Bertozzi2020 - SIR model of scenarios of COVID-19 spread in CA and NY

  public model
Model Identifier
BIOMD0000000956
Short description
The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.
Format
SBML (L2V4)
Related Publication
  • The challenges of modeling and forecasting the spread of COVID-19.
  • Bertozzi AL, Franco E, Mohler G, Short MB, Sledge D
  • Proceedings of the National Academy of Sciences of the United States of America , 7/ 2020 , Volume 117 , Issue 29 , pages: 16732-16738 , PubMed ID: 32616574
  • Department of Mathematics, University of California, Los Angeles, CA 90095; bertozzi@ucla.edu.
  • The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.
Contributors
Submitter of the first revision: Kausthubh Ramachandran
Submitter of this revision: Kausthubh Ramachandran
Modellers: Kausthubh Ramachandran

Metadata information

is (2 statements)
BioModels Database MODEL2008070001
BioModels Database BIOMD0000000956

isDescribedBy (1 statement)
PubMed 32616574

hasTaxon (2 statements)
Taxonomy SARS-CoV-2
Taxonomy Homo sapiens

hasProperty (3 statements)
Infectious Disease Ontology 0000503
Human Disease Ontology COVID-19
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
Bertozzi2020.xml SBML L2V4 representation of challenges in modelling and forecasting the spread of COVID-19 48.58 KB Preview | Download

Additional files
Bertozzi2020.cps CPS file created on COPASI version 4.28 (Build 226) representation of challenges in modelling and forecasting the spread of COVID-19 74.39 KB Preview | Download
Bertozzi2020.omex COMBINE archive representation of challenges in modelling and forecasting the spread of COVID-19 13.63 KB Preview | Download
Bertozzi2020.sedml SED-ML file representation of challenges in modelling and forecasting the spread of COVID-19 10.28 KB Preview | Download

  • Model originally submitted by : Kausthubh Ramachandran
  • Submitted: Aug 7, 2020 8:53:22 PM
  • Last Modified: Oct 5, 2020 11:09:56 PM
Revisions
  • Version: 9 public model Download this version
    • Submitted on: Oct 5, 2020 11:09:56 PM
    • Submitted by: Kausthubh Ramachandran
    • With comment: Automatically added model identifier BIOMD0000000956
  • Version: 6 public model Download this version
    • Submitted on: Aug 7, 2020 9:16:09 PM
    • Submitted by: Kausthubh Ramachandran
    • With comment: Automatically added model identifier BIOMD0000000956
  • Version: 3 public model Download this version
    • Submitted on: Aug 7, 2020 8:59:26 PM
    • Submitted by: Kausthubh Ramachandran
    • With comment: Automatically added model identifier BIOMD0000000956
  • Version: 2 public model Download this version
    • Submitted on: Aug 7, 2020 8:53:22 PM
    • Submitted by: Kausthubh Ramachandran
    • With comment: Edited model metadata online.

(*) 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
Infected

C171133 ; 0000511 		1.2639029322548E-7 item
Susceptible

0000514 ; C171133 		0.999999999999997 item
Recovered

C171133 ; 0000621 		0.0 item
Reactions
Reactions Rate Parameters
Susceptible => Infected USA___CA__NY*gamma*Ro*Infected*Susceptible gamma = 0.14; Ro = 2.7
Infected => Recovered USA___CA__NY*gamma*Infected gamma = 0.14
Curator's comment:
(added: 07 Aug 2020, 20:58:39, updated: 07 Aug 2020, 21:13:17)
Following parameter changes were done - for Fig 2, Ro was changed to 1.954 from 2.0, Io was changed to 5 from 0.025 for Fig 3, Io value for CA was changed to 5 from 0.025, Io value for NY was changed to 29 from 0.005. These changes were the results of parameter scans in COPASI to get the best fit. For Fig 2, keep Trigger_Lockdown = 0 and Trigger_CA = 1 Figure 2 (left) and (middle) - Perform a value-based parameter scan with Ro = 1.954 and Io having values 3956, 39.56, 0.3956, 0.003956 and plot [I] vs [Time] (global quantity) Figure 2 (right) - Perform a value-based parameter scan with Ro having the values 4.8, 2.4, 1.8 and Io = 5 For Fig 2 (middle), export the data and plot [Peak_time] vs log(epsilon) in R/Microsoft Excel For Fig 3 (left), keep Trigger_NY = 0 Keep Trigger_CA = 1, Trigger_Lockdown = 0, and run a timecourse for 160 days with interval size = 1. Export the data from the generated graph Keep Trigger_CA = 1, Trigger_Lockdown = 1, and run a timecourse for 160 days with interval size = 1. Export the data from the generated graph Plot both exported data in R/Microsoft Excel For Fig 3 (right) For Fig 3 (left), keep Trigger_CA = 0 Keep Trigger_NY = 1, Trigger_Lockdown = 0, and run a timecourse for 160 days with interval size = 1. Export the data from the generated graph Keep Trigger_NY = 1, Trigger_Lockdown = 1, and run a timecourse for 160 days with interval size = 1. Export the data from the generated graph Plot both exported data in R/Microsoft Excel