Brainstorming points

[thinhong]

This project aims to build a cutting-edge surveillance system to improve infectious disease monitoring and response. By ensuring all key data is collected and creating a seamless workflow from data to analysis and reporting, the system will address critical public health questions at any outbreak stage.

The project will build on the framework by Cori et al. (https://doi.org/10.1098/rstb.2016.0371) and incorporate practical implementation strategies based on field experience with HCDC to tackle logistical and operational challenges. A key focus will be on integrating data from diverse surveillance systems and comparing the proposed system with current practices to identify gaps and areas for improvement.

We need to brainstorm:

1. Key public health questions and methods

• Define questions at various outbreak stages: detection, escalation, intervention, and post-outbreak review.
• Identify epidemiological and modelling methods to address these questions.

2. Checklist of essential data elements

• Detail required data types (e.g., case-level, exposure, intervention, population data).
• Include metadata (e.g., timestamps, geographic coordinates).

3. Integrating data from multiple surveillance types

• Analyze existing systems (e.g., case notifications, hospital reports, routine syndromic surveillance, sentinel surveillance, serum bank, event-based surveillance...) and explore emerging data sources like genomic surveillance, wastewater analysis, and digital epidemiology tools.
• Ensure compatibility between real-time surveillance (what real-time actually mean? this is a question raised in many other previous meetings) and periodic reporting systems.
• Address challenges like varying data formats, reporting frequencies, and biases.

4. Stakeholders

• Define roles and responsibilities for data collection, reporting, validation, and analysis.
• Identify stakeholders across public health agencies, laboratories, hospitals, and communities.
• Plan workflows for cross-sector collaboration and data sharing.

5. Tools and workflows for data integration

• Identify tools for real-time data entry, processing, and sharing.
• Design workflows for automated data cleaning, quality control, and visualization.
• Explore the use of dashboards or interactive platforms for stakeholders to access and analyze data.

6. Flexibility and scalability

• Develop a modular, adaptable system to accommodate new data sources, outbreak scenarios, and technological advancements.
• Ensure scalability to manage varying outbreak sizes, from localized clusters to pandemics.

7. Incorporating field insights

• Integrate lessons from HCDC's experiences in managing data collection and analysis during outbreaks (can use COVID-19 and the recent measles outbreak as an example).
• Address challenges such as incomplete data, delays in reporting, and resource constraints.

8. Evaluation challenges of collecting new data

• Use the current data and fill in our newly developed platform to estimate the challenges and identify gaps in data coverage or accuracy.

9. Ethical and legal considerations

• Address privacy concerns, data security issues, and compliance with relevant legal frameworks.
• Establish clear policies for data use, sharing, and retention.
• Identify which data should remain confidential and which can be shared publicly or among stakeholders.

[thinhong]

https://doi.org/10.1016/j.vaccine.2019.11.020

[thinhong]

https://www.tandfonline.com/doi/abs/10.1080/19488300.2013.820241
https://link.springer.com/article/10.1186/s12889-016-3893-0
https://academic.oup.com/jid/article/214/suppl_4/S375/2527914
https://doi.org/10.1016/j.ijid.2016.04.021