Several factors contribute to performance overhead in epidemiology:
1. Data Collection: The process of gathering data can be time-consuming, especially when data sources are fragmented or not standardized. Manual data entry, paper-based records, and multiple data formats can increase overhead.
2. Data Processing: Cleaning, validating, and integrating data from various sources often require substantial effort. This is particularly true when dealing with incomplete or inconsistent data.
3. Data Analysis: Advanced analytical techniques, while powerful, can be resource-intensive. Complex models and simulations may require significant computational power and expertise, contributing to overhead.
4. Communication and Coordination: Ensuring effective communication and coordination among different stakeholders (e.g., public health agencies, laboratories, healthcare providers) is vital but can add to the overhead.
