despite similar control measures, infection rates differed substantially, with the high infection group and Oslo city consistently bearing the greatest burden. Conclusions: Densely populated areas with crowded housing act as early warning signals, with infection growth preceding that in other areas by days to weeks. Systematic monitoring of such high-burden urban areas could provide valuable lead time for policymakers and support more targeted, timely responses in future pandemics.

SIAM Journal on Financial Mathematics 17(3) s. 1078-1139 Doi: https://doi.org/10.1137/24m171334x

We provide a general Heath–Jarrow–Morton (HJM) framework for forward contracts written on abstract market indices with arbitrary fixing and payment adjustments and featuring collateralization in any currency denomination. In view of this, we first provide a thorough study of cross-currency markets in the presence of collateral and incompleteness. Then we give a general treatment of collateral dislocations by describing the instantaneous cross-currency basis spreads by means of HJM models, for which we derive appropriate drift conditions. The framework obtained allows us to simultaneously cover forward-looking risky IBOR rates, such as EURIBOR, and backward-looking rates based on overnight rates, such as SOFR. Due to the discrepancies in market conventions of different currency areas created by the benchmark transition, this is pivotal for describing portfolios of interest rate products that are denominated in multiple currencies. As an example of a contract simultaneously depending on all the risk factors that we describe within our framework, we treat cross-currency swaps using our proposed abstract indices.

Psychometrika s. 29-29 Doi: https://doi.org/10.1017/psy.2026.10139

Due to the indeterminacy problem in factor analysis, factors are not uniquely determined by observed data. We investigate the consequences of this problem for the interpretation of simulation studies and for linear structural equation models. For simulation procedures, factor indeterminacy implies that as long as the simulated data have the correct covariance matrix, there exists a possibly large space of factors and error terms that can generate the simulated data via the model equations. For linear structural equation models, the linearity of the structural part of the model is only partly testable by the distribution of the data. Practical recommendations for conducting simulation studies are provided. We give numerical illustrations, including a case where the observed data are normal and fit a linear structural equation model perfectly, yet are generated by a highly non-linear structural equation model.

Journal of applied econometrics Doi: https://doi.org/10.1002/jae.70074

Applied Mathematical Finance Doi: https://doi.org/10.1080/1350486X.2026.2715128