[Journal of Applied Mathematics and Physics, 18 May 2022]

We present a mathematical model of a day care center in a developed country (such as Canada), in order to use it for the estimation of individual-to-individual contact rates in young age groups and in an educational group setting. In our model, individuals in the population are children (ages 1.5 to 4 years) and staff, and their interactions are modelled explicitly: person-to-person and person-to-environment, with a very high time resolution. Their movement and meaningful contact patterns are simulated and then calibrated with collected data from a child care facility as a case study. We present these calibration results as a first part in the further development of our model for testing and estimating the spread of infectious diseases within child care centers.

[Vaccine, 6 April 2023]

Introduction: The re-emergence of pertussis has occurred in the past two decades in developed countries. The highest morbidity and mortality is seen among infants. Vaccination in pregnancy is recommended to reduce the pertussis burden in infants.

Methods: We developed and validated an agent-based model to characterize pertussis epidemiology in Alberta. We computed programmatic effectiveness of pertussis vaccination during pregnancy (PVE) in relation to maternal vaccine coverage and pertussis disease reporting thresholds. We estimated the population preventable fraction (PFP) of different levels of maternal vaccine coverage against counterfactual ”no-vaccination” scenario. We modeled the effect of immunological blunting and measured protection through interruption of exposure pathways.

Results: PVE was inversely related to duration of passive immunity from maternal immunization across most simulations. In the scenario of 50% maternal vaccine coverage, PVE was 87% (95% quantiles 82–91%), with PFP of 44% (95% quantiles 41–45%). For monthly age intervals of 0–2, 2–4, 4–6 and 6–12, PVE ranged between 82 and 99%, and PFP ranged between 41 and 49%. At 75% maternal vaccine coverage, PVE and PFP were 90% (95% quantiles 86–92%) and 68% (95% quantiles 65–69%), respectively. At 50% maternal vaccine coverage and 10% blunting, PVE and PFP were 86% (95% quantiles 77–87%) and 43% (95% quantiles 39–44%), respectively, while at 50% blunting, the corresponding values of PVE and PFP were 76% (95% quantiles 70–81%) and 38% (95% quantiles 35–40%). PVE attributable to interruption of exposure pathways was 54–57%.

Conclusions: Our model predicts significant reduction in future pertussis cases in infants due to maternal vaccination, with immunological blunting slightly moderating its effectiveness. The model is most sensitive to maternal vaccination coverage. The interruption of exposure pathways plays a role in the reduction of pertussis burden in infants due to maternal immunization. The effect of maternal immunization on population other than infants remains to be elucidated.

[Preprints, 1 December 2022]

Agent-based models (ABMs) are computational models for simulating the actions and interactions of autonomous agents in time and space. These models allow users to simulate the complex interactions between individual agents and the landscapes they inhabit and are increasingly used in epidemiology to understand complex phenomena and make predictions. However, as the complexity of the simulated systems increases, notably when disease control interventions are considered, model flexibility and processing speed can become limiting. Here we introduce SamPy, an open-source Python library for stochastic agent-based modeling of epidemics. SamPy is a modular toolkit for model development, providing adaptable modules that capture host movement, disease dynamics, and disease control interventions. Memory optimization and design provide high computational efficiency allowing modelling of large, spatially-explicit populations of agents over extensive geographical areas. In this article, we demonstrate the high flexibility and processing speed of this new library.

The version of SamPy considered in this paper is available at:

https://github.com/sam...

[IEEE Transactions on Engineering Management, August 2023]

Hospitals and other healthcare settings use various simulation methods to improve their operations, management, and training. The COVID-19 pandemic, with the resulting necessity for rapid and remote assessment, has highlighted the critical role of modeling and simulation in healthcare, particularly distributed simulation (DS). DS enables integration of heterogeneous simulations to further increase the usability and effectiveness of individual simulations. This article presents a DS system that integrates two different simulations developed for a hospital intensive care unit (ICU) ward dedicated to COVID-19 patients. AnyLogic has been used to develop a simulation model of the ICU ward using agent-based and discrete event modeling methods. This simulation depicts and measures physical contacts between healthcare providers and patients. The Unity platform has been utilized to develop a virtual reality simulation of the ICU environment and operations. The high-level architecture, an IEEE standard for DS, has been used to build a cloud-based DS system by integrating and synchronizing the two simulation platforms. While enhancing the capabilities of both simulations, the DS system can be used for training purposes and assessment of different managerial and operational decisions to minimize contacts and disease transmission in the ICU ward by enabling data exchange between the two simulations.

[International Journal of Environmental Research and Public Health, 24 February 2022]

The elderly, especially those individuals with pre-existing health problems, have been disproportionally at a higher risk during the COVID-19 pandemic. Residents of long-term care facilities have been gravely affected by the pandemic and resident death numbers have been far above those of the general population. To better understand how infectious diseases such as COVID-19 can spread through long-term care facilities, we developed an agent-based simulation tool that uses a contact matrix adapted from previous infection control research in these types of facilities. This matrix accounts for the average distinct daily contacts between seven different agent types that represent the roles of individuals in long-term care facilities. The simulation results were compared to actual COVID-19 outbreaks in some of the long-term care facilities in Ontario, Canada. Our analysis shows that this simulation tool is capable of predicting the number of resident deaths after 50 days with a less than 0.1 variation in death rate. We modeled and predicted the effectiveness of infection control measures by utilizing this simulation tool. We found that to reduce the number of resident deaths, the effectiveness of personal protective equipment must be above 50%. We also found that daily random COVID-19 tests for as low as less than 10% of a long-term care facility’s population will reduce the number of resident deaths by over 75%. The results further show that combining several infection control measures will lead to more effective outcomes.

[Mathematics of Public Health, 7 August 2023]

Agent-based modeling is a computational dynamic modeling technique that may be less familiar to some readers. Agent-based modeling seeks to understand the behavior of complex systems by situating agents in an environment and studying the emergent outcomes of agent-agent and agent-environment interactions. In comparison with compartmental models, agent-based models offer simpler, more scalable, and flexible representation of heterogeneity, the ability to capture dynamic and static network and spatial context, and the ability to consider history of individuals within the model. In contrast, compartmental models offer faster development time with less programming required, lower computational requirements that do not scale with population, and the option for concise mathematical formulation with ordinary, delay, or stochastic differential equations supporting derivation of properties of the system behavior.