Integrating Earth Observation and Field Data into a Lyme Disease Model to Map and Predict Risks to Biodiversity and Human HealthEPA Grant Number: DW-75-92243901
Title: Integrating Earth Observation and Field Data into a Lyme Disease Model to Map and Predict Risks to Biodiversity and Human Health
Investigators: Fish, Durland , Diuk-Wasser, Maria , Guan, Yongtao , Lobitz, Brad , Melton, Forrest , Nemani, Rama , Piesman, Joe , Pongsiri, Montira J. , Roman, Joe
Institution: Yale University , Centers for Disease Control and Prevention , NASA Ames , U. S. Environmental Protection Agency
EPA Project Officer: Hiscock, Michael
Project Period: January 1, 2007 through December 31, 2008
Project Amount: $250,000
RFA: Advanced Monitoring Initiative (2006) RFA Text | Recipients Lists
Research Category: Health Effects , Health
Lyme disease is the most prevalent vector-borne disease in the U.S., with more than 20,000 cases reported each year. The human risk varies geographically and is dependent upon the local distribution and abundance of vector-competent tick species and the available vertebrate host community, such as deer and mice, upon which the ticks depend. Risk can also vary by the genotype of the Lyme disease pathogen, Borrelia burgdorferi. Moreover, environmental factors and landscape features contribute to tick density, and consequently, the risk of human infection.
AMI support will expand an existing CDC-funded project by exploring the possible causal relationships between ecosystem changes and disease-transmission risk with the incorporation of remotely sensed and ground measures of biodiversity. Yale will conduct field data collection on tick density in deciduous forest habitats in the eastern U.S., including the entire range of the tick vector Ixodes scapularis. Tick samples will be assayed for genotype identification of the Borrelia spirochetes that cause Lyme disease. The prevalence and population structure of pathogenic agents found in I. scapularis will be combined with tick density measures to estimate the risk of human infection at each sample site.
Through modeling and spatial analysis, the proposed study will integrate new meteorological data and remotely sensed information on habitat characteristics from NASA satellites, in order to study the relationship between environmental indicators of host biodiversity, tick density, and infection prevalence. The proposed project will fully explore ecological processes in relation to vertebrate-host biodiversity and pathogen population structure, to answer:
- Does pathogen prevalence and population structure reveal spatial patterns that are dependent on climate and landscape characteristics?
- How do pathogen prevalence and genetic structure respond to changes in habitat structure, vertebrate communities, or other indicators of biodiversity?
Predictive risk models will be developed in year 1 and validated and refined during year 2. The final product of the proposed project will be a surface map of human risk for infection from tick-borne Lyme disease that can be routinely updated using meteorological and remotely sensed landscape data. The AMI project will result in an improved understanding of how environmental indicators and ground measures of biodiversity are related to the risk of Lyme disease infection. Decision-makers and public health managers can use the map to prevent and mitigate the human risk of disease. This approach can be applied to other vector-borne diseases where environmental factors play a major role, as well as to other areas outside of the U.S. where Lyme disease is prevalent. And, a better understanding of the role of ecological processes on disease-transmission risk can guide policies on sound land use management and the protection and restoration of ecological resources.