Electronic reporting of diagnostic laboratory test results from all healthcare sectors is a cornerstone of national preparedness and control of COVID-19 in Denmark.

The COVID-19 pandemic has led to an unprecedented demand for real-time surveillance data in order to inform critical decision makers regarding the management of the pandemic. The aim of this review was to describe how the Danish national microbiology database, MiBa, served as a cornerstone for providing data to the real-time surveillance system by linkage to other nationwide health registries. The surveillance system was established on an existing IT health infrastructure and a close network between clinical microbiologists, information technology experts, and public health officials. In 2020, testing capacity for SARS-CoV-2 was ramped up from none to over 10,000 weekly PCR tests per 100,000 population. The crude incidence data mirrored this increase in testing. Real-time access to denominator data and patient registries enabled adjustments for fluctuations testing activity, providing robust data on crude SARS-CoV-2 incidence during the changing diagnostic and management strategies. The use of the same data for different purposes, for example, final laboratory reports, information to the public, contact tracing, public health, and science, has been a critical asset for the pandemic response. It has also raised issues concerning data protection and critical capacity of the underlying technical systems and key resources. However, even with these limitations, the setup has enabled decision makers to adopt timely interventions. The experiences from COVID-19 may motivate a transformation from traditional indicator-based public health surveillance to an all-encompassing information system based on access to a comprehensive set of data sources, including diagnostic and reference microbiology.

A Diffusion Approximation Based on Renewal Processes with Applications to Strongly Biased Run-Tumble Motion.

We consider organisms which use a renewal strategy such as run-tumble when moving in space, for example to perform chemotaxis in chemical gradients. We derive a diffusion approximation for the motion, applying a central limit theorem due to Anscombe for renewal-reward processes; this theorem has not previously been applied in this context. Our results extend previous work, which has established the mean drift but not the diffusivity. For a classical model of tumble rates applied to chemotaxis, we find that the resulting chemotactic drift saturates to the swimming velocity of the organism when the chemical gradients grow increasingly steep. The dispersal becomes anisotropic in steep gradients, with larger dispersal across the gradient than along the gradient. In contrast to one-dimensional settings, strong bias increases dispersal. We next include Brownian rotation in the model and find that, in limit of high chemotactic sensitivity, the chemotactic drift is 64% of the swimming velocity, independent of the magnitude of the Brownian rotation. We finally derive characteristic timescales of the motion that can be used to assess whether the diffusion limit is justified in a given situation. The proposed technique for obtaining diffusion approximations is conceptually and computationally simple, and applicable also when statistics of the motion is obtained empirically or through Monte Carlo simulation of the motion.