Data-model fusion to better understand emerging pathogens and improve infectious disease forecasting

Area of Science:

• Ecology and infectious disease dynamics.
• Environmental change impacts and forecasting.
• Data-model fusion for ecological research.

Background:

• Ecologists face challenges in predicting ecosystem responses to global change.
• Infectious disease studies, particularly zoonotic and vector-borne diseases, require advanced forecasting methods.
• Current methods for directly transmitted diseases are often insufficient for complex ecological disease systems.

Purpose of the Study:

• To demonstrate how advances in disease forecasting necessitate a deeper understanding of host-vector populations and pathogen spillover dynamics.
• To address challenges in disease forecasting, including limited data, spatiotemporal variability, and insufficient biological understanding.
• To present a novel conceptual framework for data-model fusion in infectious disease research.

Main Methods:

• Utilized four case studies to explore challenges in disease forecasting.
• Developed a conceptual framework employing a hierarchical state-space structure.
• Integrated multiple data sources and spatial scales to inform latent parameters and partition uncertainty.

Main Results:

• Case studies highlighted limitations in data and understanding for zoonotic and vector-borne disease forecasting.
• The proposed framework effectively integrates diverse data and ecological knowledge.
• The framework partitions uncertainty in process and observation models, building on existing understanding.

Conclusions:

• Effective disease forecasting requires integrating ecological and epidemiological data and expertise.
• The presented data-model fusion framework offers a robust approach to address complex challenges in infectious disease research.
• This approach is crucial for advancing our ability to predict and manage infectious diseases in a changing world.