Statistical Modelling of Infectious Disease and Environmental Systems

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Modelling the spread of infectious diseases is important for maintaining human, animal, and plant health, as well as maintaining economic security and international trade. Understanding what factors affect the spread of a disease, or which individuals are more likely to become infected is critical in preventing and controlling a disease outbreak.***A class of statistical models, known as “individual-level models”, describe the spread of an infectious disease through a population by considering information for each individual within that population. These individuals could be individual people, plants, houses, or farms, each with their own unique set of information (such as geographical location, whether they have been vaccinated, etc.). Individual-level models use this very specific, individual-level information to describe how an infectious disease may spread across both space and time. The advantage these models is that they are very flexible, and allow for a population in which individuals are heterogeneous (e.g. not all the same). However, these models are limited in that they require a large amount of computational time, which may be unacceptable in the event of a disease outbreak when it is necessary to quickly understand how a disease.******The research program described here aims to develop ways in which this computational burden can be reduced, while still accurately identifying factors which may influence the spread of the disease. Furthermore, this research program intends to develop a subgroup of individual-level models that can incorporate missing or potentially inaccurate individual-level data, a phenomenon that often arises in public health and other such data.******Another objective of this research program is related to environmental effects monitoring initiatives (i.e., the joint federal and provincial government oil sands monitoring program in Alberta). Like the idea of surveillance for a disease outbreak, to determine whether human activity is having an environmental impact on wild populations (i.e., fish), it is necessary to identify when there are significant changes in these populations (i.e., size, weight, tumour incidence, reproductive success, etc.). The surveillance techniques for monitoring environmental effects need to be able to adapt to natural changes in the environment, as well as distinguish observed effects that are due to industrial/human influence and are not within the natural variation that is always present within an ecosystem. This proposed research program aims to develop statistical models for monitoring environmental effects that can identify significant population changes, while incorporating information about the surrounding environment and human activities that may be related to these changes. This information is pivotal in detecting impacts caused by industrial activities, and assessing the extent of any such impacts.