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. © The Author(s) 2023.
ABSTRACT
Susceptible-Infected-Recovered (SIR) models have been used to study the spread of COVID-19. In previous works, the standard SIR model has been expanded to include new states as well as geographical level transmission dynamics. We present an extended model using the Cell-DEVS formalism that simulates the effect asymptomatic COVID-19 cases have on a population. The model is an easily adaptable framework that allows for rapid-prototyping and modifications. We exemplify how to build and easily change the model using public health units of Ontario as a case study. The results show the effect asymptomatic carriers have on overall case counts and exposures at the provincial level as well as at the city level. © 2021 IEEE.
ABSTRACT
Modeling and Simulation (MS) techniques have been proven to be effective to understand how diseases spread and assess the effectiveness of decisions aimed to control them (e.g., mobility restrictions). Recently, governments used this approach to determine the evolution of the COVID-19 pandemic. In this context, MS tools that consider geographical information can improve the quality of the simulations. This research presents a methodology that allows modelers to prototype disease spread models that include geographical information. The model can be easily parameterized for other geographical regions and diseases. We present a case study of a disease spread model to show how this methodology works. © 2021 SCS.