Westerhoff2020 - systems biology model of the coronavirus pandemic 2020

  public model
Model Identifier
BIOMD0000000988
Short description
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.
Format
SBML (L2V4)
Related Publication
  • Advice from a systems-biology model of the corona epidemics.
  • Westerhoff HV, Alexey N. Kolodkin
  • NPJ systems biology and applications , 6/ 2020 , Volume 6 , Issue 1 , pages: 18 , PubMed ID: 32532983
  • Infrastructure for Systems Biology Europe - The Netherlands (ISBE.NL), Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.
  • 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.
Contributors
Kausthubh Ramachandran, Paul Jonas Jost

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Curated

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Model files

Westerhoff2020.xml SBML L2V4 file of a systems biology model of the coronavirus pandemic 2020 278.94 KB Preview | Download

Additional files

Westerhoff2020.omex COMBINE archive of a systems biology model of the coronavirus pandemic 2020 35.01 KB Preview | Download
Westerhoff2020.sedml SED-ML file of a systems biology model of the coronavirus pandemic 2020 1.81 KB Preview | Download
Westerhoff2020.cps COPASI 4.29 (Build 228) file of a systems biology model of the coronavirus pandemic 2020 305.31 KB Preview | Download
Westerhoff2020___Corona_model_of_extinguishable_epidemic_with_limited_time_incomplete_lockdown.vcml VCell file of a systems biology model of the coronavirus pandemic 2020 173.73 KB Preview | Download

  • Model originally submitted by : Paul Jonas Jost
  • Submitted: 12-Feb-2021 13:40:02
  • Last Modified: 16-Feb-2021 10:31:53
Revisions
  • Version: 5 public model Download this version
    • Submitted on: 16-Feb-2021 10:31:53
    • Submitted by: Kausthubh Ramachandran
    • With comment: Automatically added model identifier BIOMD0000000988
  • Version: 3 public model Download this version
    • Submitted on: 16-Feb-2021 10:26:54
    • Submitted by: Kausthubh Ramachandran
    • With comment: Automatically added model identifier BIOMD0000000988
  • Version: 1 public model Download this version
    • Submitted on: 12-Feb-2021 13:40:02
    • Submitted by: Paul Jonas Jost
    • With comment: updated model name and modelling approach

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Curator's comment:
(added: 16 Feb 2021, 10:26:20, updated: 16 Feb 2021, 10:26:20)
Reproduced figure 4 and image taken from running the model in VCell. The following curator's comments are for the COPASI files - This model was encoded on COPASI 4.29 (Build 228) and reproduces Figure 2 and 4. For Figure 4a set 'social_distancing_factor=10', 'governmentResponseFactorToDiagnosedInfected=0', 'lockdown_duration=7' and 'timeFraction_lockdown=0.55' and run for t=365. For Figure 4b set 'social_distancing_factor=10', 'governmentResponseFactorToDiagnosedInfected=0', 'lockdown_duration=7' and 'timeFraction_lockdown=0.7' and run for t=365. For Figure 2 set 'governmentResponseFactorToDiagnosedInfected=0', 'lockdown_duration=730' and 'timeFraction_lockdown=0.5'. Then run for t=365 with 'social_distancing_factor=1', 'social_distancing_factor=2.2', 'social_distancing_factor=10' and export the data for 'dead_corona_perc' and 'infected_tested'.