Dynamic models in infectious disease and health and healthcare more generally integrate information and processes across many domains. Model modularity serves as a key enabler for smooth and flexible coordination between domains --- for instance, between pathogen transmission, human behavior, and genomic data. Such modularity allows domain experts to independently build and refine clearly delineated model components which are composed into a single complex model. This approach divides the complexity of model building into two factors: the development of submodels by domain experts and the integration of the submodels into the whole. Applied category theory, the mathematics of composition, tackles this second challenge. Composing models using the tools of applied category theory provides visual transparency for stakeholders, formal analyzability, alignment between types of model heterogeneity (e.g., modular stratification), and opportunities for optimization and parallelization. In this course, we will show how these mathematical tools and their implementation in the open-source AlgebraicJulia programming environment can be used to rapidly develop transparent dynamic models in health, with a particular emphasis on models of infectious diseases.

Beyond teaching these fundamental advances in modeling methodology and theory, the course will explore the strong application potential for these platforms, where students will gain experience in use of existing toolsets that make practical a variety of types of compositional modeling. These tools include interactive application programming interfaces as well as browser-based interactive, collaborative, graphical user interfaces for compositional modeling. Examples model will be drawn from conditions such as COVID-19, measles, pertussis, and other infectious, zoonotic and chronic diseases.

[APS Division of Fluid Dynamics, 21 November 2021]

Properly designed facemasks can limit the spread of ballistic droplets and aerosol particles coming out of oral and nasal cavities during respiratory events, such as sneezing, coughing, singing, talking etc. Furthermore, it can also protect the user from inhaling small droplets, droplet nuclei, or aerosol particles. Thus, proper usage of facemasks can prevent the transmission of many diseases, including Covid19, influenza, measles, and the common cold. Although N95 masks are particularly designed to provide the best protection, various types of facemask became popular during the Covid19 pandemic due to a shortage of supply and high demand. In our recent study (Sharma et al. Sc. Adv. (2021) 7, eabf0452), we reported the fate of a respiratory droplet impacting on a dry facemask to show that larger droplets can penetrate the mask layers and undergo secondary atomizations leading to multiple smaller droplets. In this work, we focus on the effect of the wetness of the mask matrix on this atomization process. Indeed, due to the condensation process, longtime use renders the masks wet, and hence, its influence on the efficacy in blocking the droplet is worth investigating. We will present a regime map to show the penetration probability with impact velocity and wetness for two different types of masks. We will also present a scaling argument to explain the observed effects of wetness on penetration.

We introduce Pathogen.jl for simulation and inference of transmission network individual level models (TN-ILMs) of infectious disease spread in continuous time. TN-ILMs can be used to jointly infer transmission networks, event times, and model parameters within a Bayesian framework via Markov chain Monte Carlo (MCMC). We detail our specific strategies for conducting MCMC for TN-ILMs, and our implementation of these strategies in the Julia package, Pathogen.jl, which leverages key features of the Julia language. We provide an example using Pathogen.jl to simulate an epidemic following a susceptible-infectious-removed (SIR) TN-ILM, and then perform inference using observations that were generated from that epidemic. We also demonstrate the functionality of Pathogen.jl with an application of TN-ILMs to data from a measles outbreak that occurred in Hagelloch, Germany in 1861(Pfeilsticker 1863; Oesterle 1992).

[BMC Infectious Diseases, 19 February 2025]

Background: The global incidence of measles has increased markedly since 2023. In Canada, where measles has had elimination status for more than two decades, most cases can typically be traced to travel. While the majority of Canadians are vaccinated against the measles virus, or considered immune due to previous infection, there are communities with low vaccination coverage.

Methods: In this study, we develop a stochastic Susceptible-Exposed-Infectious-Recovered model to explore what measles outbreaks could look like upon importation into Canada under a number of scenarios, vaccination coverage levels, and public health interventions. We collect reports of real-world measles outbreaks and compare them to our model outbreaks’ size and duration.

Results: Our model suggests that community level outbreaks can be controlled at or above 85% vaccination coverage with public health interventions and that above 95% coverage, 99% of measles introductions do not result in an outbreak. Below 85% coverage, outbreaks in small communities (size 1000) with relatively strong public health measures range from median size of under 4 (80% coverage) to 186 (55%), comparable to reported outbreaks in Canada and elsewhere. Outbreaks very often last under 60 days. We characterize how outbreak sizes and durations depend on the strength of interventions, community size and vaccination coverage. We make the model available as a web-based ‘shiny’ application.

Conclusions: Since the vast majority of measles cases in Canada can be traced to imported cases, our model serves as a last step in the chain of actions needed to bridge from global measles outbreaks to local scenarios within Canada. Given cases entering Canada, we are able to project the duration and size of an outbreak, helping to inform the public of the measles-related risk.

Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada: Canadian Network for Modelling Infectious Disease (CANMOD), and the Canada 150 Research Chair Program (CC).