|
Abstract:
|
Infectious diseases are changing due to the environment and altered interactions among hosts, reservoirs, vectors, and pathogens. Vector-borne pathogens are changing more rapidly with climate change and due to their complex epidemiology may allow them to take advantage of a changing environment. The urgency for improved predictive capability is particularly true for anticipated climatic shifts impacting infectious diseases. An approach capable of predicting and forecasting vector borne diseases by coupling climate with epidemiological models, data fusion with data on differing spatial scales and animal and population movement is greatly needed. We propose a strategy for coupling climate and epidemiological models for vector-borne infectious diseases that addresses the complexity and challenges of data and model fusion, baseline requirements for data, and animal and human population movement. An infrastructure with data on the environment, vectors, and hosts at all spatial and temporal resolutions can lead to improved disease forecasting capabilities with quantified uncertainty that can support public health officials.
|
