Cohort profile: Actionable Register of Geneva Outpatients and inpatients with SARS-CoV-2 (ARGOS).

PURPOSE: The Actionable Register of Geneva Outpatients and inpatients with SARS-CoV-2 (ARGOS) is an ongoing prospective cohort created by the Geneva Directorate of Health. It consists of an operational database compiling all SARS-CoV-2 test results recorded in the Geneva area since late February 2020. This article aims at presenting this comprehensive cohort, in light of some of the varying public health measures in Geneva, Switzerland, since March 2020. PARTICIPANTS: As of 1 June 2021, the database included 360 525 patients, among which 65 475 had at least one positive test result for SARS-CoV-2. Among all positive patients, 37.6% were contacted only once, 10.6% had one follow-up call, 8.5% had two and 27.7% had three or more follow-up calls. Participation rate among positive patients is 94%. Data collection is ongoing. FINDINGS TO DATE: ARGOS data illustrates the magnitude of COVID-19 pandemic in Geneva, Switzerland, and details a variety of population factors and outcomes. The content of the cohort includes demographic data, comorbidities and risk factors for poor clinical outcome, self-reported COVID-19 symptoms, environmental and socioeconomic factors, prospective and retrospective contact tracing data, travel quarantine data and deaths. The registry has already been used in several publications focusing on symptoms and long COVID-19, infection fatality rate and re-infection. FUTURE PLANS: The data of this large real-world registry provides a valuable resource for various types of research, such as clinical research, epidemiological research or policy assessment as it illustrates the impact of public health policies and overall disease burden of COVID-19.

Assessing the role of initial conditions in the local structural identifiability of large dynamic models.

Structural identifiability is a binary property that determines whether or not unique parameter values can, in principle, be estimated from error-free input-output data. The many papers that have been written on this topic collectively stress the importance of this a priori analysis in the model development process. The story however, often ends with a structurally unidentifiable model. This may leave a model developer with no plan of action on how to address this potential issue. We continue this model exploration journey by identifying one of the possible sources of a model's unidentifiability: problematic initial conditions. It is well-known that certain initial values may result in the loss of local structural identifiability. Nevertheless, literature on this topic has been limited to the analysis of small toy models. Here, we present a systematic approach to detect problematic initial conditions of real-world systems biology models, that are usually not small. A model's identifiability can often be reinstated by changing the value of such problematic initial conditions. This provides modellers an option to resolve the "unidentifiable model" problem. Additionally, a good understanding of which initial values should rather be avoided can be very useful during experimental design. We show how our approach works in practice by applying it to five models. First, two small benchmark models are studied to get the reader acquainted with the method. The first one shows the effect of a zero-valued problematic initial condition. The second one illustrates that the approach also yields correct results in the presence of input signals and that problematic initial conditions need not be zero-values. For the remaining three examples, we set out to identify key initial values which may result in the structural unidentifiability. The third and fourth examples involve a systems biology Epo receptor model and a JAK/STAT model, respectively. In the final Pharmacokinetics model, of which its global structural identifiability has only recently been confirmed, we indicate that there are still sets of initial values for which this property does not hold.