287459
Challenges in modeling emergent vector-borne diseases
Wednesday, November 6, 2013
Christopher Mores, ScD,
School of Veterinary Medicine, Epidemiology, Louisiana State University, Baton Rouge, LA
Rebecca Christofferson, PhD,
Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA
Jennifer Giovanni, MPH,
Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA
Justin Davis, PhD,
School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA
Carrie Manore, PhD,
Mathematics Department and Center for Computational Science, Tulane University, New Orleans, LA
Claudia Riegel, PhD,
City of New Orleans Mosquito, Termite & Rodent Control Board, New Orleans, LA
Jeanine Tessmer,
Livingston Parish Mosquito Abatement District, Denham Springs, LA
George Bragg,
West Baton Rouge Mosquito Abatement Program, Port Allen,, LA
Viki Taylor,
St Tammany Parish Mosquito Abatement District, Slidell, LA
Dawn Wesson, PhD,
Department of Tropical Medicine, School of Public Health and Tropical Medicine, New Orleans, LA
J Mac Hyman, PhD,
Department of Mathematics and Center for Computational Science, Tulane University, New Orleans, LA, Laos
Helen Wearing, PhD,
Department of Mathematics and Statistics, Department of Biology, University of New Mexico, Albuquerque, NM
Disease models are challenged with prediction for the purposes of prevention or mitigation of future outbreaks. External factors, e.g., lack of funding, often lead to interruptions of data collection and research on vector-borne pathogens that only sporadically affect the human population. This confounds our ability to anticipate outbreaks of disease. Looking at the 2012 West Nile epidemic, we asked 1) Can mosquito surveillance for WNV provide a reliable indication of human risk? And 2) Can models assess and correct for sampling bias in active versus inactive seasons? Using detailed datasets from New Orleans, St. Tammany, Livingston, and West Baton Rouge Parishes, we have developed mathematical models and applied statistical techniques to answer these critical questions. We present here a framework for the implementation of a predictive risk model that addresses the multiannual effects of dynamic avian reservoir demography and weather upon arbovirus activity indicators; we further provide uncertainty and sensitivity analyses to quantify bias in this model; and provide insight into the importance of and minimum requirements for baseline surveillance of emergent but quiescent diseases. A discussion of the generalizability of these results to other emergent vector-borne diseases such as dengue and chikungunya will follow.
Learning Areas:
Epidemiology
Other professions or practice related to public health
Program planning
Public health biology
Learning Objectives:
Discuss the importance of and minimum requirements for baseline surveillance of emergent but quiescent vector-borne diseases. Formulate a framework for interaction between public health officials and disease surveillance entities, especially mosquito abatement districts.
Keyword(s): Infectious Diseases, Public Health Research
Presenting author's disclosure statement:Qualified on the content I am responsible for because: I received a ScD from Harvard University School of Public Health and the principal investigator of a mathematical modeling of infectious disease grant (MIDAS, U01 mechanism). My scientific interests include the study of biological factors that drive transmission and the integration of my experimental work into the mathematical modeling of vector borne diseases.
Any relevant financial relationships? No
I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines,
and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed
in my presentation.
