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Title Applications of Data-driven Modeling to Infectious Diseases in Africa: Anthrax in Wildlife and HIV in Humans
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Discipline/Department Environmental Science, Policy, & Management
University/Publisher University of California – Berkeley
Abstract The goal of epidemiology is to identify the biological, behavioral, and environmental causes of health outcomes or diseases and apply this knowledge to the development of effective disease interventions. Diseases are complex phenomena that arise from various interacting processes, challenging epidemiologists and disease ecologists to extract important causal relationships from observational and experimental data. While data from properly designed experimental studies are the gold standard for assessing the existence of a causal relationship, such studies may be logistically or morally infeasible in many situations. Observational data has the advantage of generally being less invasive, cheaper, and more readily available. However, such data are often plagued by a variety of biases, challenging our understanding of the underlying dynamical processes. However, by explicitly modeling the observation and sampling processes in addition to the underlying biological and behavioral processes of interest it is often possible to understand the latter more rigorously. In this dissertation, I develop empirical and analytical methods to understand the dynamics of rabies virus, canine distemper virus, <italic>Bacillus anthracis</italic>, and the human immunodeficiency virus using observational surveillance data. Importantly, models are built from the data up with the focus being on what is known about the system from the data rather than other mechanistic processes for which we know little.
Subjects/Keywords Biology; Health sciences; Animal diseases; anthrax; canine distemper virus; HIV; jackal; rabies; zebra
Language en
Rights public
Country of Publication us
Format application/pdf
Record ID california:qt4xx9c4f0
Other Identifiers qt4xx9c4f0
Repository california
Date Indexed 2018-02-26

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