COVID-19 Intervention Scenarios for a Long-term Disease Management

Document Type : Original Article

Authors

Department of Geoinformatics – Z_GIS, University of Salzburg, Salzburg, Austria

Abstract

Background
The first outbreak of coronavirus disease 2019 (COVID-19) was successfully restrained in many countries around the world by means of a severe lockdown. Now, we are entering the second phase of the pandemics in which the spread of the virus needs to be contained within the limits that national health systems can cope with. This second phase of the epidemics is expected to last until a vaccination is available or herd immunity is reached. Long-term management strategies thus need to be developed.
 
Methods
In this paper we present a new agent-based simulation model “COVID-19 ABM” with which we simulate 4 alternative scenarios for the second “new normality” phase that can help decision-makers to take adequate control and intervention measures.
 
Results
The scenarios resulted in distinctly different outcomes. A continued lockdown could regionally eradicate the virus within a few months, whereas a relaxation back to 80% of former activity-levels was followed by a second outbreak. Contact-tracing as well as adaptive response strategies could keep COVID-19 within limits.
 
Conclusion
The main insights are that low-level voluntary use of tracing apps shows no relevant effects on containing the virus, whereas medium or high-level tracing allows maintaining a considerably higher level of social activity. Adaptive control strategies help in finding the level of least restrictions. A regional approach to adaptive management can further help in fine-tuning the response to regional dynamics and thus minimise negative economic effects.

Keywords

Main Subjects

References

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International Journal of Health Policy and Management
Volume 9, Issue 12
December 2020
Pages 508-516

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History

  • Received Date: 19 April 2020
  • Revised Date: 11 July 2020
  • Accepted Date: 11 July 2020
  • First Published Date: 01 December 2020

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