Guest lecture on “An agent-based model of spread of a pandemic” by Prof Amitava Datta

Mon, 06 Dec 2021
10:16 am
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The Department of Informatics, Institut Teknologi Sepuluh Nopember (ITS), presented a guest lecture by Prof. Amitava Datta from the University of Western Australia. The lecture was held on Friday, October 29, 2021. Ary Mazharuddin Shiddiqi, Ph.D. (a lecturer at the Department of Informatics, ITS) was the event’s moderator. The event was held online through the Zoom platform and also live-streamed on the Department of Informatics’ YouTube channel. The guest lecture was about “An agent-based model of the spread of a pandemic,” which received great enthusiasm, with more than 65 participants attending. The event was part of the Implementation of the Independent Campus Competition Program (Program Kompetisi Kampus Merdeka, PKKM).

As an introduction, Prof. Amitava Datta explained that he investigated the topic in collaboration with professor Surajit Sen as a physicist and professor Peter Winkelstein (an epidemiologist from the state university of new york at buffalo). Prof. Amitava Datta said that his research had been published in the physics journal Physica A: Statistical Mechanics and its Applications. The lecture mainly discussed the importance of modeling the spread of a pandemic and developing the models as accurately as possible in real situations.

The research developed a novel agent-based model where each agent carries an effective viral load that captures the instantaneous state of infection of the agent. The spread of a pandemic was simulated and subsequently validated using publicly available COVID-19 data. This research tracks the temporal evolution of a virtual city or community of agents in terms of contracting infection, recovering asymptomatically, or getting hospitalized. The simulation results are consistent with the publicly available hospitalization and ICU patient data from the distinct regions of varying sizes in New York State. The model can predict the epidemic spread and hospitalization trend from a set of simple parameters and could be potentially valuable in predicting the disease evolution based on available data and observation about public behavior. The model simulation suggests that relaxing the social distancing measures may increase the hospitalization numbers by some 30 % or more. At the end of the guest lecture, a group photo was taken with online participants to conclude the event.

If you are interested in this talk, the record can be accessed at


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