Data assimilation and agent-based modelling: towards the incorporation of categorical agent parameters

This paper explores the use of a particle filter—a data assimilation method—to incorporate real-time data into an agent-based model. We apply the method to a simulation of real pedestrians moving through the concourse of Grand Central Terminal in New York City (USA). The results show that the particle filter does not perform well due to (i) the unpredictable behaviour of some pedestrians and (ii) because the filter does not optimise the categorical agent parameters that are characteristic of this type of model. This problem only arises because the experiments use real-world pedestrian movement data, rather than simulated, hypothetical data, as is more common. We point to a potential solution that involves resampling some of the variables in a particle, such as the locations of the agents in space, but keeps other variables such as the agents’ choice of destination. This research illustrates the importance of including real-world data and provides a proof of concept for the application of an improved particle filter to an agent-based model. The obstacles and solutions discussed have important implications for future work that is focused on building large-scale real-time agent-based models

Incorporating Particle Filtering and System Dynamic Modelling in Infection Transmission of Measles and Pertussis

Childhood viral and bacterial infections remain an important public problem, and research into their dynamics has broader scientific implications for understanding both dynamical systems and associated methodologies at the population level. Measles and pertussis are two important childhood infectious diseases. Measles is a highly transmissible disease and is one of the leading causes of death among young children under 5 globally. Pertussis (whooping cough) is another common childhood infectious disease, which is most harmful for babies and young children and can be deadly. While the use of ongoing surveillance data and - recently - dynamic models offer insight on measles (or pertussis) dynamics, both suffer notable shortcomings when applied to measles (or pertussis) outbreak prediction. In this thesis, I apply the Sequential Monte Carlo approach of particle filtering, incorporating reported measles and pertussis incidence for Saskatchewan during the pre-vaccination era, using an adaptation of a previously contributed measles and pertussis compartmental models. To secure further insight, I also perform particle filtering on age structured adaptations of the models. For some models, I further consider two different methods of configuring the contact matrix. The results indicate that, when used with a suitable dynamic model, particle filtering can offer high predictive capacity for measles and pertussis dynamics and outbreak occurrence in a low vaccination context. Based on the most competitive model as evaluated by predictive accuracy, I have performed prediction and outbreak classification analysis. The prediction results demonstrated that the most competitive models could predict the measles and pertussis outbreak patterns and classify whether there will be an outbreak or not in the next month (Area under the ROC Curve of measles is 0.89, while pertussis is 0.91). I conclude that anticipating the outbreak dynamics of measles and pertussis in low vaccination regions by applying particle filtering with simple measles and pertussis transmission models, and incorporating time series of reported case counts, is a valuable technique to assist public health authorities in estimating risk and magnitude of measles and pertussis outbreaks. Such approach offers particularly strong value proposition for other pathogens with little-known dynamics, important latent drivers, and in the context of the growing number of high-velocity electronic data sources. Strong additional benefits are also likely to be realized from extending the application of this technique to highly vaccinated populations