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Outbreak Containment Strategies Modeling: Data Needs and Lessons Learned

Explore the caveats and assumptions in outbreak modeling, assess data needs, and derive crucial lessons for effective containment. Includes considerations on social consequences, data reliability, and community surveillance.

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Outbreak Containment Strategies Modeling: Data Needs and Lessons Learned

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  1. Working Group 7: Strategies to Contain Outbreaks and Prevent Spread Outbreak and Containment Strategies

  2. Modeling • Mostly focused on caveats of modeling • Calibration • Assumptions that go into models—how do you test the assumptions and decide if they are right or things you have confidence in. • Relevance of past vs real time data • Useful to help us understand what gaps are and what data are needed to fill them, and which options or intervention points are most useful to consider. • Modeling agricultural epidemic • Help us to understand tradeoffs—eg intervening upstream vs downstream • Need to be really careful not to impel policy makers to overuse numbers—precision can outpace accuracy. However, at least mathematical modeling makes the assumptions explicit.

  3. Modeling • Input tradeoffs: socially practical/doable • Planning continually shifts throughout pandemic-so modeling has a role throughout • Lots of different models-need to be clear that one can't do it all—transmission vs supply chain efforts vs social consequences • Modeling, or at least considering, social consequences is important • A model can only deal with things you can conceive of in advance • Including stability of international relations

  4. Data Needs • More studies like Tecumseh • Comprehensive community surveillance in interpandemic period • International surveillance and well placed field trials now • Effectiveness of public health measures • Mask use—N-95 vs surgical, community vs HC setting effectiveness • Travel restrictions • Social distance practices • Disinfection effectiveness • Combination strategies • Understand more about human/population behavior • Protocols now for use in pandemic eg.route of transmission, attack rates etc, reproductive rate and generation time • Relationship of disease vs shedding vs transmissibility • Role of immunologic preparedness

  5. Lessons • Calibrating models to SARS—would current models predict that you could contain SARS with strategies that were used • Lessons learned from social isolation in SARS • Other challenges lessons learned from anthrax and smallpox and others • Trust, stigmatized populations • When different sectors have different response • Eg civilian and DoD

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