Paiva2020 - SEIAHRD model of transmission dynamics of COVID-19

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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
Kausthubh Ramachandran

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Curated

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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: 20-Aug-2020 09:42:09
  • Last Modified: 05-Oct-2020 23:19:16
Revisions
  • Version: 9 public model Download this version
    • Submitted on: 05-Oct-2020 23:19:16
    • Submitted by: Kausthubh Ramachandran
    • With comment: Automatically added model identifier BIOMD0000000960
  • Version: 6 public model Download this version
    • Submitted on: 26-Aug-2020 16:12:37
    • Submitted by: Kausthubh Ramachandran
    • With comment: Automatically added model identifier BIOMD0000000960
  • Version: 3 public model Download this version
    • Submitted on: 20-Aug-2020 09:42:09
    • Submitted by: Kausthubh Ramachandran
    • With comment: Automatically added model identifier BIOMD0000000960

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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