There are critical public health planning questions about how to respond to a biosecurity breach. The questions are challenging because the direct data that is available is limited. In this talk, we consider the case of anthrax and how statistical models, novel data sources, and efficient use of information can be brought together to address some key public health policy concerns.
We address the role that speedy detection of outbreaks can play in reducing morbidity and mortality, and the impact of public health control strategies such as antibiotic and vaccination policy. We address these questions through the development of a mechanistic model that accounts for the biology of anthrax spore clearance and germination based on a competing risks formulation. We use data from multiple sources including the 2001 anthrax outbreak in the United States, an anthrax outbreak in Russia, and studies from primates. We estimate the incubation period of disease from a statistical analysis of the data using the mechanistic competing risks model. The models are used to determine how long exposed persons should remain on antibiotics, the additional preventive value of vaccines, and the importance of early detection of outbreaks. The talk illustrates how statistical reasoning, models, and novel data sources can help develop effective public health response policies in the event of either intentional or naturally occurring outbreaks.