MAM: Flexible Monte-Carlo Agent based model for modelling COVID-19 spread

Hilla De-Leon, Dvir Aran

Research output: Contribution to journalArticlepeer-review


In the three years since SARS-CoV-2 was first detected in China, hundreds of millions of people have been infected and millions have died. Along with the immediate need for treatment solutions, the COVID-19 epidemic has reinforced the need for mathematical models that can predict the spread of the pandemic in an ever-changing environment. The susceptible-infectious-removed (SIR) model has been widely used to model COVID-19 transmission, however, with limited success. Here, we present a novel, dynamic Monte-Carlo Agent-based Model (MAM), which is based on the basic principles of statistical physics. Using public aggregative data from Israel on three major outbreaks, we compare predictions made by SIR and MAM, and show that MAM outperforms SIR in all aspects. Furthermore, MAM is a flexible model and allows to accurately examine the effects of vaccinations in different subgroups, and the effects of the introduction of new variants.

Original languageEnglish
Article number104364
JournalJournal of Biomedical Informatics
StatePublished - May 2023


  • Covid-19
  • Infectious diseases
  • Modeling

All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Computer Science Applications


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