Biological Hazards and their Impact on South Carolina: A Hazard Assessment of Zoonotic Avian Influenza
Reggie L. McCarn
Advisor: Dr. Susan L. Cutter
This thesis examines the relative vulnerability of South Carolina to Avian Influenza by means of two exposure models: naturally occurring or human-induced. The research questions ask: 1) Which exposure model produces the greatest spatial impact? 2) Which exposure model poses the most threat to the general public? 3) Are there socioeconomic or racial disparities in the exposed populations by each model?
Whether spread by insects, birds, or in a malicious manner, infectious diseases and bioagents can be tracked through spatial interactions and patterns. For instance, the geographical patterns of interaction between infected and susceptible hosts are crucial for understanding how and where infectious diseases spread. Spatial diffusion is amplified in urban centers with large populations and transportation systems which move millions of people from one place to another. Major issues for geographers and emergency pplanners alike are trying to understand how an agent could be released, how and where it could spread, and what portion of the population would be most vulnerable.
The research presented in this thesis addresses these major issues relevant to the exposure modeling of human populations to Avian Influenza. The state of South Carolina is the base for a hazard vulnerability assessment to study the distribution of waterfowl habitats and their proximity to human populations and poultry related facilities that could be adversely affected by an outbreak of Avian Influenza, whether occurring naturally or human-induced. Through a series of procedures utilizing geospatial techniques, two models (natural occurence, human-induced) are used to determine the spatial impact, those populations most vulnerable to such a hazard, and, if any, socioeconomic or racial disparities that may exist in the exposed populations. The two models are an integration of biophysical and social factors to produce an overall vulnerability surface. The results from the models reveal two very different surfaces, but indicate that the coastal plain of South Carolina is most vulnerable from both identified social and physical elements. Clusters of less vulnerable populations are identified elsewhere across the state. Based on the model results in this thesis, public health officials and the poultry industry may have a better understanding of where to place resources to fight an attack from avian influenza.