Controlling the Covid-19 Pandemic without Killing the Economy: About Data Driven Decision Making with a Data Model Assessing Local Transmission Risk
Context In the face of further waves of the COVID-19 pandemic it becomes essential to find a balance between protective actions to guard public health and restrictive measures which can collapse our economy. Background As a basis for public health decisions, officials still rely on metrics which were helpful in the beginning of the pandemic but are now not precise enough for a focused and targeted approach to keep the spread of the infection under control. This can lead to public mistrust, “pandemic tiredness”, and can cause unnecessary damage to the economy without having the desired protective effect on public health. Methods This article discusses various metrics, their advantages and caveats, and it provides suggestions for use in a more targeted and risk-based approach, as an alternative to the current “general lock-down” practice. It suggests the notion of including a concept of “risk contacts per area” to better describe the possibility of virus transmission than currently published metrics do. The article also suggests specific analyses of real-world data for identification of populations at risk for severe courses of COVID-19 to allow more targeted protective actions. Discussion Data currently used to describe the COVID-19 pandemic lack important parameters like population density and local likelihood of potentially infectious contacts. The currently often used “all or nothing” approach of shut-down orders needs to be replaced by more sophisticated tactics considering individual local exposure risks and need to be balanced towards metrics on economic short term and long-term impact. In addition, smart analyses of real-world data may contribute to effective protection of individuals at risk.