Name: Westerhoff2020 - systems biology model of the coronavirus pandemic 2020
Keywords: COVID-19

Dataset Information

Westerhoff2020 - systems biology model of the coronavirus pandemic 2020

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.

DISEASE(S): Covid-19

SUBMITTER: Paul Jonas Jost

PROVIDER: BIOMD0000000988 | BioModels | 2024-09-02

REPOSITORIES: BioModels

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Dataset's files

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			Action	DRS
	BIOMD0000000988?filename=Westerhoff2020.cps	Other
	BIOMD0000000988?filename=Westerhoff2020.omex	Other
	BIOMD0000000988?filename=Westerhoff2020.sedml	Other
	BIOMD0000000988?filename=Westerhoff2020.xml	Xml
	BIOMD0000000988?filename=Westerhoff2020___Corona_model_of_extinguishable_epidemic_with_limited_time_incomplete_lockdown.vcml	Other

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Publications

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

Westerhoff Hans V HV Kolodkin Alexey N AN

NPJ systems biology and applications 20200612 1

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 tha ...[more]

PMID: 32532983

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Project description:Baris Hancioglu, David Swigon & Gilles Clermont. A dynamical model of human immune response to influenza A virus infection. Journal of Theoretical Biology 246, 1 (2007). We present a simplified dynamical model of immune response to uncomplicated influenza A virus (IAV) infection, which focuses on the control of the infection by the innate and adaptive immunity. Innate immunity is represented by interferon-induced resistance to infection of respiratory epithelial cells and by removal of infected cells by effector cells (cytotoxic T-cells and natural killer cells). Adaptive immunity is represented by virus-specific antibodies. Similar in spirit to the recent model of Bocharov and Romanyukha [1994. Mathematical model of antiviral immune response. III. Influenza A virus infection. J. Theor. Biol. 167, 323-360], the model is constructed as a system of 10 ordinary differential equations with 27 parameters characterizing the rates of various processes contributing to the course of disease. The parameters are derived from published experimental data or estimated so as to reproduce available data about the time course of IAV infection in a naïve host. We explore the effect of initial viral load on the severity and duration of the disease, construct a phase diagram that sheds insight into the dynamics of the disease, and perform sensitivity analysis on the model parameters to explore which ones influence the most the onset, duration and severity of infection. To account for the variability and speed of adaptation of the adaptive response to a particular virus strain, we introduce a variable that quantifies the antigenic compatibility between the virus and the antibodies currently produced by the organism. We find that for small initial viral load the disease progresses through an asymptomatic course, for intermediate value it takes a typical course with constant duration and severity of infection but variable onset, and for large initial viral load the disease becomes severe. This behavior is robust to a wide range of parameter values. The absence of antibody response leads to recurrence of disease and appearance of a chronic state with nontrivial constant viral load.