A methodological approach for modeling the spread of disease using geographical discrete-event spatial models

Abstract

The study of infectious disease models has become increasingly important during the COVID-19 pandemic. The forecasting of disease spread using mathematical models has become a common practice by public health authorities, assisting in creating policies to combat the spread of the virus. Common approaches to the modeling of infectious diseases include compartmental differential equations and cellular automata, both of which do not describe the spatial dynamics of disease spread over unique geographical regions. We introduce a new methodology for modeling disease spread within a pandemic using geographical models. We demonstrate how geography-based Cell-Discrete-Event Systems Specification (DEVS) and the Cadmium JavaScript Object Notation (JSON) library can be used to develop geographical cellular models. We exemplify the use of these methodologies by developing different versions of a compartmental model that considers geographical-level transmission dynamics (e.g. movement restriction or population disobedience to public health guidelines), the effect of asymptomatic population, and vaccination stages with a varying immunity rate. Our approach provides an easily adaptable framework that allows rapid prototyping and modifications. In addition, it offers deterministic predictions for any number of regions simulated simultaneously and can be easily adapted to unique geographical areas. While the baseline model has been calibrated using real data from Ontario, we can update and/or add different infection profiles as soon as new information about the spread of the disease become available.

A data-driven approach of layout evaluation for electric vehicle charging infrastructure using agent-based simulation and GIS

“…While the focus of this study is on the EVCI location problem, it is worth noting that the GIS-based simulation methods have found utility in other disciplines. For instance, Davidson et al 48 developed a discrete-event spatial model to simulate disease spread within a pandemic, demonstrating its adaptability and ability to provide deterministic predictions for multiple regions simultaneously. Other studies using simulation to investigate the spread of infectious diseases can be found in the work by Abadeer et al, 49 as well as the review article by Ayadi et al 50 Iskandar et al 51 developed an agent-based model that incorporates realistic human behavior and urban conditions to simulate pedestrian evacuation during earthquakes at the city scale, revealing the impact of debris and human behavior on population safety and the limited capacity of open spaces to provide shelters in Beirut, Lebanon.…”

A framework for modeling human behavior in large-scale agent-based epidemic simulations

“…While these models have become quite sophisticated over the years and advanced techniques have been applied to optimize the SEIR parameters (e.g., via swarm optimization 1 ), they mostly rely on assumptions such as homogeneity of the individuals in a population and the distinction of agents solely based on their disease state, that limit the accuracy of the predictions that can be made. [2][3][4][5] In the real world, however, the spread of a disease is not only affected by the properties of the virus itself, but also by sociopsychological behavioral dynamics that heavily rely on aspects that are heterogeneous across individuals (including spatial and demographic heterogeneity 6 ). Squazzoni et al 7 point out that, given the significant adverse effects that behavioral interventions can have on individuals, decisions ''cannot be solely based on epidemiological knowledge, because the efficacy of implementation depends on people's reactions, pre-existing social norms, and structural societal constraints.…”

A Framework for Rapid Prototyping of Geographical Disease Spread with Human Behaviour Elements

“…In [13], the authors present a SEAIRD model that showed a similar transition method as those described previously. They use an asymptomatic state where an asymptomatic rate α is defined to split the infected population into infectious or asymptomatic.…”

“…Our implementation allows users to run the model at a user-defined region level and visualize how a disease might spread through a city, town, or country. The model is designed using the Cell-DEVS formalism [12] and implemented using the Cadmium simulator [13]. The model's adaptable framework allows for accessible rapid-prototyping and modifications.…”