Despite its importance, disease forecasting faces several challenges:
Data Quality: Inaccurate or incomplete data can lead to unreliable predictions. Model Uncertainty: Different models can produce varying results, making it difficult to choose the best one. Dynamic Nature of Diseases: Pathogens can mutate, and human behavior can change, complicating predictions. Resource Limitations: Limited computational resources and expertise can hinder effective forecasting. Ethical Considerations: Ensuring data privacy and avoiding public panic are essential.
