Paiva2020 - SEIAHRD model of transmission dynamics of COVID-19

  public model
Model Identifier
BIOMD0000000960
Short description
This paper proposes a dynamic model to describe and forecast the dynamics of the coronavirus disease COVID-19 transmission. The model is based on an approach previously used to describe the Middle East Respiratory Syndrome (MERS) epidemic. This methodology is used to describe the COVID-19 dynamics in six countries where the pandemic is widely spread, namely China, Italy, Spain, France, Germany, and the USA. For this purpose, data from the European Centre for Disease Prevention and Control (ECDC) are adopted. It is shown how the model can be used to forecast new infection cases and new deceased and how the uncertainties associated to this prediction can be quantified. This approach has the advantage of being relatively simple, grouping in few mathematical parameters the many conditions which affect the spreading of the disease. On the other hand, it requires previous data from the disease transmission in the country, being better suited for regions where the epidemic is not at a very early stage. With the estimated parameters at hand, one can use the model to predict the evolution of the disease, which in turn enables authorities to plan their actions. Moreover, one key advantage is the straightforward interpretation of these parameters and their influence over the evolution of the disease, which enables altering some of them, so that one can evaluate the effect of public policy, such as social distancing. The results presented for the selected countries confirm the accuracy to perform predictions.
Format
SBML (L2V4)
Related Publication
  • A data-driven model to describe and forecast the dynamics of COVID-19 transmission.
  • Paiva HM, Afonso RJM, de Oliveira IL, Garcia GF
  • PloS one , 1/ 2020 , Volume 15 , Issue 7 , pages: e0236386 , PubMed ID: 32735581
  • Institute of Science and Technology (ICT), Federal University of São Paulo (UNIFESP), São José dos Campos, SP, Brazil.
  • This paper proposes a dynamic model to describe and forecast the dynamics of the coronavirus disease COVID-19 transmission. The model is based on an approach previously used to describe the Middle East Respiratory Syndrome (MERS) epidemic. This methodology is used to describe the COVID-19 dynamics in six countries where the pandemic is widely spread, namely China, Italy, Spain, France, Germany, and the USA. For this purpose, data from the European Centre for Disease Prevention and Control (ECDC) are adopted. It is shown how the model can be used to forecast new infection cases and new deceased and how the uncertainties associated to this prediction can be quantified. This approach has the advantage of being relatively simple, grouping in few mathematical parameters the many conditions which affect the spreading of the disease. On the other hand, it requires previous data from the disease transmission in the country, being better suited for regions where the epidemic is not at a very early stage. With the estimated parameters at hand, one can use the model to predict the evolution of the disease, which in turn enables authorities to plan their actions. Moreover, one key advantage is the straightforward interpretation of these parameters and their influence over the evolution of the disease, which enables altering some of them, so that one can evaluate the effect of public policy, such as social distancing. The results presented for the selected countries confirm the accuracy to perform predictions.
Contributors
Submitter of the first revision: Kausthubh Ramachandran
Submitter of this revision: Kausthubh Ramachandran
Modellers: Kausthubh Ramachandran

Metadata information

is
BioModels Database BIOMD0000000960
BioModels Database MODEL2008200001
isDescribedBy
PubMed 32735581
hasTaxon
Taxonomy SARS-CoV-2
Taxonomy Homo sapiens
isVersionOf
Human Disease Ontology COVID-19
Infectious Disease Ontology 0000503
hasProperty
Mathematical Modelling Ontology population model
Curation status
Curated
Modelling approach(es)
population model
Tags
COVID-19

Connected external resources

SBGN view in Newt Editor

Name Description Size Actions

Model files
Paiva2020.xml SBML L2V4 representation of SEIAHRD model of transmission dynamics of COVID-19 202.25 KB Preview | Download

Additional files
Paiva2020.cps COPASI 4.29 (Build 228) file of SEIAHRD model of transmission dynamics of COVID-19 221.78 KB Preview | Download
Paiva2020.omex COMBINE archive of SEIAHRD model of transmission dynamics of COVID-19 25.53 KB Preview | Download
Paiva2020.sedml SED-ML file of SEIAHRD model of transmission dynamics of COVID-19 2.54 KB Preview | Download

  • Model originally submitted by : Kausthubh Ramachandran
  • Submitted: Aug 20, 2020 9:42:09 AM
  • Last Modified: Oct 5, 2020 11:19:16 PM
Revisions
  • Version: 9 public model Download this version
    • Submitted on: Oct 5, 2020 11:19:16 PM
    • Submitted by: Kausthubh Ramachandran
    • With comment: Automatically added model identifier BIOMD0000000960
  • Version: 6 public model Download this version
    • Submitted on: Aug 26, 2020 4:12:37 PM
    • Submitted by: Kausthubh Ramachandran
    • With comment: Automatically added model identifier BIOMD0000000960
  • Version: 3 public model Download this version
    • Submitted on: Aug 20, 2020 9:42:09 AM
    • Submitted by: Kausthubh Ramachandran
    • With comment: Automatically added model identifier BIOMD0000000960

(*) You might be seeing discontinuous revisions as only public revisions are displayed here. Any private revisions unpublished model revision of this model will only be shown to the submitter and their collaborators.

Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
Deceased

Dead ; C171133 		0.0 item
Cumulative Cases

C171133 ; event death 		0.0 item
Susceptible

C171133 ; 0000514 		9900000.0 item
Exposed

C171133 ; 0000514 ; 0000597 		36600.0 item
Hospitalized

0000511 ; C168447 ; C171133 ; C25179 		0.0 item
Recovered

0000621 ; C171133 		0.0 item
Reactions
Reactions Rate Parameters
Infectious => Deceased Country*delta_I*Infectious delta_I = 0.003
=> Cumulative_Cases; Exposed Country*kappa_rho*Exposed kappa_rho = 0.02332
Susceptible => Exposed; Infectious, Asymptomatic, Hospitalized Country*Susceptible*beta_1*(Infectious+l_a_1*Asymptomatic+l_1*Hospitalized)/Total_pop l_1 = 0.673; beta_1 = 0.334; l_a_1 = 8.0; Total_pop = 1.1E7 #
Asymptomatic => Deceased Country*mu_delta_A*Asymptomatic mu_delta_A = 0.0
Hospitalized => Deceased Country*delta_H*Hospitalized delta_H = 0.008
Exposed => Asymptomatic Country*kappa_1_rho*Exposed kappa_1_rho = 0.41668
Hospitalized => Recovered Country*gamma_r*Hospitalized gamma_r = 0.141
Exposed => Infectious Country*kappa_rho*Exposed kappa_rho = 0.02332
Infectious => Recovered Country*gamma_i*Infectious gamma_i = 0.263
Curator's comment:
(added: 20 Aug 2020, 09:40:45, updated: 20 Aug 2020, 09:40:45)
This model was encoded on COPASI 4.29 (Build 228). Fig 2 has been reproduced here To reproduce the figures, run a timecourse with the following conditions To reproduce Fig 2, set 1_Trigger_China = 1 and all other triggers to 0, duration = 60 To reproduce Fig 3, set 2_Trigger_Italy = 1 and all other triggers to 0, duration = 73 To reproduce Fig 4, set 3_Trigger_Spain = 1 and all other triggers to 0, duration = 74 To reproduce Fig 5, set 4_Trigger_France = 1 and all other triggers to 0, duration = 74 To reproduce Fig 6, set 5_Trigger_Germany = 1 and all other triggers to 0, duration = 73 To reproduce Fig 7, set 6_Trigger_USA = 1 and all other triggers to 0, duration = 102