Agent-based modelling and simulation of human mobility in the context of infectious disease spread: Development and application for the case of COVΙD-19 spread in Larnaca, Cyprus
Date Issued
May 2026
Author(s)
Fayad, Philip
Advisor
Abstract
This dissertation investigates the role of geoinformatics in enhancing the understanding
and management of epidemiological crises, using the COVID19 pandemic in Cyprus as
a case study. The research integrates spatial analysis, cartographic visualization, and
agent-based modelling to examine how human mobility and behavioral patterns influence
disease transmission.
A comprehensive geoinformatics framework was developed, including static and
dynamic maps as well as an interactive Web-GIS platform, enabling the monitoring and
visualization of the spatial and temporal evolution of COVID19. In parallel, empirical
mobility data were collected through a large-scale questionnaire survey, providing
detailed insights into the spatiotemporal behavior of Cypriot residents.
Building on these data, a novel agent-based model (EPIMO-LCA) was designed to
simulate the spread of the virus at the local level, incorporating realistic mobility patterns
and demographic characteristics. The model was validated against real epidemiological
data during the Delta and Omicron phases, demonstrating its ability to reproduce observed
trends and assess the effectiveness of non-pharmaceutical interventions.
The findings highlight the critical role of human behavior in shaping epidemic dynamics
and demonstrate the value of localized, data-driven modelling approaches for supporting
evidence-based public health decision-making.
and management of epidemiological crises, using the COVID19 pandemic in Cyprus as
a case study. The research integrates spatial analysis, cartographic visualization, and
agent-based modelling to examine how human mobility and behavioral patterns influence
disease transmission.
A comprehensive geoinformatics framework was developed, including static and
dynamic maps as well as an interactive Web-GIS platform, enabling the monitoring and
visualization of the spatial and temporal evolution of COVID19. In parallel, empirical
mobility data were collected through a large-scale questionnaire survey, providing
detailed insights into the spatiotemporal behavior of Cypriot residents.
Building on these data, a novel agent-based model (EPIMO-LCA) was designed to
simulate the spread of the virus at the local level, incorporating realistic mobility patterns
and demographic characteristics. The model was validated against real epidemiological
data during the Delta and Omicron phases, demonstrating its ability to reproduce observed
trends and assess the effectiveness of non-pharmaceutical interventions.
The findings highlight the critical role of human behavior in shaping epidemic dynamics
and demonstrate the value of localized, data-driven modelling approaches for supporting
evidence-based public health decision-making.
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