Modeling the COVID-19 pandemic: a sensitivity analysis on input data using agent-based transportation simulation

Bias in input data is likely to induce biased modeling predictions and endorse policy decisions with, at best, unpredictable consequences. In this regard, it is vital to assess the reliability and accuracy of epidemic models. In this research paper we tackle two research questions: 1. Are COVID-19 agent-based models sensitive towards bias in input data? and 2. What is the impact of such sensitivity on impact predictions? To answer these questions we rely on a transportation agent-based model, MATSIM, coupled with an epidemic model, EPISIM

• Record URL:
  https://www.cirrelt.ca/documentstravail/cirrelt-2020-34.pdf
• 
• Corporate Authors:

  Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT)

  Montreal, Quebec   
• Authors:
  • Manout, O
  • El-Megzari, I
  • CIARI, F
• Publication Date: 2020-9

Language

• English

Media Info

• Pagination: 20p

Subject/Index Terms

• TRT Terms: Accuracy; Forecasting; Health; Mathematical models; Policy; Reliability; Travel behavior
• Geographic Terms: Montreal, Quebec, Canada
• ATRI Terms: Accuracy; Forecast; Health; Modelling; Policy; Reliability; Travel behaviour
• Subject Areas: Policy;

Filing Info

• Accession Number: 01757302
• Record Type: Publication
• Source Agency: ARRB Group Limited
• Report/Paper Numbers: CIRRELT-2020-34
• Files: ATRI
• Created Date: Nov 5 2020 9:47AM