An important secondary purpose of the Smittestopp development was to provide aggregated data sets describing mobility and social interactions in Norway's population. The data were to be used to monitor the effect of government regulations and recommendations, provide input to advanced computational models to predict the pandemic's spread, and provide input to fundamental epidemiology research. In this chapter we describe the challenges and technical solutions of Smittestopp's data aggregation, as well as preliminary results from the time period when the app was active.We first give a detailed overview of the requirements, specifying the types of data to be collected and the level of spatial and temporal aggregation. We then proceed to describe the concepts for anonymization via :-anonymity and Y-differential privacy (Y-DP ), and the technical solutions for collecting and aggregating data from the database. In particular, we present details of how GPS- and Bluetooth events were mapped to geographical regions and points of interest, and the solutions employed for efficient data retrieval and processing. The preliminary results demonstrate how the recorded GPS- and Bluetooth events match with expected temporal and spatial variations in mobility and social interactions, and indicate the usefulness of the aggregated data as a tool for pandemic monitoring and research. One of the main criticisms of Smittestopp concerns the centralized storage of individuals' movements, even if such data were used and presented only at an aggregated and anonymized level. In this chapter, we also outline a completely different approach, where the GPS data do not leave the user's phone but are, instead, pre-processed to a much higher level of privacy before being dispatched to a server-side data aggregation algorithm. This approach, which would make the app significantly less intrusive, is made possible by recent advances in determining close contacts from Bluetooth data, either by a revised Smittestopp algorithm or by means of the Google/Apple Exposure Notification framework.

A central component in simulating cardiac electrophysiology is the numerical solution of nonlinear ordinary differential equations, also called cardiac ionic cell models, that describe cross-cell-membrane ion transport. Biophysically detailed cell models often require a considerable amount of computation, including calls to special mathematical functions. This paper systematically studies how to efficiently use modern multicore CPUs for this costly computational task. We start by investigating the code restructurings needed to effectively enable compiler- supported SIMD vectorisation, which is the most important performance booster in this context. It is found that suitable OpenMP directives are sufficient for achieving both vectorisation and parallelisation. We then continue with an evaluation of the performance optimisation technique of using lookup tables. Due to increased challenges for automated vectorisation, the obtainable benefits of lookup tables are dependent on the hardware platforms chosen. Throughout the study, we report detailed time measurements obtained on Intel Xeon, Xeon Phi, AMD Epyc and two ARM processors including Fujitsu A64FX, while attention is also paid to the impact of SIMD vectorisation and lookup tables on the computational accuracy. As a realistic example, the benefits of performance enhancement are demonstrated by a 10^9-run ensemble on the OakForest-PACS system, where code restructurings and SIMD vectorisation yield an 84% reduction in computing time, corresponding to 63,270 node hours.