Title:
Artificial Intelligence for Data-centric Surveillance and Forecasting of Epidemics
Artificial Intelligence for Data-centric Surveillance and Forecasting of Epidemics
Author(s)
Rodriguez Castillo, Alexander D.
Advisor(s)
Prakash, B. Aditya
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Abstract
Surveillance and forecasting of epidemics are crucial tools for decision making and planning of government officials, businesses, and the general public. In many respects, our understanding of how epidemics spread is still at its infancy, despite multiple advances in understanding how diseases spread in the population. Many of the major challenges stem from other complex dynamics, such as mobility patterns, policy compliance, and even shifts in data collection procedures. As a result of efforts to collect and process data from novel sources, granular data are becoming increasingly available on many of these variables. These datasets, however, are difficult to exploit using traditional methodologies from mathematical epidemiology and agent-based modeling. Alternatively, AI methods in epidemiology are challenged by data sparsity, distributional changes, and disparities in data quality. AI also lacks understanding of epidemic dynamics, which may lead to unrealistic predictions. Several frameworks are proposed in this dissertation to address these challenges and move toward more data-centric methods. Specifically, we utilize multiple examples to showcase that bringing the data-driven expressibility of AI into epidemiology leads to more sensitive and precise surveillance and forecasting of epidemics.
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Date Issued
2023-08-15
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Text
Resource Subtype
Dissertation