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westerhoff1

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uninfectednontested > noncoronadead

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symptomsnontested > Coronadeadnontested

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Parameters

Global parameters

Fixed species

Initial values

Functions and Rules

Assignment rules

intermittenttime = time - lockdownduration * nlockdowns - nlockdowns * lockdownpauseduration

TotalInfectioncoefficient = (1.0 - epidemicextinguished) * (Infectionfromnontesednosymptoms * infectednontested + Infectionfromtestednosymtoms * infectedtested + Infectionfromtestedsymptoms * symptomstested + Infectionfromnontestedsymptoms * symptomsnontested) / SocialDistance

Governmentinducedisolation = adjF * (1.0 + 2.5 * symptomaticnumbertolerance / symptomaticnumbertolerance * symptomstested / symptomaticnumbertolerance * governmentresponsefactortodiagnosedinfected)

totalpopulation = recoveredtested + recoverednontested + infectednontested + infectedtested + uninfectednontested + uninfectedtested + symptomstested + symptomsnontested

coronadead = 100.0 * (Coronadeadtested + Coronadeadnontested) / totalpopulation

coronadeadalldead = 100.0 * coronadead / (coronadead + noncoronadead)

noncoronadeadperc = 100.0 * noncoronadead / totalpopulation

coronarecovered = 100.0 * (recoveredtested + recoverednontested) / totalpopulation

Coronaregistered = Coronadeadtested + recoveredtested + infectedtested + symptomstested

Function definitions

populationgrowth(S, T, k) = k * S * T

Events

roundoffinfectednott

Trigger: lt(infectednontested, 0)

Delay: 0

Assignments:

  • infectednontested = 0

roundoffsymptomsnont

Trigger: lt(symptomsnontested, 0)

Delay: 0

Assignments:

  • symptomsnontested = 0

triggergovernmentact

Trigger: gt(intermittenttime, Timegovermentaction)

Delay: 0

Assignments:

  • SocialDistance = ModelValue40 * Governmentinducedisolation

triggerrepetitiveinc

Trigger: gt(intermittenttime, Timegovermentaction)

Delay: 0

Assignments:

  • nlockdowns = 1 + nlockdowns

endofepidemic

Trigger: lt(infectednontested + infectedtested + symptomstested + symptomsnontested, numbercutoff)

Delay: 0

Assignments:

  • epidemicextinguished = 1

triggerrelaxationsoc

Trigger: gt(intermittenttime, Timegovermentaction - lockdownduration * (1 - timefractionlockdown))

Delay: 0

Assignments:

  • SocialDistance = ModelValue40

Constraints

Note that constraints are not enforced in simulations. It remains the responsibility of the user to verify that simulation results satisfy these constraints.


Species:

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Advice from a systems-biology model of the corona epidemics.

  • Hans V Westerhoff
  • Alexey N Kolodkin
NPJ systems biology and applications 2020; 6 (1): 18
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.

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