Advice from a systems-biology model of the corona epidemics

Abstract:

Using standard systems biology methodologies a 14-compartment dynamic model was developed for the Corona virus epidemic. The model predicts that: (i) it will be impossible to limit lockdown intensity such that sufficient herd immunity develops for this epidemic to die down, (ii) the death toll from the SARS-CoV-2 virus decreases very strongly with increasing intensity of the lockdown, but (iii) the duration of the epidemic increases at first with that intensity and then decreases again, such that (iv) it may be best to begin with selecting a lockdown intensity beyond the intensity that leads to the maximum duration, (v) an intermittent lockdown strategy should also work and might be more acceptable socially and economically, (vi) an initially intensive but adaptive lockdown strategy should be most efficient, both in terms of its low number of casualties and shorter duration, (vii) such an adaptive lockdown strategy offers the advantage of being robust to unexpected imports of the virus, e.g. due to international travel, (viii) the eradication strategy may still be superior as it leads to even fewer deaths and a shorter period of economic downturn, but should have the adaptive strategy as backup in case of unexpected infection imports, (ix) earlier detection of infections is the most effective way in which the epidemic can be controlled, whilst waiting for vaccines.

SEEK ID: https://fairdomhub.org/publications/576

DOI: 10.1038/s41540-020-0138-8

Projects: Mechanism based modeling viral disease ( COVID-19 ) dynamics in human...

Publication type: Journal

Journal: npj Systems Biology and Applications

Citation: npj Syst Biol Appl 6(1),18

Date Published: 1st Dec 2020

Registered Mode: by DOI

help Submitter
Citation
Westerhoff, H. V., & Kolodkin, A. N. (2020). Advice from a systems-biology model of the corona epidemics. In npj Systems Biology and Applications (Vol. 6, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41540-020-0138-8
Activity

Views: 807

Created: 18th Aug 2020 at 18:31

Last updated: 18th Aug 2020 at 18:33

help Tags

This item has not yet been tagged.

help Attributions

None

Powered by
(v.1.12.0)
Copyright © 2008 - 2022 The University of Manchester and HITS gGmbH

By continuing to use this site you agree to the use of cookies