An appeal for strengthening genomic pathogen surveillance to improve pandemic preparedness and infection prevention: the German perspective.

The SARS-CoV-2 pandemic has highlighted the importance of viable infection surveillance and the relevant infrastructure. From a German perspective, an integral part of this infrastructure, genomic pathogen sequencing, was at best fragmentary and stretched to its limits due to the lack or inefficient use of equipment, human resources, data management and coordination. The experience in other countries has shown that the rate of sequenced positive samples and linkage of genomic and epidemiological data (person, place, time) represent important factors for a successful application of genomic pathogen surveillance. Planning, establishing and consistently supporting adequate structures for genomic pathogen surveillance will be crucial to identify and combat future pandemics as well as other challenges in infectious diseases such as multi-drug resistant bacteria and healthcare-associated infections. Therefore, the authors propose a multifaceted and coordinated process for the definition of procedural, legal and technical standards for comprehensive genomic pathogen surveillance in Germany, covering the areas of genomic sequencing, data collection and data linkage, as well as target pathogens. A comparative analysis of the structures established in Germany and in other countries is applied. This proposal aims to better tackle epi- and pandemics to come and take action from the "lessons learned" from the SARS-CoV-2 pandemic.

[Establishment of genomic pathogen surveillance to strengthen pandemic preparedness and infection prevention in Germany]. / Etablierung der Genomischen Erreger-Surveillance zur Stärkung des Pandemie- und Infektionsschutzes in Deutschland.

The SARS-CoV­2 pandemic has shown a deficit of essential epidemiological infrastructure, especially with regard to genomic pathogen surveillance in Germany. In order to prepare for future pandemics, the authors consider it urgently necessary to remedy this existing deficit by establishing an efficient infrastructure for genomic pathogen surveillance. Such a network can build on structures, processes, and interactions that have already been initiated regionally and further optimize them. It will be able to respond to current and future challenges with a high degree of adaptability.The aim of this paper is to address the urgency and to outline proposed measures for establishing an efficient, adaptable, and responsive genomic pathogen surveillance network, taking into account external framework conditions and internal standards. The proposed measures are based on global and country-specific best practices and strategy papers. Specific next steps to achieve an integrated genomic pathogen surveillance include linking epidemiological data with pathogen genomic data; sharing and coordinating existing resources; making surveillance data available to relevant decision-makers, the public health service, and the scientific community; and engaging all stakeholders. The establishment of a genomic pathogen surveillance network is essential for the continuous, stable, active surveillance of the infection situation in Germany, both during pandemic phases and beyond.

Navigating hospitals safely through the COVID-19 epidemic tide: Predicting case load for adjusting bed capacity.

BACKGROUND: The pressures exerted by the coronavirus disease 2019 (COVID-19) pandemic pose an unprecedented demand on healthcare services. Hospitals become rapidly overwhelmed when patients requiring life-saving support outpace available capacities. OBJECTIVE: We describe methods used by a university hospital to forecast case loads and time to peak incidence. METHODS: We developed a set of models to forecast incidence among the hospital catchment population and to describe the COVID-19 patient hospital-care pathway. The first forecast utilized data from antecedent allopatric epidemics and parameterized the care-pathway model according to expert opinion (ie, the static model). Once sufficient local data were available, trends for the time-dependent effective reproduction number were fitted, and the care pathway was reparameterized using hazards for real patient admission, referrals, and discharge (ie, the dynamic model). RESULTS: The static model, deployed before the epidemic, exaggerated the bed occupancy for general wards (116 forecasted vs 66 observed), ICUs (47 forecasted vs 34 observed), and predicted the peak too late: general ward forecast April 9 and observed April 8 and ICU forecast April 19 and observed April 8. After April 5, the dynamic model could be run daily, and its precision improved with increasing availability of empirical local data. CONCLUSIONS: The models provided data-based guidance for the preparation and allocation of critical resources of a university hospital well in advance of the epidemic surge, despite overestimating the service demand. Overestimates should resolve when the population contact pattern before and during restrictions can be taken into account, but for now they may provide an acceptable safety margin for preparing during times of uncertainty.