Spatial heterogeneity affects predictions from early-curve fitting of pandemic outbreaks: a case study using population data from Denmark.

The modelling of pandemics has become a critical aspect in modern society. Even though artificial intelligence can help the forecast, the implementation of ordinary differential equations which estimate the time development in the number of susceptible, (exposed), infected and recovered (SIR/SEIR) individuals is still important in order to understand the stage of the pandemic. These models are based on simplified assumptions which constitute approximations, but to what extent this are erroneous is not understood since many factors can affect the development. In this paper, we introduce an agent-based model including spatial clustering and heterogeneities in connectivity and infection strength. Based on Danish population data, we estimate how this impacts the early prediction of a pandemic and compare this to the long-term development. Our results show that early phase SEIR model predictions overestimate the peak number of infected and the equilibrium level by at least a factor of two. These results are robust to variations of parameters influencing connection distances and independent of the distribution of infection rates.

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.

Prediction Models in Veterinary and Human Epidemiology: Our Experience With Modeling Sars-CoV-2 Spread.

The worldwide outbreak of Sars-CoV-2 resulted in modelers from diverse fields being called upon to help predict the spread of the disease, resulting in many new collaborations between different institutions. We here present our experience with bringing our skills as veterinary disease modelers to bear on the field of human epidemiology, building models as tools for decision makers, and bridging the gap between the medical and veterinary fields. We describe and compare the key steps taken in modeling the Sars-CoV-2 outbreak: criteria for model choices, model structure, contact structure between individuals, transmission parameters, data availability, model validation, and disease management. Finally, we address how to improve on the contingency infrastructure available for Sars-CoV-2.